Data modification system for dynamic artifact correction and secure data storage

A system for dynamic data modification and correction is provided. The system comprising: a memory device with computer-readable program code stored thereon; a communication device connected to a network; a processing device, wherein the processing device is configured to execute the computer-readable program code to: monitor a first data storage location for an artifact stored in the first data storage location, the artifact comprising unobscured private data; move the artifact to a second data storage location based on identifying the unobscured private data; generate a context rule set for the artifact based on an artifact type and one or more data fields of the artifact; modify the artifact to remove the unobscured private data based on the context rule set; and reintroduce the modified artifact to the first data storage location.

BACKGROUND

In the modern digital era, the need to store documents, files, and other data is growing exponentially. Digital storage of these artifacts is desirable, as it provides enhanced data security, ease of accessibility, reduction in carbon footprint (i.e., paperless), and scalability when compared to traditional hardcopy paper methods. For financial institutions in particular, additional and continuous measures must be taken to ensure the security of stored data. As such, there exists a need for an improved data management and security system that actively monitors a data storage location and remediates identified anomalies.

BRIEF SUMMARY

A system for dynamic data modification and correction is provided. The system comprising: a memory device with computer-readable program code stored thereon; a communication device connected to a network; a processing device, wherein the processing device is configured to execute the computer-readable program code to: monitor a first data storage location for an artifact stored in the first data storage location, the artifact comprising unobscured private data; move the artifact to a second data storage location based on identifying the unobscured private data; generate a context rule set for the artifact based on an artifact type and one or more data fields of the artifact; modify the artifact to remove the unobscured private data based on the context rule set; and reintroduce the modified artifact to the first data storage location.

In one specific embodiment, modifying the artifact comprises replacing the unobscured private data with alternative data in the artifact. In another embodiment, the alternative data comprises text or a symbol to act as a placeholder for the unobscured private data. In yet another embodiment, the alternative data is configured to function as proxy data for the unobscured private data, wherein the artifact continues to function normally using the proxy data without revealing the unobscured private data.

In yet another embodiment, the second data storage location is a quarantine storage location removed from a production environment.

In yet another embodiment, the processing device is further configured to: identify a false positive for the artifact in the second data storage location based on the context rule set; and reintroduce the artifact to the first data storage location.

In yet another embodiment, the unobscured private data is an account number, a password, financial holdings data, or personal identifying information.

A computer-implemented method for dynamic data modification and correction is also provide. The computer-implemented method comprising: monitoring a first data storage location for an artifact stored in the first data storage location, the artifact comprising unobscured private data; moving the artifact to a second data storage location based on identifying the unobscured private data; generating a context rule set for the artifact based on an artifact type and one or more data fields of the artifact; modifying the artifact to remove the unobscured private data based on the context rule set; and reintroducing the modified artifact to the first data storage location.

In one specific embodiment, modifying the artifact comprises replacing the unobscured private data with alternative data in the artifact. In another embodiment, the alternative data comprises text or a symbol to act as a placeholder for the unobscured private data. In yet another embodiment, the alternative data is configured to function as proxy data for the unobscured private data, wherein the artifact continues to function normally using the proxy data without revealing the unobscured private data.

In yet another embodiment, the second data storage location is a quarantine storage location removed from a production environment.

In yet another embodiment, the computer-implemented method of claim8further comprises: identifying a false positive for the artifact in the second data storage location based on the context rule set; and reintroducing the artifact to the first data storage location.

In yet another embodiment, the unobscured private data is an account number, a password, financial holdings data, or personal identifying information.

A computer program product for dynamic data modification and correction is also provided. The computer program product comprises a non-transitory computer-readable medium comprising computer-readable instructions, the computer-readable instructions, when executed by a processing device, cause the processing device to: monitor a first data storage location for an artifact stored in the first data storage location, the artifact comprising unobscured private data; move the artifact to a second data storage location based on identifying the unobscured private data; generate a context rule set for the artifact based on an artifact type and one or more data fields of the artifact; modify the artifact to remove the unobscured private data based on the context rule set; and reintroduce the modified artifact to the first data storage location.

In one specific embodiment, modifying the artifact comprises replacing the unobscured private data with alternative data in the artifact. In another embodiment, the alternative data comprises text or a symbol to act as a placeholder for the unobscured private data. In yet another embodiment, the alternative data is configured to function as proxy data for the unobscured private data, wherein the artifact continues to function normally using the proxy data without revealing the unobscured private data.

In yet another embodiment, the second data storage location is a quarantine storage location removed from a production environment.

In yet another embodiment, the computer-readable instructions, when executed by the processing device, further cause the processing device to: identify a false positive for the artifact in the second data storage location based on the context rule set; and reintroduce the artifact to the first data storage location.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the invention, as described herein, leverage complex, specific-use computer system to provide a novel approach for improved data storage security through active data storage scrutinization and remediation. The system of the invention is configured to actively monitor one or more data storage locations of an entity or enterprise for detected anomalies within the stored data artifacts (e.g., documents, images, files, other data). These storage locations may store private data that can include personal, private, or secure identifying data that a user may prefer to remain secure and/or private while limiting exposure. The system monitors the storage locations by deploying a bot to crawl and scan through the stored data and identify potential anomalies such as unobscured private information that may lead to data vulnerabilities. The system further comprise a document scrutinizer component configured to receive items identified by the bot and determine a context of each artifact and usage of the unobscured private data. The artifacts associated with the determined anomalies are moved out of a production environment to a separate quarantine storage location until all issues are resolved. In some cases, the system may confirm initial indications of unobscured data or misuse or alternatively identify false positives through additional processing and examination. Those artifacts quarantined by the system and confirmed as requiring remediation may then be modified by the system to remove the unobscured private data through, for example, replacement of the private data with alternative data or other data masking techniques.

