Patent Publication Number: US-2023164179-A1

Title: Systems and methods for artificial model building techniques

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This patent application is a continuation of, and claims priority to and the benefit of U.S. Pat. Application No. 17/148,189, titled “SYSTEMS AND METHODS FOR ARTIFICIAL MODEL BUILDING TECHNIQUES,” and filed Jan. 13, 2021, which is a continuation of, and claims priority to and the benefit of U.S. Pat. Application No. 16/383127, titled “SYSTEMS AND METHODS FOR ARTIFICIAL MODEL BUILDING TECHNIQUES,” and filed April 12,2019, which is a continuation of, and claims priority to and the benefit of U.S. Pat. Application No. 15/994,292, titled “SYSTEMS AND METHODS FOR ARTIFICIAL MODEL BUILDING TECHNIQUES,” and filed May 31, 2018, which claims priority to and the benefit of U.S. Pat. Application No. 15/829,719, titled “SYSTEMS AND METHODS FOR ARTIFICIAL MODEL BUILDING TECHNIQUES,” and filed Dec. 1, 2017,the contents all of which are hereby incorporated herein by reference in its entirety for all purposes. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure generally relates to artificial intelligence driven security awareness systems for performing simulated phishing attacks. 
     BACKGROUND OF THE DISCLOSURE 
     It can be useful to perform simulated phishing attacks on an individual or set of individuals for the purposes of extracting information from a device used by the individuals. A phishing attack involves an attempt to acquire sensitive information such as usernames, passwords, credit card details, etc., often for malicious reasons, possible by masquerading as a trustworthy entity. For example, an email may be sent to a target, the email having an attachment that performs malicious actions when executed or a link to a webpage that either performs malicious actions when accessed or prompts the user to execute a malicious program. Malicious actions may include malicious data collection or actions harmful to the normal functioning of a device on which the email was activated, or any other malicious actions capable of being performed by a program or a set of programs. 
     BRIEF SUMMARY OF THE DISCLOSURE 
     A simulated phishing attack may test the readiness of a security system or users of a system to handle phishing attacks such that malicious actions are prevented. A simulated phishing attack may, for example, target a large number of users, such as employees of an organization. Such an attack may be performed by a party friendly or neutral to the targets of the simulated attack. In one type of simulated phishing attack, an attempt is made to lure a user (e.g., an employee of a business entity) into performing a target action. Performing a simulated phishing attack can help expose individuals that are more susceptible to phishing attacks, in addition to exposing weaknesses in the security infrastructure meant to protect users and/or devices from phishing attacks or other computerized, cyber, or digital attacks. Different users respond differently to different stimuli, and therefore the type of phishing attack that one user falls prey to may not be remotely tempting to a different user. The same user may also respond differently to a phishing attack depending on where the user is, who the user is with, what the user is doing, etc. These differences in user behaviors mean that the same simulated phishing attack does not have the same effectiveness in terms of teaching a user how to recognize threats, because not all users would have likely responded to a similar real phishing email in the first place. 
     Phishing attacks are rapidly getting more and more sophisticated, and the instigators of the phishing attacks have been able to mass scale spear phishing, which is individualized, real time, and reactive. In order for a security awareness system to be able to train users to detect such highly sophisticated and personalized attacks, the security awareness system needs to create a simulated phishing environment that is as sophisticated and individualized and synonymous with the kinds of attacks a user is likely to encounter in the real world. 
     A security awareness system can be configured to send multiple simulated phishing emails, text or short message service (SMS) messages, voice calls (e.g. via Voice Over Internet Protocol or VoIP), or Internet based communications (collectively referred to as simulated phishing messages or messages), varying the quantity, frequency, type, sophistication, timing, and combinations using machine learning algorithms or other forms of artificial intelligence. 
     In some implementations, the security awareness system may adaptively learn the best design of a simulated phishing campaign to get a user to perform the requested actions, such as clicking a hyperlink or opening a file. In some implementations, the system may adapt an ongoing campaign based on user’s responses to messages in the campaign, along with the system’s learned awareness. The learning process implemented by the security awareness system can be trained by observing the behavior of other users in the same company, other users in the same industry, other users that share similar attributes, all other users of the system, or users that have user attributes that match criteria set by the system, or that match attributes of a subset of other users in the system. 
     The system can record when and how the user action was performed and can produce reports about the actions. The reports can track the number of users the simulated phishing messages were sent to, whether the messages were successfully delivered, whether a user performed an action, whether a user performed a requested action, when an action or requested action was performed, and a combination and timing of messages that induced a user to perform a requested action. In some implementations, the system may provide training on why a user should not have performed a requested action at the time that the user performs the requested action. In some implementations, the system may enroll the user in training to be performed in the future. In some implementations, the system may add the user to a group of users. 
     The security awareness system may establish a plurality of artificial intelligence models that are used for creating and communicating simulated phishing messages to users in a simulated phishing campaign. In some embodiment, the models can be trained to represent a predetermined persona, and the model can be used during the simulated phishing campaign to determine an appropriate type of simulated phishing message, content of a simulated phishing message, timing of or between simulated phishing messages, and other parameters of simulated phishing messages and simulated phishing campaigns. 
     In one embodiment, a security awareness system establishes a model for communicating via simulated phishing campaigns by establishing a plurality of question and answer pairs to train a neural network to establish a model for a predetermined persona. In some embodiments, the question and answer pairs are established via one or more workers. The security awareness system stores the established model to be used by a campaign controller for communicating simulated phishing communications to one or more users. 
     In some embodiments, the one or more workers identify one or more of the questions or answers of the plurality of question and answer pairs from communications between a campaign controller and one or more users during execution of a simulated phishing campaign. In one embodiment, the workers create one or more of the questions or answers. In some embodiments, the question and answer pairs are established in accordance with the predetermined persona. In some embodiments, the predetermined persona may be an assistant, a travel agent, a tech support representative, a credit card company representative, or a financial institution representative. In some embodiments, the persona corresponds to an industry, a demographic or an organizational level in a company. 
     In one embodiment, the one or more workers validate that answers to the questions of the plurality of the question and answer pairs were created in accordance with a predetermined persona. In some embodiments, the workers validate that one or more of the questions or the answers of the question and answer pairs meet one or more predetermined criteria, for example a level of quality of a plurality of levels of quality, proper use of grammar, and spelling errors. In some embodiments, the workers validate the output of the model responsive to one or more inputs to the model. In some embodiments, the model is adjusted via a tuning process. 
     In one embodiment, the security awareness system generates, responsive to the training of the neural network, a metagraph and one or more inputs to pass to a list of operations to execute of the metagraph. In some embodiments, the model comprises the metagraph. In some embodiments, settings of neurons of the neural network are adjusted responsive to processing the plurality of question and answer pairs. In one embodiment, the established model comprises a matrix of values corresponding to the adjusted settings of neurons of the neural network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other objects, aspects, features, and advantages of the disclosure will become more apparent and better understood by referring to the following description taken in conjunction with the accompanying drawings, in which: 
         FIG.  1 A  is a block diagram depicting an embodiment of a network environment comprising client device in communication with server device; 
         FIG.  1 B  is a block diagram depicting a could computing environment comprising client device in communication with cloud service providers; 
         FIGS.  1 C and  1 D  are block diagrams depicting embodiments of computing devices useful in connection with the methods and systems described herein; 
         FIG.  2 A  depicts an implementation of some of the architecture of an implementation of a system capable of performing artificial intelligence driven simulated phishing attack campaigns as part of a security awareness system; 
         FIG.  2 B  depicts an implementation of an artificial intelligence driven agent (AIDA) system; 
         FIG.  2 C  depicts an example of a user interface and/or dashboard for displaying metrics and statistics about simulated phishing campaigns, showing recipient information; 
         FIG.  2 D  depicts an example of a user interface and/or dashboard for displaying metrics and statistics about simulated phishing campaigns, showing bounced emails; 
         FIG.  2 E  depicts an example of a user interface and/or dashboard for displaying metrics and statistics about simulated phishing campaigns, showing SMS messages sent; 
         FIG.  3    depicts an implementation of some of the architecture of an implementation of a system capable of creating artificial intelligence models for use as part of a security awareness system; 
         FIG.  4    depicts an implementation of a method for establishing a model for communicating via simulated phishing campaigns; 
         FIG.  5    depicts an example output of a system monitoring module monitoring the creation of one or more models; 
         FIG.  6    depicts an example input screen for a company administrator console to create an AIDA campaign; and 
         FIG.  7    depicts a company administrator console dashboard showing an overview summary of an AIDA campaign. 
     
    
    
     DETAILED DESCRIPTION 
     For purposes of reading the description of the various embodiments below, the following descriptions of the sections of the specifications and their respective contents may be helpful: 
     Section A describes a network environment and computing environment which may be useful for practicing embodiments described herein. 
     Section B describes an artificial intelligence network and environment which may be useful for practicing embodiments described herein. 
     Section C describes embodiments of systems and methods for creating, controlling and executing simulated phishing campaigns using artificial intelligence as part of a security awareness system. 
     Section D describes embodiments of systems and methods for generating, revising, and tuning artificial intelligence models for use as part of a security awareness system. 
     A. Computing and Network Environment 
     Prior to discussing specific embodiments of the present solution, it may be helpful to describe aspects of the operating environment as well as associated system components (e.g. hardware elements) in connection with the methods and systems described herein. Referring to  FIG.  1 A , an embodiment of a network environment is depicted. In brief overview, the network environment includes one or more clients  102   a  -  102   n  (also generally referred to as local machines(s)  102 , client(s)  102 , client node(s)  102 , client machine(s)  102 , client computer(s)  102 , client device(s)  102 , endpoint(s)  102 , or endpoint node(s)  102 ) in communication with one or more servers  106   a  -  106   n  (also generally referred to as server(s)  106 , node(s)  106 , machine(s)  106 , or remote machine(s)  106 ) via one or more networks  104 . In some embodiments, a client  102  has the capacity to function as both a client node seeking access to resources provided by a server and as a server providing access to hosted resources for other clients  102   a  - 102   n . 
     Although  FIG.  1 A  shows a network  104  between the clients  102  and the servers  106 , the clients  102  and the servers  106  may be on the same network  104 . In some embodiments, there are multiple networks  104  between the clients  102  and the servers  106 . In one of these embodiments, a network  104 ′ (not shown) may be a private network and a network  104  may be a public network. In another of these embodiments, a network  104  may be a private network and a network  104 ′ may be a public network. In still another of these embodiments, networks  104  and  104 ′ may both be private networks. 
     The network  104  may be connected via wired or wireless links. Wired links may include Digital Subscriber Line (DSL), coaxial cable lines, or optical fiber lines. Wireless links may include Bluetooth®, Bluetooth Low Energy (BLE), ANT/ANT+, ZigBee, Z-Wave, Thread, Wi-Fi®, Worldwide Interoperability for Microwave Access (WiMAX®), mobile WiMAX®, WiMAX®-Advanced, NFC, SigFox, LoRa, Random Phase Multiple Access (RPMA), Weightless-N/P/W, an infrared channel or a satellite band. The wireless links may also include any cellular network standards to communicate among mobile devices, including standards that qualify as 1G, 2G, 3G, 4G, or 5G. The network standards may qualify as one or more generations of mobile telecommunication standards by fulfilling a specification or standards such as the specifications maintained by the International Telecommunication Union. The 3G standards, for example, may correspond to the International Mobile Telecommuniations-2000 (IMT-2000) specification, and the 4G standards may correspond to the International Mobile Telecommunication Advanced (IMT-Advanced) specification. Examples of cellular network standards include AMPS, GSM, GPRS, UMTS, CDMA2000, CDMA-1xRTT, CDMA-EVDO, LTE, LTE-Advanced, LTE-M1, and Narrowband IoT (NB-IoT). Wireless standards may use various channel access methods, e.g. FDMA, TDMA, CDMA, or SDMA. In some embodiments, different types of data may be transmitted via different links and standards. In other embodiments, the same types of data may be transmitted via different links and standards. 
     The network  104  may be any type and/or form of network. The geographical scope of the network may vary widely and the network  104  can be a body area network (BAN), a personal area network (PAN), a local-area network (LAN), e.g. Intranet, a metropolitan area network (MAN), a wide area network (WAN), or the Internet. The topology of the network  104  may be of any form and may include, e.g., any of the following: point-to-point, bus, star, ring, mesh, or tree. The network  104  may be an overlay network which is virtual and sits on top of one or more layers of other networks  104 ′. The network  104  may be of any such network topology as known to those ordinarily skilled in the art capable of supporting the operations described herein. The network  104  may utilize different techniques and layers or stacks of protocols, including, e.g., the Ethernet protocol, the internet protocol suite (TCP/IP), the ATM (Asynchronous Transfer Mode) technique, the SONET (Synchronous Optical Networking) protocol, or the SDH (Synchronous Digital Hierarchy) protocol. The TCP/IP internet protocol suite may include application layer, transport layer, internet layer (including, e.g., IPv4 and IPv6), or the link layer. The network  104  may be a type of broadcast network, a telecommunications network, a data communication network, or a computer network. 
     In some embodiments, the system may include multiple, logically-grouped servers  106 . In one of these embodiments, the logical group of servers may be referred to as a server farm or a machine farm. In another of these embodiments, the servers  106  may be geographically dispersed. In other embodiments, a machine farm may be administered as a single entity. In still other embodiments, the machine farm includes a plurality of machine farms. The servers  106  within each machine farm can be heterogeneous - one or more of the servers  106  or machines  106  can operate according to one type of operating system platform (e.g., Windows, manufactured by Microsoft Corp. of Redmond, Washington), while one or more of the other servers  106  can operate according to another type of operating system platform (e.g., Unix, Linux, or Mac OSX). 
     In one embodiment, servers  106  in the machine farm may be stored in high-density rack systems, along with associated storage systems, and located in an enterprise data center. In this embodiment, consolidating the servers  106  in this way may improve system manageability, data security, the physical security of the system, and system performance by locating servers  106  and high-performance storage systems on localized high-performance networks. Centralizing the servers  106  and storage systems and coupling them with advanced system management tools allows more efficient use of server resources. 
     The servers  106  of each machine farm do not need to be physically proximate to another server  106  in the same machine farm. Thus, the group of servers  106  logically grouped as a machine farm may be interconnected using a wide-area network (WAN) connection or a metropolitan-area network (MAN) connection. For example, a machine farm 38 may include servers  106  physically located in different continents or different regions of a continent, country, state, city, campus, or room. Data transmission speeds between servers  106  in the machine farm can be increased if the servers  106  are connected using a local-area network (LAN) connection or some form of direct connection. Additionally, a heterogeneous machine farm may include one or more servers  106  operating according to a type of operating system, while one or more other servers execute one or more types of hypervisors rather than operating systems. In these embodiments, hypervisors may be used to emulate virtual hardware, partition physical hardware, virtualize physical hardware, and execute virtual machines that provide access to computing environments, allowing multiple operating systems to run concurrently on a host computer. Native hypervisors may run directly on the host computer. Hypervisors may include VMware ESX/ESXi, manufactured by VMWare, Inc., of Palo Alta, California; the Xen hypervisor, an open source product whose development is overseen by Citrix Systems, Inc. of Fort Lauderdale, Florida; the HYPER-V hypervisors provided by Microsoft, or others. Hosted hypervisors may run within an operating system on a second software level. Examples of hosted hypervisors may include VMWare Workstation and VirtualBox, manufactured by Oracle Corporation of Redwood City, California. 
     Management of the machine farm may be de-centralized. For example, one or more servers  106  may comprise components, subsystems and modules to support one or more management services for the machine farm. In one of these embodiments, one or more servers  106  provide functionality for management of dynamic data, including techniques for handling failover, data replication, and increasing the robustness of the machine farm. Each server  106  may communicate with a persistent store and, in some embodiments, with a dynamic store. 
     Server  106  may be a file server, application server, web server, proxy server, appliance, network appliance, gateway, gateway server, virtualization server, deployment server, SSL VPN server, or firewall. In one embodiment, a plurality of servers  106  may be in the path between any two communicating servers  106 . 
     Referring to  FIG.  1 B , a cloud computing environment is depicted. A could computing environment may provide client  102  with one or more resources provided by a network environment. The could computing environment may include one or more clients  102   a  -  102   n , in communication with the cloud  108  over one or more networks  104 . Clients  102  may include, e.g., thick clients, thin clients, and zero clients. A thick client may provide at least some functionality even when disconnected from the cloud  108  or servers  106 . A thin client or zero client may depend on the connection to the cloud  108  or server  106  to provide functionality. A zero client may depend on the cloud  108  or other networks  104  or servers  106  to retrieve operating system data for the client device  102 . The cloud  108  may include back end platforms, e.g., servers  106 , storage, server farms or data centers. 
     The cloud  108  may be public, private, or hybrid. Public clouds may include public servers  106  that are maintained by third parties to the clients  102  or the owners of the clients. The servers  106  may be located off-site in remote geographical locations as disclosed above or otherwise. Public clouds may be connected to the servers  106  over a public network. Private clouds may include private servers  106  that are physically maintained by clients  102  or owners of clients. Private clouds may be connected to the servers  106  over a private network  104 . Hybrid clouds  109  may include both the private and public networks  104  and servers  106 . 
     The cloud  108  may also include a cloud based delivery, e.g. Software as a Service (SaaS)  110 , Platform as a Service (PaaS)  112 , and Infrastructure as a Service (IaaS)  114 . IaaS may refer to a user renting the user of infrastructure resources that are needed during a specified time period. IaaS provides may offer storage, networking, servers or virtualization resources from large pools, allowing the users to quickly scale up by accessing more resources as needed. Examples of IaaS include Amazon Web Services (AWS) provided by Amazon, Inc. of Seattle, Washington, Rackspace Cloud provided by Rackspace Inc. of San Antonio, Texas, Google Compute Engine provided by Google Inc. of Mountain View, California, or RightScale provided by RightScale, Inc. of Santa Barbara, California. PaaS providers may offer functionality provided by IaaS, including, e.g., storage, networking, servers or virtualization, as well as additional resources, e.g., the operating system, middleware, or runtime resources. Examples of PaaS include Windows Azure provided by Microsoft Corporation of Redmond, Washington, Google App Engine provided by Google Inc., and Heroku provided by Heroku, Inc. of San Francisco California. SaaS providers may offer the resources that PaaS provides, including storage, networking, servers, virtualization, operating system, middleware, or runtime resources. In some embodiments, SaaS providers may offer additional resources including, e.g., data and application resources. Examples of SaaS include Google Apps provided by Google Inc., Salesforce provided by Salesforce.com Inc. of San Francisco, California, or Office365 provided by Microsoft Corporation. Examples of SaaS may also include storage providers, e.g. Dropbox provided by Dropbox Inc. of San Francisco, California, Microsoft OneDrive provided by Microsoft Corporation, Google Drive provided by Google Inc., or Apple iCloud provided by Apple Inc. of Cupertino, California. 
     Clients  102  may access IaaS resources with one or more IaaS standards, including, e.g., Amazon Elastic Compute Cloud (EC2), Open Cloud Computing Interface (OCCI), Cloud Infrastructure Management Interface (CIMI), or OpenStack standards. Some IaaS standards may allow clients access to resources over HTTP, and may use Representational State Transfer (REST) protocol or Simple Object Access Protocol (SOAP). Clients  102  may access PaaS resources with different PaaS interfaces. Some PaaS interfaces use HTTP packages, standard Java APIs, JavaMail API, Java Data Objects (JDO), Java Persistence API (JPA), Python APIs, web integration APIs for different programming languages including, e.g., Rack for Ruby, WSGI for Python, or PSGI for Perl, or other APIs that may be built on REST, HTTP, XML, or other protocols. Clients  102  may access SaaS resources through the use of web-based user interfaces, provided by a web browser (e.g. Google Chrome, Microsoft Internet Explorer, or Mozilla Firefox provided by Mozilla Foundation of Mountain View, California). Clients  102  may also access SaaS resources through smartphone or tablet applications, including e.g., Salesforce Sales Cloud, or Google Drive App. Clients  102  may also access SaaS resources through the client operating system, including e.g. Windows file system for Dropbox. 
     In some embodiments, access to IaaS, PaaS, or SaaS resources may be authenticated. For example, a server or authentication server may authenticate a user via security certificates, HTTPS, or API keys. API keys may include various encryption standards such as, e.g., Advanced Encryption Standard (AES). Data resources may be sent over Transport Layer Security (TLS) or Secure Sockets Layer (SSL). 
     The client  102  and server  106  may be deployed as and/or executed on any type and form of computing device, e.g., a computer, network device or appliance capable of communicating on any type and form of network and performing the operations described herein. 
       FIGS.  1 C and  1 D  depict block diagrams of a computing device  100  useful for practicing an embodiment of the client  102  or a server  106 . As shown in  FIGS.  1 C and  1 D , each computing device  100  includes a central processing unit  121 , and a main memory unit  122 . As shown in  FIG.  1 C , a computing device  100  may include a storage device  128 , an installation device  116 , a network interface  118 , and I/O controller  123 , display devices  124   a  -  124   n , a keyboard  126  and a pointing device  127 , e.g., a mouse. The storage device  128  may include, without limitation, an operating system, software, and a software of a simulated phishing attack system  120 . As shown in  FIG.  1 D , each computing device  100  may also include additional optional elements, e.g., a memory port  103 , a bridge  170 , one or more input/output devices  130   a  -  130   n  (generally referred to using reference numeral  130 ), and a cache memory  140  in communication with the central processing unit  121 . 
     The central processing unit  121  is any logic circuity that responds to and processes instructions fetched from the main memory unit  122 . In many embodiments, the central processing unit  121  is provided by a microprocessor unit, e.g.: those manufactured by Intel Corporation of Mountain View, California; those manufactured by Motorola Corporation of Schaumburg, Illinois; the ARM processor and TEGRA system on a chip (SoC) manufactured by Nvidia of Santa Clara, California; the POWER7 processor, those manufactured by International Business Machines of White Plains, New York; or those manufactured by Advanced Micro Devices of Sunnyvale, California. The computing device  100  may be based on any of these processors, or any other processor capable of operating as described herein. The central processing unit  121  may utilize instruction level parallelism, thread level parallelism, different levels of cache, and multi-core processors. A multi-core processor may include two or more processing units on a single computing component. Examples of multi-core processors include the AMD PHENOM IIX2, INTER CORE i5 and INTEL CORE i7. 
     Main memory unit  122  may include on or more memory chips capable of storing data and allowing any storage location to be directly accessed by the microprocessor  121 . Main memory unit  122  may be volatile and faster than storage  128  memory. Main memory units  122  may be Dynamic Random-Access Memory (DRAM) or any variants, including static Random-Access Memory (SRAM), Burst SRAM or SynchBurst SRAM (BSRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM (EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM), Single Data Rate Synchronous DRAM (SDR SDRAM), Double Data Rate SDRAM (DDR SDRAM), Direct Rambus DRAM (DRDRAM), or Extreme Data Rate DRAM (XDR DRAM). In some embodiments, the main memory  122  or the storage  128  may be non-volatile; e.g., non-volatile read access memory (NVRAM), flash memory non-volatile static RAM (nvSRAM), Ferroelectric RAM (FeRAM), Magnetoresistive RAM (MRAM), Phase-change memory (PRAM), conductive-bridging RAM (CBRAM), Silicon-Oxide-Nitride-Oxide-Silicon (SONOS), Resistive RAM (RRAM), Racetrack, Nano-RAM (NRAM), or Millipede memory. The main memory  122  may be based on any of the above described memory chips, or any other available memory chips capable of operating as described herein. In the embodiment shown in  FIG.  1 C , the processor  121  communicates with main memory  122  via a system bus  150  (described in more detail below).  FIG.  1 D  depicts an embodiment of a computing device  100  in which the processor communicates directly with main memory  122  via a memory port  103 . For example, in  FIG.  1 D  the main memory  122  may be DRDRAM. 
       FIG.  1 D  depicts and embodiment in which the main processor  121  communicates directly with cache memory  140  via a secondary bus, sometimes referred to as a backside bus. In other embodiments, the main processor  121  communicates with cache memory  140  using the system bus  150 . Cache memory  140  typically has a faster response time than main memory  122  and is typically provided by SRAM, BSRAM, or EDRAM. In the embodiment shown in  FIG.  1 D , the processor  121  communicates with various I/O devices  130  via a local system bus  150 . Various buses may be used to connect the central processing unit  121  to any of the I/O devices  130 , including a PCI bus, a PCI-X bus, or a PCI-Express bus, or a NuBus. For embodiments in which the I/O device is a video display  124 , the processor  121  may use an Advanced Graphic Port (AGP) to communicate with the display  124  or the I/O controller  123  for the display  124 .  FIG.  1 D  depicts and embodiment of a computer  100  in which the main processor  121  communicates directly with I/O device  130   b  or other processors  121 ′ via HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology.  FIG.  1 D  also depicts an embodiment in which local busses and direct communication are mixed: the processor  121  communicates with I/O device  130   a  using a local interconnect bus while communicating with I/O device  130   b  directly. 
     A wide variety of I/O devices  130   a  -  130   n  may be present in the computing device  100 . Input devices may include keyboards, mice, trackpads, trackballs, touchpads, touch mice, multi-touch touchpads and touch mice, microphones, multi-array microphones, drawing tablets, cameras, single-lens reflex cameras (SLR), digital SLR (DSLR), CMOS sensors, accelerometers, infrared optical sensors, pressure sensors, magnetometer sensors, angular rate sensors, depth sensors, proximity sensors, ambient light sensors, gyroscopic sensors, or other sensors. Output devices may include video displays, graphical displays, speakers, headphones, inkjet printers, laser printers, and 3D printers. 
     Devices  130   a  - 130   n  may include a combination of multiple input or output devices, including, e.g., Microsoft KINECT, Nintendo Wiimote for the WII, Nintendo WII U GAMEPAD, or Apple iPhone. Some devices  130   a  -  130   n  allow gesture recognition inputs through combining some of the inputs and outputs. Some devices  130   a  -  130   n  provide for facial recognition which may be utilized as an input for different purposes including authentication and other commands. Some devices  130   a  -  130   n  provide for voice recognition and inputs, including, e.g., Microsoft KINECT, SIRI for iPhone by Apple, Google Now or Google Voice Search, and Alexa by Amazon. 
     Additional devices  130   a  -  130   n  have both input and output capabilities, including, e.g., haptic feedback devices, touchscreen displays, or multi-touch displays. Touchscreen, multi-touch displays, touchpads, touch mice, or other touch sensing devices may use different technologies to sense touch, including, e.g., capacitive, surface capacitive, projected capacitive touch (PCT), in-cell capacitive, resistive, infrared, waveguide, dispersive signal touch (DST), in-cell optical, surface acoustic wave (SAW), bending wave touch (BWT), or force-based sensing technologies. Some multi-touch devices may allow two or more contact points with the surface, allowing advanced functionality including, e.g., pinch, spread, rotate, scroll, or other gestures. Some touchscreen devices, including, e.g., Microsoft PIXELSENSE or Multi-Touch Collaboration Wall, may have larger surfaces, such as on a table-top or on a wall, and may also interact with other electronic devices. Some I/O devices  130   a  -  130   n , display devices  124   a  -  124   n  or group of devices may be augmented reality devices. The I/O devices may be controlled by an I/O controller  123  as shown in  FIG.  1 C . The I/O controller may control one or more I/O devices, such as, e.g., a keyboard  126  and a pointing device  127 , e.g., a mouse or optical pen. Furthermore, an I/O device may also provide storage and/or an installation medium  116  for the computing device  100 . In still other embodiments, the computing device  100  may provide USB connections (not shown) to receive handheld USB storage devices. In further embodiments, a I/O device  130  may be a bridge between the system bus  150  and an external communication bus, e.g. a USB bus, a SCSI bus, a FireWire bus, an Ethernet bus, a Gigabit Ethernet bus, a Fibre Channel bus, or a Thunderbolt bus. 
     In some embodiments, display devices  124   a  -  124   n  may be connected to I/O controller  123 . Display devices may include, e.g., liquid crystal displays (LCD), thin film transistor LCD (TFT-LCD), blue phase LCD, electronic papers (e-ink) displays, flexile displays, light emitting diode displays (LED), digital light processing (DLP) displays, liquid crystal on silicon (LCOS) displays, organic light-emitting diode (OLED) displays, active-matrix organic light-emitting diode (AMOLED) displays, liquid crystal laser displays, time-multiplexed optical shutter (TMOS) displays, or 3D displays. Examples of 3D displays may use, e.g. stereoscopy, polarization filters, active shutters, or auto stereoscopy. Display devices  124   a  -  124   n  may also be a head-mounted display (HMD). In some embodiments, display devices  124   a  -  124   n  or the corresponding I/O controllers  123  may be controlled through or have hardware support for OPENGL or DIRECTX API or other graphics libraries. 
     In some embodiments, the computing device  100  may include or connect to multiple display devices  124   a  -  124   n , which each may be of the same or different type and/or form. As such, any of the I/O devices  130   a - 130   n  and/or the I/O controller  123  may include any type and/or form of suitable hardware, software, or combination of hardware and software to support, enable or provide for the connection and use of multiple display devices  124   a  -  124   n  by the computing device  100 . For example, the computing device  100  may include any type and/or form of video adapter, video card, driver, and/or library to interface, communicate, connect or otherwise use the display devices  124   a  -  124   n . In one embodiment, a video adapter may include multiple connectors to interface to multiple display devices  124   a  -  124   n . In other embodiments, the computing device  100  may include multiple video adapters, with each video adapter connected to one or more of the display devices  124   a  -  124   n . In some embodiments, any portion of the operating system of the computing device  100  may be configured for using multiple displays  124   a  -  124   n . In other embodiments, one or more of the display devices  124   a  -  124   n  may be provided by one or more other computing devices  100   a  or  100   b  connected to the computing device  100 , via the network  104 . In some embodiments software may be designed and constructed to use another computer’s display device as a second display device  124   a  for the computing device  100 . For example, in one embodiment, an Apple iPad may connect to a computing device  100  and use the display of the device  100  as an additional display screen that may be used as an extended desktop. One ordinarily skilled in the art will recognize and appreciate the various ways and embodiments that a computing device  100  may be configured to have multiple display devices  124   a  -  124   n . 
     Referring again to  FIG.  1 C , the computing device  100  may comprise a storage device  128  (e.g. one or more hard disk drives or redundant arrays of independent disks) for storing an operating system or other related software, and for storing application software programs such as any program related to the software  120 . Examples of storage device  128  include, e.g., hard disk drive (HDD); optical drive including CD drive, DVD drive, or BLU-RAY drive; solid-state drive (SSD); USB flash drive; or any other device suitable for storing data. Some storage devices may include multiple volatile and non-volatile memories, including, e.g., solid state hybrid drives that combine hard disks with solid state cache. Some storage device  128  may be non-volatile, mutable, or read-only. Some storage device  128  may be internal and connect to the computing device  100  via a bus  150 . Some storage device  128  may be external and connect to the computing device  100  via a 1/0 device  130  that provides an external bus. Some storage device  128  may connect to the computing device  100  via the network interface  118  over a network  104 , including, e.g., the Remote Disk for MACBOOK AIR by Apple. Some client devices  100  may not require a non-volatile storage device  128  and may be thin clients or zero clients  102 . Some storage device  128  may also be used as an installation device  116 , and may be suitable for installing software and programs. Additionally, the operating system and the software can be run from a bootable medium, for example, a bootable CD, e.g. KNOPPIX, a bootable CD for GNU/Linux that is available as a GNU/Linux distribution from knoppix.net. 
     Client device  100  may also install software or application from an application distribution platform. Examples of application distribution platforms include the App Store for iOS provided by Apple, Inc., the Mac App Store provided by Apple, Inc., GOOGLE PLAY for Android OS provided by Google Inc., Chrome Webstore for CHROME OS provided by Google Inc., and Amazon Appstore for Android OS and KINDLE FIRE provided by Amazon.com, Inc. An application distribution platform may facilitate installation of software on a client device  102 . An application distribution platform may include a repository of applications on a server  106  or a cloud  108 , which the clients  102   a - 102   n  may access over a network  104 . An application distribution platform may include application developed and provided by various developers. A user of a client device  102  may select, purchase and/or download an application via the application distributionplatform. 
     Furthermore, the computing device  100  may include a network interface  118  to interface to the network  104  through a variety of connections including, but not limited to, standard telephone lines LAN or WAN links (e.g., 802.11, T1, T3, Gigabit Ethernet, Infiniband), broadband connections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet, Ethernet-over- SONET, ADSL, VDSL, BPON, GPON, fiber optical including FiOS), wireless connections, or some combination of any or all of the above. Connections can be established using a variety of communication protocols (e.g., TCP/IP, Ethernet, ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), IEEE 802.1 la/b/g/n/ac CDMA, GSM, WiMax and direct asynchronous connections). In one embodiment, the computing device  100  communicates with other computing devices  100 ′ via any type and/or form of gateway or tunneling protocol e.g. Secure Socket Layer (SSL) or Transport Layer Security (TLS), or the Citrix Gateway Protocol manufactured by Citrix Systems, Inc. The network interface  118  may comprise a built-in network adapter, network interface card, PCMCIA network card, EXPRESSCARD network card, card bus network adapter, wireless network adapter, USB network adapter, modem or any other device suitable for interfacing the computing device  100  to any type of network capable of communication and performing the operations described herein. 
     A computing device  100  of the sort depicted in  FIGS.  1 B and  1 C  may operate under the control of an operating system, which controls scheduling of tasks and access to system resources. The computing device  100  can be running any operating system such as any of the versions of the MICROSOFT WINDOWS operating systems, the different releases of the Unix and Linux operating systems, any version of the MAC OS for Macintosh computers, any embedded operating system, any real-time operating system, any open source operating system, any proprietary operating system, any operating systems for mobile computing devices, or any other operating system capable of running on the computing device and performing the operations described herein. Typical operating systems include, but are not limited to: WINDOWS 2000, WINDOWS Server 2012, WINDOWS CE, WINDOWS Phone, WINDOWS XP, WINDOWS VISTA, and WINDOWS 7, WINDOWS RT, WINDOWS 8 and WINDOW 10, all of which are manufactured by Microsoft Corporation of Redmond, Washington; MAC OS and iOS, manufactured by Apple, Inc.; and Linux, a freely-available operating system, e.g. Linux Mint distribution (“distro”) or Ubuntu, distributed by Canonical Ltd. of London, United Kingdom; or Unix or other Unix-like derivative operating systems; and Android, designed by Google Inc., among others. Some operating systems, including, e.g., the CHROME OS by Google Inc., may be used on zero clients or thin clients, including, e.g., CHROMEBOOKS. 
     The computer system  100  can be any workstation, telephone, desktop computer, laptop or notebook computer, netbook, ULTRABOOK, tablet, server, handheld computer, mobile telephone, smartphone or other portable telecommunications device, media playing device, a gaming system, mobile computing device, or any other type and/or form of computing, telecommunications or media device that is capable of communication. The computer system  100  has sufficient processor power and memory capacity to perform the operations described herein. In some embodiments, the computing device  100  may have different processors, operating systems, and input devices consistent with the device. The Samsung GALAXY smartphones, e.g., operate under the control of Android operating system developed by Google, Inc. GALAXY smartphones receive input via a touch interface. 
     In some embodiments, the computing device  100  is a gaming system. For example, the computer system  100  may comprise a PLAYSTATION 3, or PERSONAL PLAYSTATION PORTABLE (PSP), or a PLAYSTATION VITA device manufactured by the Sony Corporation of Tokyo, Japan, or a NINTENDO DS, NINTENDO 3DS, NINTENDO WII, or a NINTENDO WII U device manufactured by Nintendo Co., Ltd., of Kyoto, Japan, or an XBOX 360 device manufactured by Microsoft Corporation. 
     In some embodiments, the computing device  100  is a digital audio player such as the Apple IPOD, IPOD Touch, and IPOD NANO lines of devices, manufactured by Apple Computer of Cupertino, California. Some digital audio players may have other functionality, including, e.g., a gaming system or any functionality made available by an application from a digital application distribution platform. For example, the IPOD Touch may access the Apple App Store. In some embodiments, the computing device  100  is a portable media player or digital audio player supporting file formats including, but not limited to, MP3, WAV, M4A/AAC, WMA Protected AAC, AIFF, Audible audiobook, Apple Lossless audio file formats and .mov, .m4v, and .mp4 MPEG-4 (H.264/MPEG-4 AVC) video file formats. 
     In some embodiments, the computing device  100  is a tablet e.g. the IPAD line of devices by Apple; GALAXY TAB family of devices by Samsung; or KINDLE FIRE, byAmazon.com, Inc. of Seattle, Washington. In other embodiments, the computing device  100  is an eBook reader, e.g. the KINDLE family of devices by Amazon.com, or NOOK family of devices by Barnes &amp; Noble, Inc. of New York City, New York. 
     In some embodiments, the communications device  102  includes a combination of devices, e.g. a smartphone combined with a digital audio player or portable media player. For example, one of these embodiments is a smartphone, e.g. the iPhone family of smartphones manufactured by Apple, Inc.; a Samsung GALAXY family of smartphones manufactured by Samsung, Inc; or a Motorola DROID family of smartphones. In yet another embodiment, the communications device  102  is a laptop or desktop computer equipped with a web browser and a microphone and speaker system, e.g. a telephony headset. In these embodiments, the communications devices  102  are web-enabled and can receive and initiate phone calls. In some embodiments, a laptop or desktop computer is also equipped with a webcam or other video capture device that enables video chat and video call. 
     In some embodiments, the status of one or more machines  102 ,  106  in the network  104  is monitored, generally as part of network management. In one of these embodiments, the status of a machine may include an identification of load information (e.g., the number of processes on the machine, CPU and memory utilization), of port information (e.g., the number of available communication ports and the port addresses), or of session status (e.g., the duration and type of processes, and whether a process is active or idle). In another of these embodiments, this information may be identified by a plurality of metrics, and the plurality of metrics can be applied at least in part towards decisions in load distribution, network traffic management, and network failure recovery as well as any aspects of operations of the present solution described herein. Aspects of the operating environments and components described above will become apparent in the context of the systems and methods disclosed herein. 
     B. Artificial Intelligence Network and Environment 
     An intelligent agent is any system or device that perceives its environment and takes actions to maximize its chances of success at some goal. The term artificial intelligence is used when a machine mimics cognitive functions such as learning and problem solving. One of the tools used for artificial intelligence is neural networks. Neural networks are modeled after the neurons in the human brain, where a trained algorithm determines an output response for input signals. The main categories of neural networks are feedforward neural networks, where the signal passes only in one direction, and recurrent neural networks, which allow feedback and short-term memory of previous input events. 
     A wide variety of platforms has allowed different aspects of AI to develop. Advances in deep artificial neural networks and distributed computing have led to a proliferation of software libraries, including Deeplearning4j, which is open-source software released under Apache License 2.0 and supported commercially by Skymind of San Francisco, California, and TensorFlow, an artificial intelligence system which is open-source released under Apache License 2.0, developed by Google, Inc. 
     Deep learning comprises an artificial neural network that is composed of many hidden layers between the inputs and outputs. The system moves from layer to layer to compile enough information to formulate the correct output for a given input. In artificial intelligence models for natural language processing, words can be represented (also described as embedded) as vectors. Vector space models (VSMs) represent or embed words in a continuous vector space where semantically similar words are mapped to nearby points (are embedded nearby each other). Two different approaches that leverage VSMs are count-based methods and predictive methods. Count-based methods compute the statistics of how often some word co-occurs with its neighbor words in a large text corpus, and then maps these count-statistics down to a small, dense vector for each word. Predictive models directly try to predict a word from its neighbors in terms of learned small, dense, embedding vectors. 
     Neural probabilistic language models are traditionally trained using the maximum likelihood (ML) principle to maximize the probability of the next word given previous words (or context) based on the compatibility of the next word with the context. The model is trained by maximizing its log-likelihood on a training set. The objective is maximized when the model assigns high probabilities to the words which are desired (the real words) and low probabilities to words that are not appropriate (the noise words). 
     A framework is provided that allows a model builder to express a machine learning algorithm symbolically, wherein the machine learning algorithm is modeled as a computation graph. This can interface with a set of Python classes and methods that provide an API interface, resulting in re-targetable systems that can run on different hardware. 
     The learned values from the recurrent neural network may also be serialized on disk for doing the inference step at a later time. These learned values are stored in multidimensional arrays that also contain shape and type information while in memory. The TensorFlow software libraries call these multidimensional arrays tensors. 
     C. Systems and Methods for Creating, Controlling and Executing Simulated Phishing Campaigns Using Artificial Intelligence. 
     The following describes systems and methods of creating, controlling and executing simulated phishing campaigns using artificial intelligence. 
     A system can be configured to send multiple simulated phishing emails, text messages, phone calls (e.g. via VoIP) and Internet based communications, varying the quantity, frequency, type, sophistication, content, timing, and combination of messages using machine learning algorithms or other forms of artificial intelligence. Such a system may be referred to as an artificial intelligence driven agent system, or AIDA system, or simply a system. The set of phishing emails, text messages, and/or phone calls may be referred to as a simulated phishing campaign. In some implementations, some or all messages (email, text messages, VoIP calls, Internet based communications) in a simulated phishing campaign after the first simulated phishing message may be used to direct the user to open the first simulated phishing message, or to open the latest simulated phishing message. In some implementations, simulated phishing messages of a campaign may be intended to lure the user to perform a different requested action, such as selecting a hyperlink in an email or text message, or returning a voice call. 
     In some implementations, the system may adaptively learn the best method (e.g., set of steps) and/or the best combination of messages to get the user to perform the requested action, such as interacting with a hyperlink or opening a file. The learning process implemented by the system can be trained by observing the behavior of other users in the same company or in the same industry, by observing the behavior of all other users of the system, or by observing the behavior of a subset of other users in the system based on one or more attributes of the subset of other users meeting one or more selected criteria. 
     The system can record when and how the action was performed and can produce reports about the actions. The reports can track the number of users the simulated messages were sent to, whether messages were successfully delivered, whether a user performed a requested action, when a requested action was performed, and a combination and timing of messages that induced a user to perform a requested action. In some implementations, the system may provide training on why a user should not have performed a requested action at the time that the user performs the requested action. 
     An AIDA system may use information from many sources to create, train, and refine artificial intelligence models to create simulated phishing messages for users. As examples, an AIDA system may extract information from the past efficiency of templates that have been used to phish users. An AIDA system may extract information that was made public due to a data breach. An AIDA system may extract information from past user communications with a security awareness system. An AIDA system may use information from user profiling, for example language, gender, political affiliation, interests and career information. An AIDA system may use information found on social media. An AIDA system may use information from logs from previous simulated phishing campaigns, including all actions performed on a user and all user actions performed. An AIDA system may use information from event logs, for example Windows event logs. An AIDA system may use information from learning management system (LMS) analysis, which may inform the AIDA systems as to exactly what training a user has had, where the user performed well and where the user struggled with the training that the user completed, and what the user should know. An AIDA system may use information from company profiling activities, for example email exposure check results, applications used, software as a service (SaaS) services used, etc. An AIDA system may use information from industry profiles corresponding to an industry that a user’s company is associated with. 
     In some embodiments, an AIDA system is capable of performing risk analysis of users, groups of users, or a company. For example, an AIDA system may be able to perform a risk profile of a user with respect to wire transfer fraud, or IP theft. In some embodiments, an AIDA system can track events in a company and/or for a user in a company to identify one or more risk points. In some embodiments, an AIDA system can track information that a given user is exposed to in order to identify a risk point. For example, employees in a company that regularly deal with wire transfers may be likely to be at a higher risk for wire transfer fraud, and people that are exposed to sensitive information may be at a higher risk for leaking intellectual property. 
     In some embodiments, an AIDA campaign duration is limited to a fixed period of time, for example a fixed number of days. In some embodiments, an AIDA campaign will terminate once a certain percentage of users fail the campaign. In some embodiments, an AIDA campaign will terminate if a certain percentage of users fail the campaign in a first period of time. In some embodiments, an AIDA campaign stops for a specific user once that user fails a simulated phishing test as part of the campaign. 
     Referring to  FIG.  2 A  in a general overview,  FIG.  2 A  depicts some of the architecture of an implementation of a system  200  capable of creating, controlling and executing simulated phishing campaigns using artificial intelligence. In some implementations, the system  200  includes a server  106  and a client  102  and a network  104  allowing communication between these system components. The server  106  may include an AIDA system  215 , a simulated phishing campaign manager  251 , a trusted domains storage  255 A, an untrusted domains storage  255 B, and a simulated phishing emails storage  256 . The AIDA system  215  may include a system monitoring module  270 , a campaign controller  250 , a company administrator console  295 , and a security awareness system server  280 . The simulated phishing campaign manager  251  may include a user interface manager  252  and a simulated phishing message generator  253 . The simulated phishing message generator  253  may include a virtual machine  254 . The client  102  may include a communications module  234 , a user interface  235 , a display  236 , a messaging application  237 , an executing application  238 , a storage for trusted domains  245 A, and a storage for untrusted domains  245 B. 
     The server  106  may be a part of a cluster of servers  106 . In some embodiments, tasks performed by server  106  may be performed by a plurality of servers. These tasks may be allocated among the plurality of servers by an application, service, daemon, routine, or other executable logic for task allocation. The server  106  may include a processor and memory. Some or all of server  106  may be hosted on cloud  108 , for example by Amazon Web Services (AWS). 
     Each of the server  106 , the AIDA system  215 , and the simulated phishing campaign manager  251 , and any components or modules thereof, may comprise a program, service, task, script, library, application, or any type and form of executable instructions or code executable on one or more processors. Any of the server  106 , the AIDA system  215 , and/or the simulated phishing campaign manager  152  may be combined into one or more modules, applications, programs, services, tasks, scripts, libraries, applications, or executable code. 
     The simulated phishing campaign manager  251  includes a simulated phishing message generator  253 , which may be implemented as or contain a virtual machine  254 . Responsive to a user input, the simulated phishing campaign manager  251  generates a campaign for a simulated phishing attack, including one or more selected phishing message templates, one or more selected landing page templates, and one or more selected targeted user groups, in addition to other user input. 
     The simulated phishing campaign manager  251  may manage various aspects of a traditional simulated phishing attack campaign, for example a simulated phishing attack campaign that does not use an artificial intelligence driven agent (AIDA). For example, the simulated phishing campaign manager  251  may process input from the server  106  and/or may provide access as needed to various applications, modules, and other software components of server  106  to other various applications, modules, and other software components of server  106 . The simulated phishing campaign manager  251  may monitor and control timing of various aspects of a simulated phishing attack campaign, may process requests for access to simulated attack campaign results, and/or may perform other tasks related to the management of a simulated phishing attack campaign. 
     In some embodiments, the simulated phishing campaign module  251  may be integrated with or coupled to memory  122 . In some embodiments, the memory may include any type and form of storage, such as a database or file system. The memory  122  may store data such as parameters and scripts corresponding to the choices made by a server  106  through a simulated phishing campaign manager  251 , e.g. as described above for a particular simulated phishing attack. 
     In an implementation, the simulated phishing campaign manager  251  includes a simulated phishing message generator  253 . The simulated phishing message generator  253  may be integrated with or coupled to the memory  122  so as to provide the simulated phishing message generator  253  access to parameters associated with messaging choices made for a particular simulated campaign by e.g. the server  106 . The simulated phishing message generator  264  may be integrated with or coupled to memory or a memory store or otherwise a storage, such as a database, containing simulated phishing emails  256 . The simulated phishing message generator  253  may be an application, service, daemon, routine, or other executable logic for generating messages. The messages generated by the simulated phishing message generator  253  may be of any appropriate format. For example, they may be email messages, test or SMS messages, messages used by particular messaging applications such as, e.g. WhatsApp™, or any other type of message. Message types to be used in a particular attack may be selected by e.g. a server  106  using a simulated phishing campaign manager  251 . The messages may be generated in any appropriate manner, e.g. by running an instance of an application that generates the desired message type, such as running e.g. a Gmail™ application, Microsoft Outlook™, WhatsApp™, a text messaging application, or any other appropriate application. The messages may be generated by running a messaging application on e.g. a virtual machine  254 , or may simply be run on an operating system of the server  106 , or may be run in any other appropriate environment. The messages may be generated to be formatted consistent with specific messaging platforms, for example Outlook  365 , Outlook Web Access (OWA), Webmail, iOS, Gmail client, and so on. 
     In some embodiments, the simulated phishing message generator  253  can be configured to generate messages having the ability to traverse users who interact with the messages to a specific landing page. 
     In some embodiments, the simulated phishing message generator  253  can be configured to generate a simulated phishing email. The email can appear to be delivered from a trusted email address, such as the email address of an executive of the company at which the targeted user is employed. In addition, the email can have a “Subject:” field that is intended to cause the user to take an action, such as initiating a wire transfer. In some embodiments, the simulated phishing message generator  253  can generate one or more simulated phishing emails which are stored in the simulated phishing emails storage  256 . In some embodiments, the simulated phishing message generator  253  can generate multiple instances of the email which may be delivered to multiple users, such as a subset of all of the employees of the company. In some embodiments, the simulated phishing message generator  253  can generate multiple instances of the email which may be delivered to a user group. For example, the server  106  can select any number of employees who should be targeted by a simulated attack, can create a user group and store this user group in the memory  122 . The simulated phishing message generator  253  can retrieve this information from the memory  122  and can generate a set of emails similar to the email, each addressed to a respective target identified in the information stored in the memory  122 . That is, the simulated phishing message generator  253  can generate the emails such that the “From:” and “Subject:” fields of each email are identical, while the “To:” field is adjusted according to the desired targets. 
     In an implementation, a simulated phishing campaign manager  251  may be e.g., another name for a system administrator, such as a security manager, a third-party security consultant, a risk assessor, or any other party that uses the simulated phishing campaign manager  251  installed on a server. The server  106  may wish to direct a simulated phishing attack by interacting with the simulated phishing campaign manager  251  installed on the server. The simulated phishing campaign manager  251  may be, for example, a desktop computer, a laptop computer, a mobile device, or any other suitable computing device. The simulated phishing campaign manager  251  may be e.g., an application on a device that allows for a user of the device to interact with the server  106  for e.g. purposes of creating, configuring, tailoring and/or executing a simulated phishing attack and/or viewing and/or processing and/or analyzing the results of a phishing attack. 
     In an implementation, the simulated phishing campaign manager  251 , when executed, causes a graphical user interface to be displayed to the server  106 . In other embodiments, the simulated phishing campaign manager  251  allows for user input through a non-graphical user interface, such as a user interface that accepts text or vocal input without displaying an interactive image. A graphical user interface may be displayed on a screen of a mobile phone, or a monitor connected to a desktop or laptop computer, or may be displayed on any other display. The user may interact with e.g. the graphical user interface on the device by typing, clicking a mouse, tapping, speaking, or any other method of interacting with a user interface. The graphical user interface on the device may be a web-based user interface provided by a web browser (e.g. Google Chrome, Microsoft Internet Explorer, or Mozilla Firefox provided by Mozilla Foundation of Mountain View, California), or may be an application installed on a user device capable of opening a network connection to simulated phishing campaign manager  251 , or may be any other type of interface. 
     In an implementation, the simulated phishing campaign manager  251  and/or server  106  may make choices concerning how a simulated phishing attack is to be carried out. For example, a graphical user interface run by the simulated phishing campaign manager  251  may be displayed to the server  106 . An administrator, via the server  106 , may input parameters for the attack that affect how it will be carried out. For example, via the server  106  an administrator may make choices as to which users to include as potential targets in the attack, the method of determining which users are to be selected as targets of the attack, the timing of various aspects of the attack, whether to use an attack template that includes values for one or a plurality of failure indicators, how responses from targeted users should be uniquely identified, and other choices. These choices may be made by selecting options displayed on a graphical user interface from dropdown menus, being presented with choices through a simulated attack wizard, or in any other appropriate manner. 
     In an implementation, the simulated phishing campaign manager  251  may allow the server  106 , such as via application programming interfaces (APIs), to access and/or change settings of an account maintained with any party involved with the attack, such as, for example, a third party security service provider, or may allow the server  106  to access and/or change settings of an account maintained with a third party security service provider, such as one that e.g. manages an exploit server, view bills and/or make payments to a third party security service provider, to perform these functions with other third parties involved in the attack, or provide any other functions that would be appropriate for facilitating communications between the server  106  and any other parties involved in the attack. 
     The system  200  also includes the client  102 . A client may be a target of any simulated phishing attack. For example, the client may be an employee, member, or independent contractor working for an organization that is performing a security checkup or conducts ongoing simulated phishing attacks to maintain security. The client  102  may be any device used by the client. The client does not need to own the device for it to be considered a client device  102 . The client  102  may be any computing device, such as a desktop computer, a laptop, a mobile device, or any other computing device. In some embodiments, the client  102  may be a server or set of servers accessed by the client. For example, the client may be the employee or a member of an organization. The client may access a server that is e.g. owned or managed or otherwise associated with the organization. Such a server may be a client  102 . 
     In some implementations, client  102  may include a communications module  234 . This may be a library, application programming interface (API), a set of scripts, or any other code that may facilitate communications between the client  102  and any of the server  106 , a third-party server, or any other server. In some embodiments, the communications module  234  determines when to transmit information from the client  102  to the external servers via a network  104 . In some embodiments, the information transmitted by the communications module  264  may correspond to a message, such as an email, generated by the messaging application  237 . 
     In some embodiments, the client  102  may include a user interface  235  such as a keyboard, a mouse, a touch screen, or other appropriate user interface. This may be a user interface that is e.g. connected directly to a client  102 , such as, for example, a keyboard connected to a mobile device, or may be connected indirectly to a client  102 , such as, for example, a user interface of a client device used to access a server client  102 . The client may include a display  236 , such as a screen, a monitor connected to the device in any manner, or any other appropriate display. 
     In an implementation, the client  102  may include a messaging application  237 . The messaging application  237  may be any application capable of viewing, editing, and/or sending messages. For example, the messaging application  237  may be an instance of an application that allows viewing of a desired message type, such as any web browser, a Gmail™ application, Microsoft Outlook™, WhatsApp™, a text messaging application, or any other appropriate application. In some embodiments, the messaging application  237  can be configured to display simulated phishing attack emails. Furthermore, the messaging application  237  can be configured to allow the target to generate reply messages or forwarded messages in response to the messages displayed by the messaging application  237 . 
     The client  102  may include storage for trusted domains  245 A and untrusted domains  245 B. Each of the client  102 , messaging application  237 , executing application  238 , client service  242 , and user console  243  may comprise a program, service, task, script, library, application or any type and form of executable instructions or code executable on one or more processors. Any of the client  102 , messaging application  237 , executing application  238 , client service  242 , and/or user console  243  may be combined into one or more modules, applications, programs, services, tasks, scripts, libraries, applications, or executable code. 
     The client  102  receives messages sent by the server  106  based upon the campaign created and executed by the simulated phishing campaign manager  251  and/or by the AIDA system  215 . The client  102  is able to receive the simulated phishing messages via the messaging application  237 , display the received messages for the user using the display  236 , and is able to accept user interaction via the user interface  235  responsive to the displayed message. In some embodiments, if the user interacts with the simulated phishing message, the client traverses to a landing page selected for the phishing campaign. 
     Referring to  FIG.  2 B , in a general overview,  FIG.  2 B  depicts some of the architecture of an implementation of an AIDA system  215  capable of creating, controlling and executing simulated phishing campaigns using artificial intelligence. The AIDA system  215  may include a system monitoring module  270 , a campaign controller  250 , a company administrator console  295 , and a security awareness system server  280 . The system monitoring module  270  may include metrics management  271 , error tracking  272 , and warning count tracking  273 . The company administrator console  295  may include metrics generator  296 , phish-prone percentage calculator  297 , and dashboard generator  298 . Security awareness system server  280  may include security awareness system administrator  288 , LDAP  289 , active directory  290 , a display  291 , and an administrator console  292 . Security awareness system server  280  may include a training modules storage  281 , a landing domains storage  282 , a landing pages storage  283 , an accounts storage  284 , a users storage  285 , a groups storage  286 , and a memberships storage  287 . Campaign controller  250  may include a serving module  230 , a continuously block  231 , a model retraining module  232 , and a new campaign monitor  233 . Campaign controller  250  may include workers  260 , email workers  261 , and website workers  263 . Campaign controller  250  may include a text to speech engine  240 , an action queue  266 , and incoming email queue  264 , and an outgoing email queue  265 . Campaign controller  250  may include a campaigns storage  201 , a campaign recipients storage  202 , a template details storage  203 , a templates storage  204 , a “call from” phone numbers storage  205 , a campaign groups storage  206 , a tagging storage  207 , a tags storage  208 , a models storage  216 , one or more campaign recipient actions table storages  220   a  ...  220   n , a VoIP content storage  241 , and an email database  262 . 
     Any of the AIDA system  215 , the system monitoring module  270 , the campaign controller  250 , company administrator console  295 , a security awareness system server  280 , the metrics management  271 , error tracking  272 , and warning count tracking  273  may comprise one or more a program, service, task, script, library, application, or any type and form of executable instructions or code executable on one or more processors. 
     In some embodiments, the system monitoring module or system monitor  270  keeps track of the health of functional blocks of the system  200 . In some embodiments, the system monitoring module monitors the delays, queues, loads, and other parameters of the system  200 , such that the security awareness system administrator  288  can keep track of the system  200 . In some embodiments, the system monitoring module  270  includes metrics management  271 , which keeps track of any performance metrics for any functional block or module in the system. In some embodiments, metrics management  271  keeps track of the number of messages processed in a given unit of time. In some embodiments, metrics management  271  keeps track of how many instances of each functional block are in use at a given time. In some embodiments, metrics management  271  keeps track of how many of each type of messages were sent. In some embodiments, metrics management  271  keeps track of how many actions were stored in the one or more actions table(s). In some embodiments, metrics management  271  keeps track of how many messages or different types were put into different queues. In some embodiments, the system monitoring module  270  includes error tracking  272 . In some embodiments, error tracking  272  keeps track of actions in a queue which are not processed. In some embodiments, error tracking  272  keeps track of user email addresses that are incorrect. In some embodiments, error tracking  272  raises an error if the system monitoring module  270  cannot access one or more databases. In some embodiments, the system monitoring module  270  includes warning count tracking  273 . In some embodiments, warning count tracking  273  keeps track of the number of warnings that have occurred in a period of time. 
     The data identified, monitored, obtained or processed by the system monitoring module may be stored in any type and form of database, files or logs. In some embodiments, such data may be stored in a time series type or based database. In some embodiments, the data for the system monitoring module may be stored in an open source time series database that is optimized for fast, high-availability storage and retrieval of time series data. An example of an open-source time series database is INFLUXDB, which is written in programming language GO and is provided by InfluxData of San Francisco, California. In some embodiments, the time series database is hosted in the cloud. In some embodiments, the time series database is local to the server  106 . 
     The data that is stored by the system monitoring module may be processed, analyzed and displayed via a tool and/or user interface. The tool and/or user interface may allow and/or provide for a system administrator to query and alert on metrics and create a managed dashboard to visually display the data and metrics. In some embodiments, the time series data that is accessed by the system monitoring module is analyzed and visually displayed using an open source software platform to allow a security system administrator  288  to query and alert on metrics and to create dashboards to visually display time series data. An example of an open source software platform for time series analytics and visualization is Grafana, which is provided by GrafanaLabs (formerly known as Raintank) of New York, New York. In some embodiments, the analytics and visualization platform is hosted in the cloud. In some embodiments, the analytics and visualization platform is hosted locally on the server  106 . In some embodiments, the analytics and visualization platform is an open source platform. In some embodiments, the analytics and visualization platform is proprietary to the security awareness system provider. In some embodiments, the system monitoring module  270  retrieves the time series data in one or more folders on the server  106 . In some embodiments, the system monitoring module  270  uses plug-ins to retrieve the time series data. In some embodiments, the system monitoring modules uses an API to enable a loading mechanism to retrieve the data. 
     In some embodiments, more than one instance of the system monitoring module  270  may exist. In some embodiments, there exists one or more instance of the system monitoring module  270  to monitor one or more model managers  370 . In some embodiments, there exists one or more instance of the system monitoring module  270  to monitor the operation of one or more campaign controllers  250 . In some embodiments, one or more instance of the system monitoring module  270  monitors both the model creation and the operation of the campaign controller  250 . In some embodiments, one or more instance of the system monitoring module  270  keeps track of the health of one or more workers  260 , one or more email workers,  261 , one or more serving modules  230 , one or more model controllers  320 , and one or more Q&amp;A workers  315 . 
     AIDA system  215  may include a company administrator console  295 . The company administrator console  295  enables an administrator of an account to create an AIDA simulated phishing campaign (also referred to as an AIDA campaign) using a user interface, such as graphical user interface or command line interface, and/or an application programming interface (API). In some embodiments, the company administrator, via the company administrator console  295 , inputs the date and time that they want the AIDA campaign to start. In some embodiments, the company administrator inputs the time zone for the AIDA campaign. In some embodiments, the company administrator selects whether or not the AIDA campaign should allow text messages. In some embodiments, the company administrator selects whether or not the AIDA campaign should allow VoIP calls. In some embodiments, the company administrator selects the user groups that are to be included in the AIDA campaign. In some embodiments, the company administrator can select from one or more pre-existing user groups. In some embodiments, the company administrator can select from one or more users to create a new user group. 
     In some embodiments, the company administrator console  295  includes metrics generator  296  which tracks metrics about what happened in the AIDA campaign. In some embodiments, metrics generator  296  tracks the number of users of the AIDA campaign. In some embodiments, metrics generator  296  tracks the number of VoIP calls made in the AIDA campaign. In some embodiments, metrics generator  296  tracks the number of text messages sent in the AIDA campaign. In some embodiments, metrics generator  296  tracks the number of emails sent in the AIDA campaign. In some embodiments, metrics generator  296  tracks the number of user interactions with links in the AIDA campaign. 
     In some embodiments, the company administrator console  295  includes a metric generator or calculator  296 , such as a phish-prone percentage calculator  297 . The metric generator may establish, generate or calculate any type and form of metrics and/or statistics related to any of the data for any simulated phishing campaigns, any data processed, identified or provided by the campaign controller and/or and data stored in any of the models, and/or any data stored in any of the databases described herein. The metric generator may establish, generate or calculate any type and form of metrics and/or statistics related to any of the data stored in, with or associated with any of the following for example: campaign recipient actions  220 , campaigns  201 , campaign recipients  202 , template details  203 , templates  204 , “call from” phone numbers  205 , campaign groups  206 , tagging  207 , tags  208 , training modules  281 , landing domains  282 , landing pages  283 , accounts  284 , users  285 , groups  286 , memberships  287 , trusted domains  245  and  255 , untrusted domains  245  and  255 , simulated phishing emails  256 , any of the models  216 , metagraph  361 , Q&amp;A pairs  350 , approved Q&amp;A pairs  351 , neurons  363 , training Q&amp;A pairs  352 , Testing Q&amp;A pairs  353 , all configuration super parameters  363 , groups  286 , memberships  287 , accounts  284  and users  285 . The metrics and/or statistics may include any type and form of average, mean, summation, percentages, count and/or function of any one or more data items or combination of data items including over any time period or frequency or temporal parameters. 
     In some embodiments, phish-prone percentage calculator  297  calculates a phish-prone percentage as the percentage of users that interacted with a link in the AIDA campaign out of the total number of users that received messages as part of the campaign. In some embodiments, phish-prone percentage calculator  297  calculates a phish-prone percentage as the percentage of messages for which a user interacted with a link in the message as part of the AIDA campaign out of the total number of messages sent in the AIDA campaign. In some embodiments, phish-prone percentage calculator  297  calculates the phish-prone percentage across all of the campaigns that have been executed for the company. In some embodiments, phish-prone percentage calculator  297  calculates the phish-prone percentage for the most recent AIDA campaign for the company. 
     In some embodiments, the company administrator console  295  includes dashboard generator  298 . In some embodiments, dashboard generator  298  displays an overview page which displays information about an AIDA campaign. In some embodiments, dashboard generator  298  generates a display of the number of times a user interacts with a link in a simulated phishing message that is part of an AIDA campaign over a given time period after the start of the AIDA campaign. In some embodiments, dashboard generator  298  generates a display of the number of times a user has interacted with a link in each of the first number of time periods after the start of an AIDA campaign. In some embodiments, the time period is one hour. In some embodiments, dashboard generator  298  displays a circle with a size that is proportionate to the number of interactions with a simulated phishing message in a time period, wherein the greater the number of user interactions with links in simulated phishing messages, the larger the size of the circle that is displayed. In some embodiments, dashboard generator  298  displays the status of the AIDA campaign as one of stopped, started, paused, ongoing, discontinued, completed, finished, cancelled, restarted, or aborted. In some embodiments, dashboard generator  298  displays the date and time that an AIDA campaign was created on. In some embodiments, dashboard generator  298  displays the date an AIDA campaign was started on. In some embodiments, dashboard generator  298  displays the end date of an AIDA campaign. In some embodiments, if the campaign is one of stopped, paused, ongoing, discontinued, cancelled, restarted, or aborted, the end date is displayed as “Not Finished”. In some embodiments, the company administrator can highlight a specific recipient and see all the actions performed on that recipient (e.g. messages sent to the recipient, what detail page was used, when the message was sent, etc.) and all the actions that the recipient performed (e.g. clicked on a link in a text message, responded to an email, etc.). For example, if there is a record in the one or more campaign recipient actions table(s) indicating that the campaign controller  250  sent them an email, then the company administrator can click on this action and the company administrator console  295  displays a copy of the detail page of the template that was used to generate the email that the user received. 
     In some embodiments, dashboard generator  298  displays information about the browser, agent or platform that the user uses to view the messages of a campaign. In some embodiments, dashboard generator  298  displays information about multiple user’s browsers, agents, or platforms in a pie chart format. 
     In some embodiments, dashboard generator  298  displays a user page which displays an AIDA campaign report which individually shows actions associated with each of the recipients of the AIDA campaign. In some embodiments, dashboard generator  298  displays one or more metrics of the campaigns across one or more tabs, and when a company administrator clicks on one of the tabs, more detailed information is shown to the company administrator. In one embodiment, dashboard generator  298  generates one or more tabs for AIDA campaign recipients, emails sent, emails delivered, emails opened, emails clicked, emails bounced, SMS messages sent, SMS messages clicked, SMS message errors, phone calls made, and phone call errors. In one embodiment, when the company administrator selects the recipients tab, the dashboard generator displays a list of the email addresses of all of the recipients of the AIDA campaign and an indication of whether or not they failed the campaign. 
     Referring briefly to  FIGS.  2 C,  2 D and  2 E  are examples of user interfaces and/or dashboards for displaying metrics and statistics about simulated phishing campaigns. An administrator can click on any of the tabs of the example user interfaces to see any of the following information, including any details for the same: EMAILS DELIVERED, EMAILS OPENED, EMAILS CLICKED, EMAILS BOUNCED, SMS SENT, SMS CLICKED, SMS ERRORS, PHONE CALLS MADE, PHONE CALL ERRORS. The administrator can see for each user each email the user received, if the user clicked on a link in the email and/or each SMS sent, and/or each phone call placed. If the administrators clicks or hovers over any of the information indicators in the user interface of  FIGS.  2 C- 2 E , the system will provide more information on the cause of the error or issue. 
     AIDA system  215  may include a security awareness system  280  running on one or more servers, sometimes also referred to as security awareness system server. The security awareness system  280  may comprises one or more applications, programs, services, processes, libraries or any type and form of executable instructions executable on one or more computing devices. Security awareness system  280  provides a user interface for the security awareness system administrator  288  through the administrator console  292 . In some embodiments, the administrator console  292  provides an interface for the security awareness system administrator  288  to make updates on one or more of the campaign controllers  250  and the workers  260  to enable the use of a specific version of a model. In some embodiments, the administrator console  292  on the security awareness system  280  provides an interface for security awareness system administrator  288  to add new versions of template detail pages for one or more templates. In some embodiments, the administrator console  292  on the security awareness system  280  provides an interface for security awareness system administrator  288  to specify the usage percentage for one or more template detail pages of a template, such that each template detail page gets used a specified percentage of the time. In some embodiments, the usage percentages for specific template detail pages and/or specific versions of template detail pages are calculated using count values for records that have the same template detail ID. Each time the template detail ID gets used, the percentage that each version of the template detail page has been used is calculated using the count values. The version of the template detail ID page that is the greatest amount less than the target usage percentage gets used in creating the message. In some embodiments, when the security system administrator  288  adds one of a new template detail page and a new version of a template detail page, the count values of records with the same template detail ID are set to zero. 
     In some embodiments, the security awareness system  280  includes display  291 .The display  291  may provide a user interface and/or dashboard to show or display any results from execution of simulated phishing campaigns and allow a user to review any such results. In some embodiments, display  291  is used to display system information provided by the system monitoring module  270 . In some embodiments, the display provides an administrator console interface or user interface from which a user can edit, create, and/or manage one or more of the following: accounts, phishing templates, landing pages, landing domains, templates, such as AIDA or training templates, training modules and any of the other components, modules, functions of any of the system described herein. 
     In some embodiments, the security awareness system  280  includes an active directory  290  and LDAP  289  and/or interfaces to an active directory  290  running or operating on one or more other devices using an LDAP (Lightweight Directory Access Protocol) protocol  289 . In some embodiments, LDAP  289  is the protocol used to communicate with active directory  290 . In some embodiments, LDAP  289  is a service that implements LDAP and provides services to access LDAP based systems, such as the active directory. In some embodiments, the server of the security awareness system implements or provides the active directory. In some embodiments, another server implements or provides the active directory. The security awareness system interfaces or accesses the active directory  290  to identify, obtain and/or extract user information, such as email address, first and last name, location, manager information and any other information about the user stored in the active directory. Any information stored or provided by the active directory  290  may be used by the campaign controller for creating, managing or executing simulated phishing campaigns. In some embodiments, the campaign controller accesses or interfaces to the active directory  290 , such as via LDAP. In some embodiments, the campaign controller communicates or interfaces with the security awareness system to obtain the user information from the active directory  290 . In some embodiments, users may be imported or added manually, such as if an active directory is not used. 
     In some embodiments, the security awareness system  280  includes a storage for training modules  281 . In some embodiments, the security awareness system  280  includes a storage for landing domains  282 . The security awareness system may store any of the training modules and/or landing domains in any type and form of database, including cloud based storage or local storage. 
     In some embodiments, the security awareness system  280  includes a storage for landing pages  283 . A landing page may comprise a uniform resource locator or domains constructed to identify or point back to a server or system maintained or known by the server  280  and/or campaign controller. In some embodiments, the URL or domain identifies a tracking service or server of the system used for tracking. In some embodiments, the URL or domain is constructed to mimic, masquerade, disguise or simulate a domain or URL they are not. In some embodiments, the data structure for the landing page information stored for each landing page in the landing pages storage  283  includes one or more of a landing page ID, the HTML content of the landing page, the title of the landing page, one or more identifiers of the landing page, the account (company) ID that the landing page is to be used for, the landing page category ID, the date and time the landing page was created at, and the date and time the landing page was updated at. The categories identified by the category ID for the landing pages can be any predetermined category provided by the system or user generated or specified categories. The landing page categories may be used to group landing pages based on common traits or attributes. Some examples of categories include but are not limited to: point of failure video training, phishing for sensitive information, and error pages. The categories may be based on a type of campaigns, templates, models, personas, companies, groups of users or attributes of any of the foregoing. In some embodiments, landing pages may be assigned to one category, while in other embodiments, landing pages may be assigned to multiple categories. 
     In some embodiments, the security awareness system  280  includes a storage for accounts  284 . In some embodiments, the data structure for the account information stored for each account in accounts storage  284  includes one or more of an account ID, a company name, a company address, a company phish-prone percentage, an industry ID, a company size, the business hours for the company, the days of the week that the company operates, the region of the company, and the time zone of the company. In some embodiments, the account storage  284  is a relational database. In some embodiments, the account storage relational database  284  has a relationship with users storage  285 , wherein the relationship links one or more user records from users storage  285  to an account ID. In some embodiments, account storage relational database  284  has a relationship with groups storage  286 , wherein the relationship links one or more group records from groups storage  286  to an account ID. In some embodiments, the account storage relational database  284  has a relationship with campaigns storage  201 , wherein the relationship links one or more campaign records from campaigns storage  201  to an account ID. 
     In some embodiments, the security awareness system  280  includes a storage for users  285 . In some embodiments, the data structure of the user information stored for each user in users storage  285  includes one or more of a user ID, a user email address, the account ID associated with a user, a user’s name, a user’s job title, a user’s phone number, a user’s mobile phone number, a user’s location, what time zone a user is in, a user’s division, a user’s manager’s name, a user’s manager’s email address, a user’s employee number, a user’s gender, and the date and time that a user’s record was created and/or updated. 
     In some embodiments, the security awareness system  280  includes a storage for groups  286 . In some embodiments, the data structure of the group information stored for each group in groups storage  286  includes one or more of a group ID, an account ID associated with a group, a name of the group, and a date and time that the group record was created and/or updated. In some embodiments, groups storage  286  is a relational database. In some embodiments, groups storage relational database  286  has a relationship with users storage  285 , wherein the relationship links one or more users from users storage  285  to a group ID. 
     In some embodiments, the security awareness system  280  includes a storage for memberships  287 . In some embodiments, membership storage  287  is a relational database which links users to groups. In some embodiments, the data structure of the membership information stored in memberships storage  287  includes one or more of a membership ID, a user ID, a group IP, and a date and time that a membership record was created and/or updated. In some embodiments, memberships storage  287  lists which users are in which groups. In some embodiments, a user can be in multiple groups. 
     AIDA system  215  may include one or more campaign controllers  250 . In some embodiments, the campaign controller includes a serving module  230 . The campaign controller includes, is configured with or implemented to have any of the instructions, function and/or logic to perform the operations and functionality of the campaign controller described herein, such as creating, managing and executing a simulated phishing campaign In some implementations, the serving module is the intelligent engine or brain of campaign controller  250  that receives and processes input related to a campaign and provides output regarding the operation, instruction or functions for a campaign The serving module  230  uses information, such as from any of the storage or databases described herein, to design a customized AIDA simulated phishing campaign for a given user, such as a campaign that is likely to have the highest probability of getting that specific user to interact with a link. The serving module may use information about any results from executing simulated phishing campaigns for that user and/or other users. 
     In some embodiments, design choices for an AIDA campaign include choice of model, choice of template including detail pages that will get used, when to start the campaign, duration of the campaign, frequency or how often to test a campaign recipient, type(s) of communications or messages (e.g., email, text, VoIP, etc.) of the campaign and a timing of the campaign. In some embodiments, the choice of a template for a given user may be made based on user attributes, or it may be randomly selected. In some embodiments, templates are available in advance, and each template could have any number of emails, text or VoIP calls, in any order. In some embodiments, the detailed pages and steps in a campaign are pre-determined when a template is created. In some embodiments, a state machine progresses an AIDA campaign through each stage of a template, performing actions that need to be performed with timing that is associated with that template. For example, the stages of a template may be “send an email”, followed by “send a text”, followed by “call”. The template gets worked through from front to back until a user action occurs which indicates that they have failed the test and need to go for training. As soon as a user interacts with a link, the AIDA campaign for that user stops. A template may have any number of steps and any combination of different message types. In one embodiment, a template comprises one of each of an email, an SMS or text message, and a VoIP call. 
     In some embodiments, serving module  230  will provide to a campaign controller  250  combinations of data about the user and campaign controller  250  may use that data to further customize an AIDA campaign for that user. In some embodiments, data may include information about the back-off time to be used between messages, information about specific detail pages related to a template for a model selected for the user, and information representing specific wording of messages that are sent to the user. In some embodiments, serving module  230  knows which model and version of the model to use for a given user in a given campaign through reading information from template details storage  203 . In some embodiments, serving module  230  periodically polls one or more model storages  216  in order to determine if a new model is available or a new version of a model is available. In some embodiments, serving module  230  will load the new model or the new version of the model to memory so that the model can be used by campaign controller  250 . Multiple models can be loaded at one time, and multiple versions of a single model can be loaded at one time. In some embodiments, serving module  230  can view and access all models and all versions of all models. 
     In some embodiments, serving module  230  determines or selects a persona model from models storage  216  with which to phish a given AIDA campaign recipient for a given AIDA campaign. In some embodiments, serving module determines or selects a persona model that meets one or more criteria or threshold for a rate of success for a user or group of users. In some embodiments, serving module determines or selects a persona model that is more likely or most likely, such as via machine learning, to cause a user or group of users to interact with a link of a simulated phishing communication or message. In some embodiments, serving module  230  determines one or more templates and one or more detail pages within the one or more templates with which to phish a given AIDA campaign recipient for a given AIDA campaign. In some embodiments, serving module  230  determines one or more frequencies of an AIDA campaign and/or one or more timings of an AIDA campaign with which to phish a given AIDA campaign recipient for a given AIDA campaign. In one embodiment, serving module  230  determines one or more training modules for a user to undergo if the user fails a given AIDA campaign. In some embodiments, the model comprises a neural network that was created during a training process, combined with a metagraph which is a set of functions and parameters to call. In some embodiments, a metagraph is stored in metagraph storage  361 . The metagraph stores may comprise a text file or a Protobuf file. In some embodiments, serving module  230  identifies, specifies or provides the set of functions and/or parameters to call, to execute the model. 
     In some embodiments, an AIDA campaign has a defined order in which to take actions for a campaign recipient, which is defined by a template. A template may comprise any type and form of data structure, configuration and/or parameters, set of data, policies and/or rules for specifying how to create, execute and/or manage a simulated phishing campaign. The template may specify any of the design choices for the campaign, including but not limited to model, template, detail pages that will get used, when to start the campaign, duration of the campaign, frequency or how often to communicate with a campaign recipient, type(s) of communications or messages (e.g., email, text, VoIP, etc.) of the campaign, order of communications/messages and a timing of the campaign, including any timing between communications/messages. 
     In some embodiments, campaign controller  250  may create a template for an AIDA campaign as the campaign is running based on a user’s actions in response to an action sent to the user by campaign controller  250 . In some embodiments, campaign controller  250  may modify an existing template during an AIDA campaign based on a user’s actions in response to an action sent to the user by campaign controller  250 . In some embodiments, campaign controller  250  may change the order of actions in the template based on a user’s actions in response to an action sent to the user by campaign controller  250 . In some embodiments campaign controller  250  may change the content of messages described by the template detail pages and to be sent to a user, based on a user’s actions in response to an action sent to the user by campaign controller  250 . In some embodiments campaign controller  250  may change the timing of messages sent to a user based on a user’s actions in response to an action sent to the user by campaign controller  250 . In some embodiments, serving module  230  performs these functions on behalf of campaign controller  250 . In some embodiments, campaign controller  250  makes determinations based on a user’s actions in response to an action sent to the user by campaign controller  250  in addition to other information that the system knows or can obtain about the user. 
     In some embodiments, when a recipient in a campaign responds to a message of the campaign, campaign controller  250  sends the recipient’s response to serving module  230 . In some embodiments, the recipient’s response is capture as a string. In some embodiments, serving module  230  receives the recipient’s response as a string and parses the string into individual words and runs the individual words into a model in order to determine an appropriate response that will encourage the recipient to interact with a link in a message that was sent to them. In some embodiments, serving module  230  sends the string received from campaign controller  250  along with a metagraph containing a set of steps to process the string to a model. In some embodiments, serving module  230  executes the metagraph using a TensorFlow SDK. In some embodiments, the metagraph is stored in metagraph storage  361 . The SDK is a set of APIs and the order in which serving module  230  calls the APIs determines the program or order of actions to be executed. In some embodiments, serving module  230  parses the string into individual words and from the words creates vectors into a vocabulary array. In some embodiments, a vocabulary array comprises a multidimensional array containing words. In some embodiments, the vocabulary array is created using unique words sourced from the questions and answers that were used to train the model. 
     In some embodiments, serving module  230  passes an integer for every word of the string received from campaign controller  250  to the model. In some embodiments, serving module  230  sends a stop code after sending one or more integers to the model. In some embodiments, in response to receiving the inputs from serving module  230 , the model returns to serving module  230  a series of integers. In some embodiments, serving module  230  translates the integers received from the model back into words using the vocabulary array. In some embodiments, serving module  230  reconstructs a string from the words from the vocabulary array corresponding to the integers, and sends the string to campaign controller  250 . In some embodiments, campaign controller  250  uses this string to create a message to a campaign recipient. 
     In some embodiments, an appropriate response generated by serving module  230  may include another copy of the link that was in a previous message. In some embodiments, an appropriate response generated by serving module  230  may include a new link for the user to interact with. In some embodiments, serving module  230  generates an appropriate response to the campaign recipient according to a model selected for the campaign recipient for the current campaign. 
     In some embodiments, campaign controller  250  includes a model retraining module  232  or model retrainer. The model retraining module  232  periodically retrains one or more artificial intelligence models  216 . The model retraining module  232  may initiate retraining for a model after the model has been used a number of times and there is history on how effective the model has been. The model retraining module  232  may initiate retraining for a model because new information pertaining to the model has been acquired by AIDA system  215 . The model retraining module  232  may initiate retraining for a model once it has received and stored sufficient recipient feedback to the model from AIDA campaigns. Once the model training module  232  has created a new version of a model, the new version of the model is stored in the appropriate model storage  216 . In some embodiments, testing such as A/B testing may be used in order to determine if one version of a model is more effective than a second version of the model. 
     In some embodiments, campaign controller  250  includes a storage for campaigns  201 . In some embodiments, the data structure of the campaign information stored for each campaign in campaign storage  201  includes one or more of a campaign ID, an account ID, a campaign name, a date and time that the campaign is scheduled to start, a date and time that the campaign started, a date and time that the campaign ended, a group to add a user to if the user interacts with a link in a simulated phishing message, a number of delivered simulated phishing emails that were delivered for this campaign, a number of simulated phishing emails that bounced back, a number of simulated phishing emails that were opened, a number of simulated phishing emails that a recipient interacted with, a status of the campaign, a phish prone percentage, a time zone, a data and time that the campaign was created and/or updated, whether or not text and/or SMS messages are allowed for the campaign, and whether or not VoIP calls are allowed for the campaign. In some embodiments, campaign storage  201  is a relational database. In some embodiments, campaigns storage relational database  201  has a relationship with groups storage  286  and recipients storage  202 , wherein the relationship links one or more recipients to a group, and one or more groups to a campaign. In some embodiments, when a new AIDA campaign is created by the security awareness system server  280 , new campaign monitor  233  creates a record for the campaign in campaigns storage  201  when the campaign is created, based on information provided in the company administrator console  295 . Records in campaigns storage  201  are associated with accounts from accounts storage  284  which contains information about the company the campaign is associated with, for example the industry that the company is in. In some embodiments, the new campaign monitor  233  detects that a new campaign has been created by looking for records in campaign storage  201  where one of the created at date and time, the start date and time, and the scheduled at data and time of the record is in the past and where the end date and time of the record is not indicated and/or is in the future. In some embodiments, new campaign monitor  233  detects that a new campaign is running or executing by checking whether or not a corresponding process or a new process is executing or running in memory. In some embodiments, when campaign controller  250  detects a new campaign record in campaigns storage  201 , campaign controller  250  updates the campaign record in campaigns storage  201  with the actual campaign start time, and creates one or more records in campaign recipients storage  202 , for each user that is a recipient for the campaign. In some embodiments, the recipients comprise users that are selected for the campaign by the company administrator in the company administrator console  295 . In some embodiments, the recipients comprise users that are members of groups selected for the campaign by the company administrator in the company administrator console  295 . The user record created in campaign recipients storage  202  is associated with the campaign record in campaigns storage  201  for the campaign. In some embodiments, information about a user that is a recipient for a campaign is extracted from users storage  285  when the user record is created in campaign recipients storage  202 , for example a user’s email address and mobile phone number, what account the user is on, and what campaign the user is in. In some embodiments, information about a user is uploaded by a company administrator when the user record is created in campaign recipients storage  202 . In some embodiments, information about a user is created based on a synchronization process with the account active directory  290  or using the LDAP service  289  to access an account directory. In some embodiments, information about a user is created or obtained from an active directory service  290  or via an LDAP service  289 , or otherwise using LDAP to communicate with an active directory. 
     In some embodiments, the campaign controller  250  includes continuously block  231 . The continuously block may include any type and form of executable instructions performing the functions and operations described herein. In some embodiments, the continuously block is a component or module of the campaign controller. In some embodiments, the continuously block is a set of functions, operations and instructions of the campaign controller. In some embodiments, the continuously block is a logical and executable construct for performing a set of functions. As with some or all of the other components of the AIDA system  215 , multiple instances of continuously block  231  may be instantiated simultaneously for scalability and redundancy. In some embodiments, for each active AIDA campaign, continuously block  231  dynamically creates a list of campaign recipients that have not interacted with a link (e.g., all the users that are still actively in campaigns, since once a user clicks on a link the campaign ends for that user) based on the time of the last action for the recipient. In some embodiments, continuously block  231  dynamically creates this list by running a SQL query that joins to campaigns storage  201 , to campaign recipients storage  202 , and to campaign recipient actions storage  220 . In some embodiments, continuously block  231  retrieves a number of records from the dynamically created list of campaign recipients and checks the number of records to determine if AIDA system  215  should perform an action for a recipient. In some embodiments, continuously block  231  continues to retrieve a number of recipient records to check to see if the recipients should have an action performed for them. If the recipient needs an action to be performed, campaign controller  250  puts an action message into action queue  266  to perform the action for the recipient, and the recipient’s action table  220  is updated with a new record for the action that has been put into the action queue  266 . If all recipient actions have been performed and all recipient records have been checked, in some implementations continuously block  231  will sleep for a period of time and then restart checking recipient records. In some embodiments, action queue  266  is an Amazon Simple Queue Service (SQS) queue. 
     In some embodiments, continuously block  231  may use a state machine to determine if it is time to send a recipient an action. If a state machine is used to track the state of each recipient, the state machine is updated when campaign controller  250  puts the action for the recipient into action queue  266 . In some embodiments, the recipient moves from one step in a template to a next step in a template when an action is put into action queue  266  for the recipient. In some embodiments, the recipient moves from one step in a template to a next step in a template when an action is performed on a recipient. In some embodiments, when an action is put into action queue  266  for a recipient, the action is written into campaign recipient action storage  220  as a new record. In some embodiments, when an action is performed on a recipient, the action is written into the campaign recipient action storage  220  as a new record. 
     In some embodiments, continuously block  231  examines campaign storage  201  to find all actively running campaigns, and then examines campaign recipients storage  202  for all recipients in actively running campaigns. In some embodiments, continuously block  231  looks at the date and time the recipient was last processed for needed actions (LastCheckedAt). In some embodiments, recipients are retrieved by continuously block  231  for processing based on their LastCheckedAt data and time, with the recipients with the oldest LastCheckedAt date and time being retrieved first. In some embodiments, when continuously block  231  retrieves the record of a recipient in an actively running campaign to be reviewed, that recipient’s campaign recipient record is marked so that no other continuously block  231  will retrieve the same recipient’s record. 
     In some embodiments, after an action is performed for a recipient, there is a minimum amount of time that must pass before a next action is performed for this recipient. In some embodiments, the amount of time between when an action is performed for a recipient and when the next action is performed for a recipient may be bounded by a minimum value and a maximum value. For example, AIDA system  215  may be configured such that at least one hour and not more than two and a half hours must pass between consecutive actions performed for a recipient in an active campaign. In some embodiments, the amount of time between when an action is performed for a recipient and when the next action is performed for a recipient may be randomly chosen. In some embodiments, the amount of time between when an action is performed for a recipient and when the next action is performed for a recipient may be randomly chosen within the bounds of a minimum value and a maximum value. 
     In some embodiments, after an action is performed by campaign controller  250  for a recipient of an active campaign, the LastCheckedAt data and time is set to one hour past the time when the action is performed. For example, in some embodiments, if an action is performed by campaign controller  250  of a recipient of an active campaign on January 1 st  at 7:00 a.m., the LastCheckedAt data and time is set to January 1 st  at 8:00 a.m. In some embodiments, the LastCheckedAt data and time is stored in campaign recipients storage  202  in a record for the recipient. Continuously block  231  determines which recipients are due for a next action by looking for recipients, wherein the LastCheckedAt date and time in the recipient record in the campaign recipients storage  202  is older than the present time. When the LastCheckedAt date and time in the recipient record in campaign recipients storage  202  is older than the present time, then continuously block  231  checks when the last action was sent to the recipient. In some embodiments, continuously block  231  determines when the last action was sent to the recipient by sorting the records in the one or more campaign recipient actions table(s) in descending order in which they were created, and selecting the most recent record based on the time at which that record was created, which is the LastSentAction date and time. Continuously block  231  then generates a random number representing a duration of time. In some embodiments, the random number is less than a preset maximum value for the amount of time between when an action is performed for a recipient and when the next action is performed for a recipient. Continuously block  231  adds the random number representing a duration in time to the LastSentAction date and time. If the sum of the LastSentAction data and time plus the random number presenting a duration in time is older than the current time, then continuously block  231  determines that it is time for the recipient to be sent an action. In some embodiments, continuously block  231  checks the one or more campaign recipient actions table(s) in the campaign recipient actions storage  220  periodically to see if any recipient needs to be sent an action. 
     If it is time to send a recipient a next action, then in some embodiments, campaign controller  250  moves to the next step in that recipient’s template to determine what action to perform for that recipient. In some embodiments, campaign controller  250  determines a next action to perform for that recipient based on one or more of the recipient’s responses to a previous action. In some embodiments, after campaign controller  250  puts an action to be sent to the recipient into action queue  266 , continuously block  231  updates the LastCheckedAt date and time for that recipient to the current time plus a minimum back-off time before a next action can be sent to the recipient. In some embodiments, after the message is successfully delivered to the recipient, continuously block  231  updates the LastCheckedAt date and time for that recipient to the current time plus a minimum back-off time before a next action can be sent to the recipient. 
     In some embodiments, campaign controller  250  may utilize the LastCheckedAt date and time field in the campaign recipient record for a recipient to cause the AIDA system to ignore the recipient for a period of time and not send the recipient any actions. In some embodiments, campaign controller  250  retrieves the business hours start and business hours end files from the accounts table for the account associated with the recipient. If the current time is outside of business hours, then in some embodiments continuously block  231  will set the LastCheckedAt date and time to the start of the next business day so that the user isn’t looked at by campaign controller  250  until then. In some embodiments, campaign controller  250  determines statutory or mandatory holidays based on a location or region of the recipient or the account associated with the recipient, and continuously block  231  will set the LastCheckedAt date and time to the start of the next working day after the statutory or mandatory holiday. In some embodiments, campaign controller  250  determines that the current date and time falls on a weekend, and continuously block  231  then sets the LastCheckedAt date and time to be the start of the first day after the weekend. It can be seen how the campaign controller  250  can use the LastCheckedAt date and time to insert any desired back-off duration between actions of the campaign for a recipient. 
     In some embodiments, continuously block  231  uses business logic based on one of a recipient, an account associated with the recipient, an attribute associated with the recipient, an attribute associated with the account associated with the recipient, and other information pertaining to the recipient in order to determine which recipient records to examine such that continuously block  231  does not have to look at all recipients that are in active campaigns on each review. In some embodiments, artificial intelligence based timing models will be used to determine the best timing for a next action for a given recipient in a given campaign, rather than using a random back off period. 
     In some embodiments, campaign controller  250  includes storage for campaign recipients  202 . In some embodiments, security awareness system server  280  accesses recipient records in campaign recipients storage  202  to determine all the users that are in an AIDA campaign. In some embodiments, campaign controller  250  can determine whether a user has been a recipient in an AIDA campaign in the past by determining if a recipient record for the user exists in campaign recipients storage  202 . Campaign controller  250  can determine which campaign or campaigns the user was a recipient for by reading the campaign ID in each of the recipient records for the user in campaign recipients storage  202 . In some embodiments, the data structure of the campaign recipients information stored for each campaign recipient in campaign recipient storage  202  includes one or more of a recipient ID, a campaign ID, the recipients’ user ID, the last time this recipient was processed for needed actions (LastSentAction date and time), an indication of the first next time that a recipient should be considered ready to receive a next campaign action (LastCheckedAt date and time), the recipient’s email address, and the recipient’s phone number. In some embodiments, if the user has not previously been part of an AIDA campaign, campaign controller  250  collects data including the attributes and features of the user from users storage  285 . In some embodiments, the information that campaign controller  250  collects from users storage  285  includes a user’s email address, a user’s phone number, a user’s mobile phone number, the account that the user is associated with (e.g. the company that the user is associated with), and other information that that can be accessed about the user from users storage  285 . In some embodiments, if the user has not previously been part of an AIDA campaign, then campaign controller  250  collects data including the attributes and features of the account that the user is associated with from accounts storage  284 . In some embodiments, the information that campaign controller  250  collects from accounts storage  284  includes the industry that the user’s company is in, where the company is geographically located, the company’s phish-prone percentage, and other information that can be access about the user’s company from accounts storage  284 . In some embodiments, campaign controller  250  collects and curates information about the user from one or more of the Internet, social media feeds, and reliable databases. In some embodiments, a unique record is created in campaign recipients storage  202  for a user for every different campaign and the unique record is associated with the campaign, such that there is more than one recipient record in campaign recipients storage  202  for a user. 
     In some embodiments, some of the data structure in campaign recipient storage  202  is filled in by one or more workers  260 , such as when the recipient interacts with a simulated phishing message. A worker  260  may include any type and form of executable instructions performing the functions and operations described herein. In some embodiments, the worker is a component or module of the campaign controller. In some embodiments, the worker is a set of functions, operations and instructions of the campaign controller. In some embodiments, the worker is a logical and executable construct for performing a set of assigned functions. In some embodiments, a worker  260  will record the date and time when a recipient opened an email message. In some embodiments, a worker  260  will record a date and time when a recipient interacted with any of the links in an email or a text. In some embodiments, a worker  260  will record a date and time when an email was delivered to a recipient’s email server. In some embodiments, a worker  260  will record a date and time when an email template has been processed and is waiting in the outgoing email queue  265 . In some embodiments, a worker  260  will record a date and time when an email is sent to a recipient. In some embodiments, a worker  260  will record a data and time when all templates were delivered to this recipient. In some embodiments, the recipients’ browser agent string, including one or more of a user agent, a platform, a browser, a browser version, and OS, and an IP address, will be recorded when the recipient clicks on a link in a simulated phishing message. In some embodiments, campaign recipients storage  202  is a relational database. In some embodiments, campaigns recipients storage relational database  202  has a relationship with campaign recipient actions storage  220   a  ...  220   n . 
     In some embodiments, campaign controller  250  includes a storage for template details  203 . In some embodiments, the data structure of the template details information stored for each template detail record in template details storage  203  includes one or more of a template ID, settings for a service that describes the input one or more VoIP calls, settings for one or more text or SMS messages, settings for one or more email messages, and an ordinal field which contains the order of a collection of detail records for the template. In some embodiments, the data structure of the template details information stored for each template record in template details storage  203  includes a date and time that the record was created and/or updated. In some embodiments, a template detail record in template detail storage  203  can associate a template detail page to a landing domain. 
     In some embodiments, the settings for a service that describes the input for one or more VoIP calls include a script string. The script string may include the voice script to use for a VoIP call. In some embodiments, the settings for a service that describes the input for one or more VoIP calls includes a voice type to use to speak the script on a voice call. In some embodiments, the settings for a service that describes the input for one or more VoIP calls includes a language to use for a VoIP call. In some embodiments, the settings for a service that describes the input for one or more VoIP calls includes a counter which indicates the number of times to repeat the VoIP call voice script. In some embodiments, the settings for a service that describes the input for one or more VoIP calls includes a location of an audio file to be used for a VoIP call. In some embodiments, the audio file may be an MPEG-1 audio layer 3 (MP3) file, an MPEG-1 audio layer 4 (MP4) file, a pulse-code modulation (PCM) file, a waveform audio file format (WAV) file, an audio interchange file format (AIFF) file, an advanced audio coding (AAC) file, a windows media audio (WMA) file, a free lossless audio codec (FLAC) file, an Apple lossless audio codec (ALAC) file, a Window media audio (WMA) file, or any other audio file format. In some embodiments, the audio files that may be used as an input to one or more VoIP calls are stored in VoIP content storage  241 . In some embodiments, a text to speech (TTS) engine  240  may be used to generate an audio file for one or more VoIP calls. In some embodiments, the text to be used by the TTS comes from serving module  230  of campaign controller  250 . 
     In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a string. The string may identify, contain or provide the body of the message. In some embodiments, the string comprises an identifier to file that has the body of the message. In some embodiments, the string comprises an identifier or key to a record or data in a database that has the body of the message In some embodiments, the string comprises an identifier to file that will be attached with the message. In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a string which contains the subject of the message. In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a string which indicates who or where the message is from. In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a string which indicates a reply to address for the message. In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a string which contains the name to display to indicate who or where the message is from. In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a landing page ID which indicates the landing page to use for this message. In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a landing domain ID which indicates the domain to use for a message. In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a landing domain prefix or a landing domain suffix to add before the domain or at the end of a URL and before a slug. In some embodiments, the settings for a service that describes the input for one or more SMS messages, text messages, or emails messages includes a type which indicates whether the record is for an email, an SMS or text message, or a VoIP call. 
     In some embodiments, the service that provides SMS or text messages and VoIP calls is a cloud based communications platform as a service that enables communications between mobile devices, applications, services, and systems, such as by providing a globally available cloud API. An example of a cloud communications platform as a service that can be used to provide SMS or text messages and VoIP calls is Twilio of San Francisco, California. In some embodiments, workers  260  pass to a cloud communications platform one or more of a “call from” phone number, a recipient phone number to call to, and a URL to an audio file to be played on the call. 
     In some embodiments, campaign controller  250  includes a storage for templates  204 . In some embodiments, the data structure of the template information stored for each template record in template storage  204  includes one or more of a template ID, a template name, a template category ID, an indicator of whether or not the template is archived, an indication of the level of sophistication of the template, and a date and time that the template was created and/or updated. In some embodiments, template storage  204  is a relational database. In some embodiments, template storage relational database  204  has a relationship with template details storage  203 . 
     In some embodiments, campaign controller  250  includes a storage for “call from” phone numbers  205 . In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes one or more of a phone number ID, an abbreviation for one of the state, the province, the region, the county, and the jurisdiction. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes a city name that the phone number is associated with. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes a country code associated with the phone number. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes an area code associated with the phone number. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes a list of other phone number area codes in the same area as the phone number. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes the digits of the phone number. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes an indication of whether or not the phone number can be used in an AIDA campaign. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes an indication of whether or not the phone number can send or receive text messages. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes an indication of whether or not the phone number can be used to send or receive VoIP calls. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes an indication of whether the phone number is an international number. In some embodiments, the data structure of the “call from” phone number information stored for “call from” phone number record in “call from” phone number storage  205  includes a date and time that the record was created and/or updated at. In some embodiments, the AIDA system  215  chooses a “call from” number to send a message to a recipient such that the area code of the “call from” number is the same as the area code of the recipient’s phone number. 
     In some embodiments, campaign controller  250  includes a storage for campaign groups  206 . In some embodiments, the data structure of the campaign groups information stored for each campaign group record in campaign groups storage  206  includes one or more of a campaign ID and a group ID. In some embodiments, a record in campaign group storage  206  is used to associate campaign records with group records. In some embodiments, when campaign controller  250  creates a campaign, campaign controller  250  selects one or more groups that the campaign will be sent to, which establishes a relationship between the campaign and one or more groups in groups storage  286 . In some embodiments, groups in groups storage  286  are already established and are linked to accounts. In some embodiments, one account may have multiple established groups which are stored in groups storage  286 . In one embodiment, groups in campaign groups storage  206 , together with memberships storage  287  and groups storage  287  are linked together through relational databases to establish which groups are part of an AIDA campaign, and to establish which users are part of those groups. Groups in campaign groups storage  206  are linked to a campaign ID, to a group ID, and then groups storage  286  links users to groups based on memberships storage  287  which may be a relational database. 
     In some embodiments, campaign controller  250  includes storage tagging  207  and storage for tags  208 . In some embodiments, the data structure of the tagging information stored for each tagging record in tagging storage  207  includes one or more of a record ID, a tag ID, a taggable ID, a taggable type, a tagger ID, a tagger type, a context, and a date and time that the record was created and/or updated. In some embodiments, taggings are used to categorize templates. In some embodiments, taggings in taggings storage  207  indicate an association between a tag from tag storage  208  and a template from template storage  204 . The tags and/or tagging may be any type and form of data, identifier, string, etc. to help identify, group, associate or classify certain elements or data, such as by attributes, categories, users and the like. In some embodiments tags are used to categorize templates and may be used to group templates, such as based off a model output or by customer. In some embodiments, the data structure of the tags information stored for each tags record in tags storage  208  includes one or more of a record ID, a tag name, and a taggings count. One example of a tag is “fraud reporting”. In one embodiment, there may be one or more templates related to fraud reporting, and the one of more templates related to fraud reporting are all assigned the same fraud reporting tag. Another example of a tag is “appointment reminders”. 
     In some embodiments, campaign controller  250  includes, stores and/or manages one or more campaign recipient actions table(s) in one or more campaign recipient actions storages  220   a  ...  220   n  ( 220 ). In some embodiments, the data structure of the actions information stored for each record in the one or more campaign recipient actions table(s) includes one or more of a record ID, a recipient ID, a template ID, a template detail ID, a template ordinal, a type of action, a landing domain ID, and landing page ID, a landing domain, and attachment type, an attachment filename, a sophistication level, a “reply to” address, a “from” display name, a subject, an email system message ID, and email system queue ID, one or more failure codes and one or more error messages, information about the recipient’s browser and user agent if the user clicks on a phish URL, a scheduled at date and time, a created at date and time, and an updated at date and time. In some embodiments, when a template is chosen for an AIDA campaign for a given user, the association of the template with the user for the specific AIDA campaign in stored in the campaign recipients actions table in the template ID field. In some embodiments, the one or more campaign recipient actions table(s) store(s) actions for multiple users/recipients. In some embodiments, the template and where the campaign recipient is in that template is a state that is saved by campaign controller  250  in the one or more campaign recipient actions table(s). When a message gets sent to a campaign recipient, that action gets recorded in a record in the one or more campaign recipient actions table(s). That record in the one or more campaign recipient actions table(s) is later used by campaign controller  250  to know that a step in the template has occurred and the campaign should proceed to the next step in the template. 
     In some embodiments, the campaign controller  250  queries, interfaces or uses records and/or data of the one or more campaign recipient actions table(s) to determine next action(s) to perform or take. For example, when it is time for campaign controller  250  to perform the next action for a given recipient, campaign controller  250  looks in the one or more campaign recipient actions table(s) to determine what the last action was, and then either looks in the template to determine what the next step is, or determines what the next step is using an AI model, and then campaign controller  250  sends a message to action queue  266  to trigger the next action for the campaign recipient. In some embodiments, the message that campaign controller  250  puts into action queue  266  contains one or more of a recipient ID, a template ID, and a detail ID, which is the ordinal value within the template, which refers to which detail page to use. In some embodiments, campaign controller  250  reads the detail ID from the record of the last action for the recipient, stored in the one or more campaign recipient actions table(s), in order to determine what step of the template the recipient is currently on. In some embodiments, the message that campaign controller  250  puts into action queue  266  includes the type of the message to be sent to the recipient, wherein the type is one of an email, an SMS or text message, and VoIP call, or and Internet based communication. In some embodiments, the type of the message to be sent to the recipient is determined based on the template. 
     In some embodiments, the type of action is one of email, text, call, email delivered, email delivery failed, opened, email clicked, text clicked, reporting using a user interface, error sending text, and error making VoIP call. In some embodiments, when a user clicks the phish URL, one of more of the following information is stored in the action record: user agent, platform, browser, browser version, operating system, whether or not the user is using a mobile device, whether or not the user is a bot, and an IP address. 
     In some embodiments, campaign controller  250  creates, manages and/or processes records and/or data in campaign recipient actions storage  220 . In some embodiments, when campaign controller  250  performs an action on a campaign recipient, campaign controller  250  checks to see if there exists one or more records for that recipient in the one or more campaign recipient actions table(s) in campaign recipient actions storage  220 . In some embodiments, if no record exists, then this means that this is a new recipient that has not participated in an AIDA campaign before, and campaign controller  250  creates a new campaign recipient actions table for this recipient, and/or creates a record in the one or more campaign recipient actions table(s) for the action that was performed on this recipient. 
     In some embodiments, one or more records in the one or more campaign recipient actions table(s)  220  identifies or tracks whether a campaign recipient has been part of an AIDA campaign. In some embodiments, if one or more campaign recipient actions table(s)  220  with one or more records for a campaign recipient exists, then the recipient has been in an AIDA campaign previously. In some embodiments, campaign controller  250  extracts information for that recipient from the one or more campaign recipient actions table(s)  220  in addition to extracting user attributes from users storage  285  and company attributes from accounts storage  284 , and campaign controller  250  passes this information to serving module  230 . 
     In some embodiments, campaign controller  250  maintains records of any activity, events, issues, errors, user interactions, user actions, lack of user interactions, etc. (generally referred to activity or events) that have happened to, occurred with, caused by or associated with a given user in all previous AIDA campaigns in the one or more campaign recipient actions table(s) stored in campaign recipient actions storage  220 . In some embodiments, both actions and the result of actions get stored in one or more campaign recipient actions table(s). In some embodiments, any data associated with the activity or events is stored, such as but not limited to, data about the computing device, the user, user input, any applications, programs or tasks running on the computing device. In some embodiments, if the phone number for the user was incorrect, this information gets stored in the one or more campaign recipient actions table(s). In some embodiments, if the user opens an email, this information gets stored in the one or more campaign recipient actions table(s). In some embodiments, if the user clicks on a link in a message, this information gets stored in the one or more campaign recipient actions table(s). In some embodiments, the amount of time between sending an action to a recipient and the recipient’s response to the action gets stored in the one or more campaign recipient actions table(s). In some embodiments, campaign controller  250  additionally maintains records of all non-AIDA campaigns and/or training programs that the user has completed or been exposed to in the one or more campaign recipient actions table(s) stored in campaign recipient actions storage  220 . In some embodiments, one or more of campaign controller  250 , serving module  230 , workers  260 , email workers  261 , website workers  264 , and security awareness system server  280  can access campaign recipient actions table(s) storage  220 . 
     In some embodiments, while one or more AIDA campaigns are running, campaign controller  250  collects information for all users that are recipients in an AIDA campaign and the information gets stored in the one or more campaign recipient actions table(s), in one or more campaign recipient actions storages  220   a  ...  220   n . For every campaign, there is a unique user recipient record in campaign recipients storage  202  that is linked to each new action performed on that user for the campaign. In some embodiments, there are multiple campaign recipient action records in the one or more campaign recipient actions table(s). In some embodiments, records in the one or more campaign recipient actions table(s) are linked to a single record in campaign recipients storage  202 . In some embodiments, when a user gets added to a new AIDA campaign, a new campaign recipient record in campaign recipient storage  202  will be created for the user that is only linked to the new AIDA campaign. In some embodiments, if the user has already been in an AIDA campaign, and therefore the user already has one or more records in one or more campaign recipient actions table(s), the entries of the actions for a new AIDA campaign get stored in new records in the one or more campaign recipient actions table(s), and the new records are linked to the new campaign recipient record in campaign recipient storage  202 , which is linked to the new AIDA campaign which is stored in a record in campaigns storage  201 . 
     In some embodiments, the one or more campaign recipient actions table(s) are stored in campaign recipient actions storage  220 . In some embodiments, the one or more campaign recipient actions table(s) are persistent and maintained indefinitely or until a predetermined time period. In some embodiments, the one or more campaign recipient actions table(s) is/are stored forever and does not get deleted. In some embodiments, the one or more campaign recipient actions table(s) are retroactive and only contain actions from that past that have happened, and not actions that will happen in the future. In some embodiments, user actions that are stored in one or more campaign recipient actions table(s) are also reported on the company administrator console  295  so that the company administrator know what happened. In some embodiments, the data within the campaign recipient actions tables can be used by dashboard generator  298  in company administrator console  295  to generate reports and visual data displays. 
     In some embodiments, campaign controller  250  includes one or more workers  260 . In some embodiments, workers  260  receive messages from actions queue  266  and perform the actions that the messages describe. In some embodiments, when workers  260  receive a message from actions queue  266 , if the action described in the message is to send an email to a recipient, then workers  260  put the message directly into incoming email queue  264  for one or more email workers  261  to pick up and process. 
     In some embodiments, when workers  260  receive a message from the action queue  266 , workers  260  do the task of building the message. In some embodiments, email workers  261  use the information in the message to fetch the detail page of the indicated template from email database  262 , and using user specific information from users storage  285 , email workers  261  will populate the detail page with the user specific information, and then email workers  261  will put the full composed email into cloud storage, and put the headers of the email into outgoing email queue  265 , which sends emails via two or more mail servers. In some embodiments, the cloud storage is an S3 bucket provided by Amazon Simple Storage Service (Amazon S3). In some embodiments, outgoing email queue  265  is an Amazon Simple Queue Service (SQS) queue. In some embodiments, for scaling and redundancy, there are multiple workers  260 , and the queue service (for example, Amazon SQS) posts the message from action queue  266  to an available worker  260 . In some embodiments, the queue service spreads messages from actions queue  266  evenly across multiple workers  260 . 
     In some embodiments, workers  260  determine the recipient of the message, and look up the recipient in campaign recipient storage  202  to determine the campaign the recipient is in, then workers  260  look up the campaign in campaigns storage  201 , and then workers  260  look up the one or more records in the one or more campaign recipient actions table(s) to determine the template to use, and then workers  260  look up the detail page of the template using the detail ID. 
     In some embodiments, if the detail page of the template is a text message, the worker retrieves the data required to send the test message to the recipient, for example the recipients mobile phone number, from users storage  285 , and then workers  260  build the text message and send it through the Twilio service. In some embodiments, if the detail page of the template is an email, then workers  260  forward the message exactly as they received it to incoming email queue  264  for email workers  261  so that they can build the email message for the recipient. In some embodiments, workers  260  have a template fetcher which builds the message, retrieving user specific information that is built into the email message and incorporated into the detail page of the template. 
     In some embodiments, campaign controller  250  includes one or more email workers  261 . In some embodiments, email workers  261  generate email messages. In some embodiments, email workers  261  interface with two queues; incoming email queue  264  and outgoing email queue  265 . In some embodiments, incoming email queue  264  indicates that it is time to send an email message. The message to do this action come to email workers  261  from campaign controller  250  via workers  260 . In some embodiments, the message to generate an email message contains the recipient ID, the template ID, and the detail ID. In some embodiments, email workers  261  compose the email, put the email body and the email headers together, and put the completed email address in outgoing email queue  265  until it gets processed. In some embodiments, email workers  261  only put the email header in outgoing email queue  265  until it gets processed. In some embodiments, once the email gets sent, campaign controller  250  updates the one or more campaign recipient actions table(s) to reflect that the AIDA system delivered the email to the recipient, and the email header is removed from the outgoing email queue  265 . In some embodiments, email workers  261  have an email database  262  which contains a queue table, message headers, and a transient table where the state is stored. 
     In some embodiments, campaign controller  250  includes one or more website workers  263 . In some embodiments, when a recipient opens an email, clicks on a link in an email or a text message, or otherwise interacts with the action sent to them, website worker  263  serves up the landing page from landing page storage  283  to the recipient. In some embodiments, website workers  263  present the recipient with any training that they must complete at the moment of failure. In some embodiments, when a recipient fails a simulated phishing test, website workers  263  enroll the user in remedial training that will take place at some time in the future. 
     In some embodiments, website workers  263  track one or more of the following information: which recipients interacted with a link, what browsers the recipients were using when they interacted with a link, what the recipient’s user agent was when they interacted with a link. In some embodiments, website workers  263  record the recipient’s actions in one or more campaign recipient actions table(s). In some embodiments, when campaign controller  250  wants to send a VoIP message, worker  260  that receives that message from action queue  266  and asks website worker  263  what message to send. In some embodiments, anything that the AIDA system  215  tracks and any actions taken by the recipients are automatically sent to website workers  263 . In some embodiments, if a recipient replies to a text message, the recipients response is stored by website workers  263 . In some embodiments, if a user replies to an AIDA simulated phishing message, their reply gets delivered to a special email address that is connected to an AWS Simple Notification Service (SNS). In some embodiments, the SNS sends this reply email to an AWS Lambda endpoint (AWS Lambda) which stores the reply email in an S3 bucket. When the reply email gets stored in the S3 bucket, security awareness system server  280  gets notified that there is a new email reply which creates a record in the one or more campaign recipient actions table(s) which is/are monitored by campaign controller  250 . 
     In some embodiments, landing pages are served to a recipient by website workers  263  when a user interacts with a link in a message. In some embodiments, all the information about the recipient comes back to campaign controller  250  through the URL that the recipient interacted with, as this URL is created specifically for each recipient and it has at least the recipient ID and the detail ID in it. In some embodiments, the information in the URL is encrypted. 
     D. Artificial Intelligence Models 
     Referring to  FIG.  3    in a general overview,  FIG.  3    depicts an embodiment of a system  300  used for creating, updating, and managing models, such as artificial intelligence or machine learning models, for use in AIDA simulated phishing campaigns. System  300  includes campaign controller  250 , security awareness system server  280 , system monitoring module  270 , and model manager  370 . Artificial intelligence refers to computer systems which exhibit intelligent behavior, including the capacity to learn, maintain a large storehouse of knowledge, use reasoning, apply analytic abilities, discern relationships between facts, communicate ideas to others and understand communications from others, and perceive and make sense of the situation. Machine learning systems create new knowledge by finding previously unknown patterns in data, driving solutions by learning patterns in data. 
     Neural networks are computer systems designed, constructed and configured to simulate the human nervous system. The neural network architecture consists of an input layer, which inputs data to the network; an output layer, which produces the resulting guess from the network; and a series of one or more hidden layers, which assist in propagating. Such systems learn to do tasks or make decisions by considering examples. A neural network or artificial neural network is based on a collection of connected units called neurons or artificial neurons. Each connection (synapse) between neurons can transmit a signal to another neuron. The receiving (postsynaptic) neuron can process the signal(s) and then signal downstream neurons connected to the neuron. Neurons may have state, generally represented by real numbers, typically between 0 and 1. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream. Further, neuron may have a threshold such that only if the aggregate signal is below (or above) that level is the downstream signal sent. Typically, neurons are organized in layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first (input), to the last (output) layer, possibly after traversing the layers multiple times. In artificial networks with multiple hidden layers, the initial layers might detect primitives (e.g. the pupil in an eye, the iris, eyelashes, etc..) and their output is fed forward to deeper layers who perform more abstract generalizations (e.g. eye, mouth) .... and so on until the final layers perform the complex object recognition (e.g. face). 
     Neural networks are trained with data, such as a series of data points. The networks guess which response should be given, and the guess is compared against the correct of “best” guess for each data point. If errors occur, the neurons are adjusted, and the process repeats itself. Training a neural network model corresponds to selecting one model from the set of allowed models. A model may be established by selection of a neural network configured, programed or trained in a certain way with certain data. 
     In the context of the AIDA system, neural networks may be trained with data related to simulated phishing campaigns to create or establish models that direct, identify or specify how to configure and/or execute a simulate phishing campaign. As such, the training of neural networks applies machine learning to data from and associated with results of simulated phishing campaigns to establish models for simulated phishing campaigns. A model for a simulated phishing campaign may take as input any type and form of information related to the simulated phishing campaign, such as but not limited to attributes of user, attributes of the company of the users, date and temporal information, previous actions, user history, template information, previous types of messages communicated, timing information, etc. The model may output any information for creating, executing and/or managing a simulated phishing campaign, such as but not limited to a first action to perform, a next action to perform, a persona to use, a template to use, content of the template, type of message/communication, timings of message/communications, etc. 
     The AIDA system  215  uses information related to simulated phishing communications and campaigns to develop, establish and or train models. In some embodiments, the AIDA system uses question and answer pairs and/or information learned from past simulated phishing campaigns to create models which are able to target the greatest vulnerabilities of a user. In some embodiments, AIDA system  215  can combine redacted information across multiple companies (accounts) and determine the greatest vulnerability of a specific industry, or a specific geographic region, or of a specific population demographic, or of a specific organizational level, as examples. 
     One type of artificial intelligence or machine learning model used by AIDA system  215  is a persona model. In one embodiment, persona models are stored in persona models storage,  210 . A persona model is a persona that AIDA system  215  uses to communicate with users. The persona model may be a model configured, established or trained to represent a certain type or category of person. The persona model may be a model configured, established or trained to represent a certain type of persona or personality. The persona model may be a model configured, established or trained to represent a certain type or category of job, occupation or role. In some embodiments, a persona model is a dental office assistant. In some embodiments, a persona model is a travel agent. In some embodiments, a personal model is a credit card company. In some embodiments, a persona model is a technical support representative. In some embodiments, a persona model is a technical support representative for Facebook, created by Facebook, Inc. of Menlo Park, California. 
     In one embodiment, models are created by model controller or manager  320 . In a general overview, model manager  370  includes storages for question and answer pairs (Q&amp;A pairs)  350 , storage for question and answer pairs used for training (training Q&amp;A pairs)  352 , storage for questions and answer pairs that are used for testing (testing Q&amp;A pairs)  353 , and storage for question and answer pairs that are approved (approved Q&amp;A pairs)  351 . In some embodiments, model manager  370  includes historical data exporter  301 , and Q&amp;A pairs exporter  302 . In some embodiments, model manager  370  includes model controller  320 , Q&amp;A workers  315 , and AI tool  360 . Model manager  370  may include storages for scenario descriptions  310 , storages for metagraph  361 , storages for neurons  363 , and storages for AI configuration super parameters  362 . 
     In some embodiments, model manager  370  includes worker interface  314 . The worker interface may comprise any type and form of executable instructions, such as an application, program, service, process, task or API, executable one or more processors, for interfacing and/or communications with one or more workers. The worker interface may be designed, constructed and/or configured to prompt, query, ask or request information, input or to work on a task from one or more workers. For example, the worker interface may include or provide a user interface that provides information on a queue, a task and/or status of a task. The worker interface may be designed, constructed and/or configured to receive and/or obtains information from one or more workers. For example, the worker interface may include or provide a user interface that receives information from a work, such as input, or results from or status of a task. In some implementations, the work interface is designed, constructed and/or configured to interface and/or communicate with a user, such as a user performing a task of as worker. In some implementations, the work interface is designed, constructed and/or configured to interface and/or communicate with a system, application, program, etc., that is to perform one or more tasks. For example, in some embodiments, Q&amp;A workers  315  may be a model, or may be an automated software agent. In some embodiments, worker interface  314  is a model, a program, a function, a module, an automated software agent or software instructions operating on one or more processors that interfaces with one or more person., In some embodiments, worker interface  314  organizes task queues, job queues, tasks and/or jobs. In some embodiments, worker interface  314  passes information to Q&amp;A workers  315  and/or receives information from Q&amp;A workers  315 . Worker interface  315  may invite, un-invite, select, or deselect Q&amp;A workers  315 . 
     In one embodiment, models are created by creating question and answer pairs. In some embodiments, a service such as Amazon Mechanical Turk (MTurk) is used to create question and answer pairs. In some embodiments, questions are recipient responses to messages sent to a recipient by AIDA system  215 , and answers are the AIDA system responses to the recipients responses to AIDA system messages. In some embodiments, the question and answer pairs are stored in Q&amp;A pairs storage  350 . In some embodiments, only the question and answer pairs that have not yet been validated are stored in Q&amp;A pairs storage  350 . In some embodiments, the question and answer pairs are validated by Q&amp;A workers  315  and then stored in approved Q&amp;A pairs storage  351 . In some embodiments, validated Q&amp;A pairs in approved Q&amp;A pairs storage  351  are divided into two or more groups of Q&amp;A pairs. In some embodiments, one or more group of Q&amp;A pairs is used for training models and is stored in training Q&amp;A pairs storage  352 . In some embodiments, one or more group of Q&amp;A pairs is used for testing models and is stored in testing Q&amp;A pairs storage  353 . 
     In some embodiments, Q&amp;A workers  315  are MTurk workers. In some embodiments, a model is trained by feeding it a number of approved questions which represent example AIDA system messages that a specific model could send to a recipient, along with a number of approved answers which represent appropriate responses to the approved questions. 
     In some embodiments, model controller  320  creates jobs for Q&amp;A workers  315  to develop Q&amp;A pairs. In some embodiments, the job requests give an example of the messages that campaign controller  250  sends to a user, in addition to examples of good Q&amp;A pairs. In some embodiments, model controller  320  additionally gives Q&amp;A workers  315  examples of poor Q&amp;A pairs. In some embodiments, while model controller  320  is utilizing Q&amp;A workers  315  to create Q&amp;A pairs, system monitoring module  270  maintains a dashboard of all the jobs being performed by Q&amp;A workers  315 . 
     In some embodiments, a first task given to Q&amp;A workers  315  by model controller  320  is to create Q&amp;A pairs, comprising user responses to an AIDA system message (questions) and the AIDA system responses to the user responses (answers). In some embodiments, Q&amp;A workers  315  are given a scenario description from scenario descriptions storage  310 , which includes the original AIDA system message or messages, and Q&amp;A workers  315  are first asked to create replies or questions that the recipient may ask the AIDA system, and then Q&amp;A workers  315  are asked to create an appropriate answer that the system could respond to the recipients replies or questions with. In some embodiments, model controller  320  informs Q&amp;A workers  315  that the generated recipient responses (the questions) should be representative of the way a user would respond if they received the AIDA system message that was detailed in the scenario description. 
     In some embodiments, model controller  320  informs Q&amp;A workers  315  to create system replies to recipient responses (answers) that will encourage the recipient to interact with a link in a simulated phishing message. In some embodiments, model controller  320  informs Q&amp;A workers  315  to apply criteria to creating the system responses (answers) that are created for the recipient responses (questions). In some embodiments, model controller  320  informs Q&amp;A workers  315  to use proper grammar and spelling in the system responses (answers). In some embodiments, model controller  320  informs Q&amp;A workers  315  to not use slang in the system responses (answers). In some embodiments, model controller  320  informs Q&amp;A workers  315  that the recipient responses to the AIDA system messages (the questions) may incorporate slang, spelling mistakes, profanities, typical shorthand, and urban grammar. 
     In one embodiment, a sample AIDA system email message provided by model controller  320  to Q&amp;A workers  315  is: 
     This email is to confirm a password reset was just requested for your account. If you did not request this, please visit the following link: http://secure.socialtech.com/accounts/password-reset-request/?uuid=9431edpoks&amp;language=en&amp;reset=reject Otherwise you will be locked out of your SocialTech account. Thank you, SocialTech. 
     In one embodiment, an example of an AIDA system SMS or text message provided by model controller  320  to Q&amp;A workers  315  is: 
     A password change was requested. We want to verify this is you. If you did not request a password change please click this link http://bit.ly/2hXJZd6 or you will be locked out of your SocialTech account. 
     In some embodiments, the task given to Q&amp;A workers  315  by model controller  320  is to create Q&amp;A pairs, where all the questions and answers are to be different. In one embodiment, an example of acceptable question and answer pairs are as follows:
     Question 1: I don’t have a SocialTech account
   Answer 1: Sorry, but someone requested a password reset on your account. Please click the link to verify or dispute this.   
   Question 2: When will I get locked out?
   Answer 2: You have 8 hours to click the link to verify or dispute the password reset, after which you will be locked out of your Social Tech account.   
   

     In some embodiments, the goal of the system response (answer) to the recipient response (question) is to get the recipient to interact with or click on a link, therefore answers to questions which do not move the user towards this action are not acceptable. In one example, if the system response (answer) created by Q&amp;A worker  315  is that the recipient should “Call customer service”, this response would not encourage the recipient to click on a link and therefore would not be acceptable. Similarly, system responses (answers) that are phrased in a way which would elicit further recipient responses (questions) or would encourage the recipient to disregard the system message are not acceptable. 
     In some embodiments, once Q&amp;A workers  315  have created Q&amp;A pairs which may be stored in Q&amp;A pairs storage  350 , Q&amp;A workers  315  are asked to review and validate the created Q&amp;A pairs to see if they are acceptable. In some embodiments, Q&amp;A worker  315  responds to the appropriateness of the questions and the answers with a binary reply, for example one of yes if the question or answer is acceptable and zero or no if the question or answer is not acceptable. In some embodiments, each Q&amp;A pair is reviewed by more than Q&amp;A worker  315 . In some embodiments, the Q&amp;A pairs are only considered approved if all of the Q&amp;A workers  315  that review the Q&amp;A pairs deem the questions and the answers to be acceptable. In some embodiments, the Q&amp;A pairs that are considered approved are stored in approved Q&amp;A pairs storage  351 . In some embodiments, the Q&amp;A pairs are considered approved by one or more or all of the Q&amp;A workers  315  that reviewed the Q&amp;A pairs are further reviewed by a trusted person before being stored in approved Q&amp;A pairs storage  351 . 
     In some embodiments, model manager  370  includes Q&amp;A pairs exporter  302 . In some embodiments, Q&amp;A pairs exporter  302  extracts question and answer pairs from approved Q&amp;A pairs storage  351  and creates intermediate files. In some embodiments, the one or more intermediate files are used for the input into a data prep program which separates the Q&amp;A pairs into two groups. In some embodiments, one group is used for initialization and/or training of a neural network for a model, and one group is used for testing the neural network for the model. The Q&amp;A pairs in the first group are stored in training Q&amp;A pairs storage  352 , and the Q&amp;A pairs in the second group are stored in testing Q&amp;A pairs storage  353 . In some embodiments, training Q&amp;A pairs storage  352  and testing Q&amp;A pairs storage  353  are MySQL databases which are hosted on Amazon AWS RDS. 
     In some embodiments, the Q&amp;A pairs generation and validation process is done for every model that is generated. In some embodiments, the Q&amp;A pairs generation and validation process is fully automated. In some embodiments, some portions of the Q&amp;A pairs generation and validation process are automated. In some embodiments, responses given by recipients in AIDA campaigns are used as questions for the Q&amp;A pairs generation and validation process. 
     In some embodiments, the models created by model manager  370  are stored in model storage  216 . In some embodiments, model storage  216  is part of campaign controller  250 . In some embodiments, model storage  216  is part of model manager  370 . In some embodiments, model storage  216  is stored in memory  122  as part of AIDA system  215 . In some embodiments, model storage  216  is stored in a cloud storage, for example an S3 bucket. 
     In some embodiments, the model manager  370  may create persona models that are stored in person model storage  210 . Persona models are created to represent a specific role or entity as previously described. In some embodiments, model manager  370  may create classifications models that are stored in classification model storage  209 . Classification models are variants of persona models for groups or segments that share one or more common attributes. Classification models are more tightly aligned with the group or segment. In some embodiments, classification models may be created for different industries. In some embodiments, classification models may be created for different demographics. In some embodiments, classification models may be created for different organizational levels within a company. For example, a classification model for recipients at a director level may be created. Classification models may be created for any user attribute or combination of attributes that a group of users can be built around. Q&amp;A pairs that are specific to the segment are used to train and test the classification model. In some embodiments, the questions for the questions and answer pairs are extracted from actual responses to messages sent to the recipients from campaign controller  250 , wherein the recipients are part of a group of recipients that share one or more attributes. 
     In some embodiments, once the model is trained, Q&amp;A pairs from testing Q&amp;A pairs storage  353  are used to validate the model’s behavior. In some embodiments, the answers that a trained model generates during a testing phase are reviewed by one or more Q&amp;A workers  315  to determine how appropriate they are. In some embodiments, Q&amp;A workers  315  use a ranking to represent how close the answers generated by the model are to the answers of the testing Q&amp;A pairs. In some embodiments, the answers generated by the model to the questions of the testing Q&amp;A pairs are graded on a Likert scale with 1 being the worst response and 5 being the best response. 
     In some embodiments, model controller  320  determines model parameters when creating a model using a neural network. In some embodiments, model controller  320  determines how many neurons will be in the model. In some embodiments, model controller  320  determines how many layers will be in the model. In some embodiments, model controller  320  determines one or more of an amount of backpropagation, a dimension, and a learning rate. In some embodiments, the model parameters determined by model controller  320  when creating a model are referred to as AI configuration super parameters. In some embodiments, AI configuration super parameters are part of a TensorFlow configuration. In some embodiments, AI configuration super parameters are set in Python code or as command line parameters for a python program that trains a model. In some embodiments, the AI configuration super parameters are stored in AI configuration super parameters storage  362 . In some embodiments, AI configuration super parameters are stored in a bash script format in AI configuration super parameters storage  362 . In some embodiments, AI configuration super parameters are stored in project notes or a readme file in AI configuration super parameters storage  362 . 
     In some embodiments, a model that results from a training and testing process is stored as one of integer values or real values in a matrix in model storage  216 . In some embodiments, the matrix aligns to a word matrix. In some embodiments, after the model is built, the model may be further adjusted using a tuning process that adjusts the values of the neurons. In some embodiments, the values of the neurons may be stored in neuron storage  363 . In some embodiments, the values of the neurons may be stored with the model in model storage  216 . In some embodiments, a model that results from a training and testing process further comprises a metagraph. In some embodiments, a metagraph is a list of operations to execute, and which model inputs to pass to the list of operations. In some embodiments, a metagraph is built by writing a python program that calls TensorFlow APIs to create an execution graph which is stored in memory. In some embodiments, saving an execution graph to a memory creates a metagraph. In some embodiments, a metagraph is a stored version of the in-memory execution graph and is stored in metagraph storage  361 . In some embodiments the metagraph is used to execute steps of a neural network. In some embodiments, the metagraph is stored with the model in model storage  216 . In some embodiments, serving module  230  retrieves a model from model storage  216  and a corresponding metagraph from metagraph storage  361  and makes the model and the metagraph available to campaign controller  250 . 
     In some embodiments, a model represents a persona. Models may be associated with multiple campaigns, as more than one model may be used in a campaign. Each model may have one or more versions. In some embodiments, AIDA system  215  includes a table which contains a list of all models and the versions of the models that may be used. In some embodiments, a usage counter is maintained for every version of a model, and each time the version of the model is used in an AIDA campaign, the usage counter is incremented. In some embodiments, the model version with the lowest usage count is the next model to be used by a campaign controller. In some embodiments, a security awareness system administrator  288  may set a target use percentage for one or more version of a model. In some cases, campaign controller  250  will use a version of a model for a campaign based on which version of a model is farthest below its target use percentage. 
     In some embodiments, models are created which select a preferred, or desired kind of training for a user based on recipient information and/or recipient actions when they fail a phishing campaign. In some embodiments, training models are created based on a user’s behavior in an AIDA campaign subsequent to completing specific training materials. In some embodiments, training models are created based on a user’s behavior in an AIDA campaign after the user has failed a previous simulated phishing campaign and has received training targeted towards the failure mode of the user. 
     In some embodiments, information from simulated phishing campaigns, information about users, information about accounts, and other information can be used to create new models and to update existing models. For example, one or more neural networks may be trained using results of simulated phishing campaigns, information about users of that simulated phishing campaign and through training establish one or more models. This information may, for example, highlight behavioral differences between people which may be used by the classification models to create segmentations of users into different groups based on certain attributes, wherein each group gets targeting with a specific persona model based on the likelihood that the specific persona model will increase the probability of the user interacting with a link. In some embodiments, historical information is pulled from one or more campaign recipient actions table(s) by historical data exporter  301  and formatted to be used to create a new model or update an existing model to create a new version of an existing model. 
     Models may be created for segmentations of a population, for clusters in a population, and for any group of a population. For example, a neural network may be trained with data regarding a segment to establish a model for that segment. In some embodiments, AIDA model controller  320  creates one or more models for an individual company (account). 
     In some embodiments, for a user that has not been part of an AIDA simulated phishing campaign, campaign controller  250  may redact information from users that are grouped according to similar attributes using one or more classification models (e.g. users that are in the same or similar industry, users that have similar seniority in a company, users that perform a similar role in an organization, users that have been with an organization for a similar length of time, users that are in similar geographic locations, etc.). The one or more classification models built using redacted information from users with similar attributes may be used along with personal and generic information for the new user to customize an AIDA campaign for that user, thereby creating an appropriate first AIDA campaign for a user that has no previous AIDA campaign history. 
     In some embodiments, statistical models may be used for persona models, classification models, clustering models, timing models, or any other type of model. In some embodiments, logistic regression models may be used for persona models, classification models, clustering models, timing models, or any other type of model. In some embodiments, k-means models may be used for persona models, classification models, clustering models, timing models, or any other type of model. In some embodiments, polynomial regression models may be used for persona models, classification models, clustering models, timing models, or any other type of model. In some embodiments, models may be based on deep neural networks, which can be used to create models including, for example, statistical models such as logistic regressions. In some embodiments, a deep neural network used is a sequence to sequence (seq2seq) deep neural networks model (also known as neural machine translation). 
     In some embodiments, information about a user that has interacted with a link, such as one or more of a browser the user was using when they performed the action, whether the user performed the action on their phone, a time of the action, an email client used, an IP address of the user, a browser user agent, a user’s operating system, and a browser version may be used to create models, to choose a model for a specific user, or as a feedback loop to include behavior in serving module  230  which may inform things such as a next action in a template, a next template detail page, a next timing for sending a next message, etc. 
     In some embodiments, one or more historical data exporters  301  reads data from one or more storages and creates files in the correct format needed by the model controller  320  to train new models, retrain existing models, or tune existing models. In some embodiments, one or more historical data exporter s301 reads data from campaigns storage  201 . In some embodiments, one or more historical data exporters  301  reads data from campaign recipients storage  202 . In some embodiments, one or more historical data exporters  301  reads data from campaign recipient actions storage  220 . In some embodiments, one or more historical data exporters  301  reads data from scenario descriptions storage  310 . 
     Referring to  FIG.  4    in a general overview,  FIG.  4    depicts an implementation of a method  400  for establishing a model for communicating via simulated phishing campaigns. In a brief overview, the method  400  may include establishing, via one or more Q&amp;A workers  315 , a plurality of question and answer pairs to train a model for communicating via simulated phishing campaigns (step  400 ). The method can include training, by a model trainer executing on a device, a neural network with the plurality of question and answer pairs (step  420 ). The method can include establishing, by the model training responsive to training the neural network, the model to provide a predetermined persona for simulated phishing communications (step  440 ). The method can also include storing the model to be used by a campaign controller for communicating simulated phishing communications to one or more devices of one or more users (step  460 ). 
     Referring again to  FIG.  4   , and in greater detail, the method  400  may include establishing, via one or more Q&amp;A workers  315 , a plurality of question and answer pairs to train a model for communicating via simulated phishing campaigns (step  400 ). Questions and answer pairs may be stored in the Q&amp;A pairs storage  350 . In some embodiments, the method comprises identifying, by the one or more Q&amp;A workers, one or more of the questions or answers of the plurality of question and answer pairs from communications between a campaign controller and one or more users during execution of a simulated phishing campaign. In some embodiments, the worker interface  314  identifies one or more of the questions or answers of the plurality of question and answer pairs from communications between a campaign controller and one or more users during execution of a simulated phishing campaign. In some embodiments, one or more Q&amp;A workers  315  may create one or more of the questions or answers of the plurality of the question and answer pairs. One or more Q&amp;A workers  315  may identify one or more responses from a user to a simulated phishing communication during a previous campaign, and that response may be used as a question for a question and answer pair, and one or more Q&amp;A workers  315  may create an answer for the question that is based on a previous response from a user. In some embodiments, one or more of the questions or answers of the plurality of question and answer pairs are established in accordance with a predetermined persona. In some examples, one or more Q&amp;A workers  315  may identify one or more responses from a user to a simulated phishing communication during a previous campaign that corresponds to the desired persona, and that response may be used as a question for a question and answer pair. As an example, a user may have received a simulated phishing communication in a previous campaign wherein the simulated phishing communication tried to get the user to confirm or acknowledge a dental appointment. The user may have responded to the simulated phishing communication with a reply, for example the user may have replied, “I don’t think I have an appointment.” A Q&amp;A worker  315  may use the user’s response to this simulated phishing communication as a question for a question and answer pair used to create a persona model for a dental office assistant. In some embodiments, one or more of the questions or answers of the question and answer pairs are chosen from information from past simulated phishing campaigns in order to target the greatest vulnerabilities of a user. 
     In one embodiment, model controller  320  creates jobs for Q&amp;A workers  315  to develop question and answer pairs. In some embodiments, model controller  320  communicates with worker interface  314  to manage the creation and sending of jobs to Q&amp;A workers  315 . In some embodiments, Q&amp;A workers  315  are given examples of appropriate question and answer pairs, as well as examples of inappropriate question and answer pairs. In some examples, Q&amp;A workers  315  are given a scenario description to provide context for creating question and answer pairs for a predetermined model. In some embodiments, one or more of the answers of the question and answer pairs are established to encourage the user to interact with a link in a simulated phishing message. 
     In some embodiments, the answers to the questions of the plurality of question and answer pairs are reviewed by one or more Q&amp;A workers  315  to validate that they were established in accordance with the predetermined persona. The persona model may be a model configured, established or trained to represent a certain type or category of person. The persona model may be a model configured, established or trained to represent a certain type of persona or personality. The persona model may be a model configured, established or trained to represent a certain type or category of job, occupation or role. In some embodiments, one or more Q&amp;A workers  315  of the plurality of Q&amp;A workers validate that one or more of the questions or answers of the plurality of question and answer pairs meet one or more predetermined criteria. In some examples, the Q&amp;A workers  315  validate that one or more of the questions or answers reach a level of quality of a plurality of levels of quality. In some embodiments, the Q&amp;A workers  315  may validate that one or more of the questions or answers are assigned a minimum score. In some embodiments, the Q&amp;A workers  315  respond to the appropriate of the questions and/or answers of the question and answer pairs with a binary reply, for example a “1” if the question or answer is appropriate and a “0” if the question or answer is not appropriate. In some examples, the Q&amp;A workers  315  validate that one or more of the questions or answers use correct grammar and/or have no spelling errors. In some examples, validated question and answer pairs are stored in approved Q&amp;A pairs storage  351 . The validated question and answer pairs may be divided into groups of question and answer pairs, for example one group of question and answer pairs for training a model and one group of question and answer pairs for testing a model. The question and answer pairs for testing a model may be stored in testing Q&amp;A pairs storage  353 . 
     The method  400  can include training, by a model trainer executing on a device, a neural network with the plurality of question and answer pairs (step  420 ). In some embodiments, responsive to training the neural network, a metagraph and one or more inputs to pass to a list of operations to execute the metagraph is created. In some embodiments, the settings of neurons of the neural network are adjusted responsive to processing the plurality of question and answer pairs. The questions and answer pairs for training a model may be stored in training Q&amp;A pairs storage  352 . In some embodiments, the model is adjusted via a tuning process. A neural network may also be trained without using a metagraph/execution graph. An administrator may write a software program using a non-compiled language, or an executable using a compiled language, that performs the step of training a neural network. 
     The method can include establishing, by the model trainer responsive to training the neural network, the model to provide a predetermined persona for simulated phishing communications (step  440 ). In some embodiments, one or more Q&amp;A workers  315  validate the output of the model responsive to one or more inputs to the model. In some embodiments, the model comprises the metagraph. In some embodiments, the model comprises a matrix of values corresponding to the adjusted settings of neurons of the neural network. In one embodiment, the model is further configured to provide the predetermined persona comprising one of the following: an assistant, a travel agent, a tech support representative, a credit card company representative, and a financial institution representative. In some embodiments, the model is further configured to provide the predetermined persona corresponding to one of an industry, a demographic or an organizational level in a company. 
     The method can include storing the model to be used by a campaign controller for communicating simulated phishing communications to one or more devices of one or more users (step  460 ). In some embodiments, the model is stored in models storage  216 . In some embodiments, storing the model comprises storing one or more of the question and answer pairs used to create the model, the question and answer pairs used to test the model, the metagraph, the execution graph, and the matrix of neuron settings. In some embodiments, a model that results from a training and testing process is stored as one of integer values or real values in a matrix in model storage  216 . In some embodiments, the matrix aligns to a word matrix. In some embodiments, the values of the neurons may be stored in neuron storage  363 . In some embodiments, the values of the neurons may be stored with the model in model storage  216 . 
       FIG.  5    depicts an example output of a system monitoring module monitoring the creation of one or more models. In some embodiments, the system monitors assignments for workers. In some examples, the system monitors one or more of assignments returned, assignments abandoned, assignments rejected, assignments accepted, assignments submitted, and assignments approved. In some embodiments, the system monitors one or more of SQS events received, SQS event receive errors, and SQS event receive database errors. The system monitor may monitor one or more of job canceled checks, job canceled check errors, checking for new jobs and creating job runs. In some examples, the system may monitor one or more of new hits needed, hits created, hits reviewable, review hits created, review hits reviewable, reviewables checks, reviewables check errors, review hits expired, review assignments approved, and answers submitted. In some embodiments, a system administrator may determine the time period over which to display the monitored information. In some embodiments, a system administrator may determine the refresh rate of the monitored information. 
       FIG.  6    depicts an example input screen for a company administrator console to create an AIDA campaign. In some embodiments, the AIDA campaign creation screen allows a company administrator to name a campaign. In some examples, a company administrator can set one or more of the starting time, starting date, and time zone for the campaign. In some embodiments, the company administrator to select and/or create user groups for the campaign. The company administrator may choice whether to allow text messages and allow VoIP calls as part of the new AIDA campaign. 
       FIG.  7    depicts a company administrator console dashboard showing an overview summary of an AIDA campaign generated by a dashboard generator. In some embodiments, dashboard generator  298  generates a display of the number of times a user interacts with a link in a simulated phishing message that is part of an AIDA campaign over a given time period after the start of the AIDA campaign. In some embodiments, dashboard generator  298  generates a display of the number of times a user has interacted with a link in each of the first number of time periods after the start of an AIDA campaign. In some embodiments, the time period is one hour. In some embodiments, dashboard generator  298  displays a circle with a size that is proportionate to the number of interactions with a simulated phishing message in a time period, wherein the greater the number of user interactions with links in simulated phishing messages, the larger the size of the circle that is displayed. In some embodiments, dashboard generator  298  displays the status of the AIDA campaign as one of stopped, started, paused, ongoing, discontinued, completed, finished, cancelled, restarted, or aborted. In some embodiments, dashboard generator  298  displays the date and time that an AIDA campaign was created on. In some embodiments, dashboard generator  298  displays the date an AIDA campaign was started on. In some embodiments, dashboard generator  298  displays the end date of an AIDA campaign. In some embodiments, if the campaign is one of stopped, paused, ongoing, discontinued, cancelled, restarted, or aborted, the end date is displayed as “Not Finished”. In some embodiments, the company administrator can highlight a specific recipient and see all the actions performed on that recipient (e.g. messages sent to the recipient, what detail page was used, when the message was sent, etc.) and all the actions that the recipient performed (e.g. clicked on a link in a text message, responded to an email, etc.). For example, if there is a record in the one or more campaign recipient actions table(s) indicating that the campaign controller  250  sent them an email, then the company administrator can click on this action and the company administrator console  295  displays a copy of the detail page of the template that was used to generate the email that the user received. In some embodiments, dashboard generator  298  displays information about the browser, agent or platform that the user uses to view the messages of a campaign. In some embodiments, dashboard generator  298  displays information about multiple user’s browsers, agents, or platforms in a pie chart format. 
     It should be understood that the systems described above may provide multiple ones of any or each of those components and these components may be provided on either a standalone machine or, in some embodiments, on multiple machines in a distributed system. The systems and methods described above may be implemented as a method, apparatus or article of manufacture using programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof. In addition, the systems and methods described above may be provided as one or more computer-readable programs embodied on or in one or more articles of manufacture. The term “article of manufacture” as used herein is intended to encompass code or logic accessible from and embedded in one or more computer-readable devices, firmware, programmable logic, memory devices (e.g., EEPROMs, ROMs, PROMS, RAMS, SRAMs, etc.), hardware (e.g., integrated circuit chip, Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), etc.), electronic devices, a computer readable non-volatile storage unit (e.g., CD-ROM, floppy disk, hard disk drive, etc.). The article of manufacture may be accessible from a file server providing access to the computer-readable programs via a network transmission line, wireless transmission media, signals propagating through space, radio waves, infrared signals, etc. The article of manufacture may be a flash memory card or a magnetic tape. The article of manufacture includes hardware logic as well as software or programmable code embedded in a computer readable medium that is executed by a processor. In general, the computer-readable programs may be implemented in any programming language, such as LISP, PERL, C, C++, C#, PROLOG, or in any byte code language such as JAVA. The software programs may be stored on or in one or more articles of manufacture as object code. 
     While various embodiments of the methods and systems have been described, these embodiments are illustrative and in no way limit the scope of the described methods or systems. Those having skill in the relevant art can effect changes to form and details of the described methods and systems without departing from the broadest scope of the described methods and systems. Thus, the scope of the methods and systems described herein should not be limited by any of the illustrative embodiments and should be defined in accordance with the accompanying claims and their equivalents.