In contrast, conventional data storage security systems and methods are reactive, wherein anomalies or potential of exposure of private data is only flagged after an artifact containing said data is attempted to be shared, published, or the like be it accidental or purposeful. For example, a conventional system may only identify private data in an email attachment being sent to an external third party only after a sensitive artifact has been attached and attempted to be sent. Alternatively, the present invention provides an active monitoring and continuous remediation approach to enhance data security and limit exposure possibility. The system actively scans and crawls through data storage locations to identify and remediate issues, ideally, before the files are used. In this way, the system improves data storage security and limits private data exposure by addressing a problem present in conventional storage methods.

As used herein, the term “artifact” may refer to any electronic data stored within a data storage location. Non-limiting examples of an artifact include documents, images, videos, files, and the like. In some embodiments, an artifact may include electronic records of physical documents or items such as a scanned image of a photograph or form.

As used herein, the term “computing resource” may refer to elements of one or more computing devices, networks, or the like available to be used in the execution of tasks or processes. A computing resource may be used to refer to available processing, memory, and/or network bandwidth, data usage or data transfer bandwidth or limits, and/or power of an individual computing device as well a plurality of computing devices that may operate as a collective for the execution of one or more tasks (e.g., one or more computing devices operating in unison). In some embodiments, user resources such as computing resources may include an amount of use-time during which the resources are being utilized by the system and/or other systems and devices described herein. In some embodiments, a “data storage resource” or “storage resource” may refer to computing resources to be used in the storage and/or transfer of data between data storage locations such as data storage hardware or network locations. Data storage resources may further include computer storage devices and data storage locations such as hard drives, solid state drives, storage area networks, and the like.

As used herein, the term “user” may refer to any entity or individual associated with the context-based data scrutinization and remediation system. In some embodiments, a user may be a computing device user, a phone user, a mobile device application user, a customer of an entity or business (i.e., an interaction or transaction provider), a system operator, and/or employee of an entity (e.g., a financial institution). In one embodiment, the user is an individual or customer interacting with one or more devices within the system. In another embodiment, a user may be a system administrator, technician, software or application developer, computer engineer, support associate, or the like associated with the context-based data scrutinization and remediation system and/or a data storage location utilized by the system. In some embodiments, identities of an individual may include online handles, usernames, identification numbers (e.g., Internet protocol (IP) addresses), aliases, family names, maiden names, nicknames, or the like. In some embodiments, the user may be an individual or an organization (i.e., a charity, business, company, governing body, or the like).

As used herein the term “user device” may refer to any device that employs a processor and memory and can perform computing functions, such as a personal computer or a mobile device, wherein a mobile device is any mobile communication device, such as a cellular telecommunications device (i.e., a cell phone or mobile phone), a mobile Internet accessing device, or other mobile device. Other types of mobile devices may include laptop computers, tablet computers, wearable devices, cameras, video recorders, audio/video player, ATMs, global positioning system (GPS) devices, gaming entertainment devices, or any combination of the aforementioned. The device may be used by the user to access the system directly or through one or more applications, an online portal, internet browser, virtual private network, or other connection channel. The user device comprises a display for providing a user interface to the user for interacting with an application displayed on the device and presented to the user. In some embodiments, a user device may comprise a clock/timer or counter, camera, location sensor (e.g., GPS), or the like.

As used herein, the term “entity” may be used to include any organization or collection of users that may interact with the context-based data scrutinization and remediation system. An entity may refer to a business, company, or other organization that either maintains or operates the system or requests use and accesses the system. In one embodiment, an entity may include a data storage provider. In one embodiment, the entity may be a business or financial entity. The terms “financial institution” and “financial entity” may be used to include any organization that processes financial transactions including, but not limited to, banks, resource management firms, insurance companies and the like. In specific embodiments of the invention, use of the term “bank” is limited to a financial entity in which account-bearing customers conduct financial transactions, such as account deposits, withdrawals, transfers and the like. In other embodiments, an entity may be a business, organization, a government organization or the like that is not a financial institution. In some embodiment, an entity may be a business, vendor, or the like that provides a resource (e.g., goods, services, or other resources) to another user or entity via an interaction at an interaction location (e.g., within an application, a web site, brick-and-mortar location).

As used herein, “authentication information” may refer to any information that can be used to authenticate an identify a user. For example, a system may prompt a user to enter authentication information such as a username, a password, a personal identification number (PIN), a passcode, biometric information (e.g., voice authentication, a fingerprint, and/or a retina scan), an answer to a security question, a unique intrinsic user activity, such as making a predefined motion with a user device. This authentication information may be used to at least partially authenticate the identity of the user (e.g., determine that the authentication information is associated with the account) and determine that the user has authority to access an account or system. In some embodiments, the system may be owned or operated by an entity. In such embodiments, the entity may employ additional computer systems, such as authentication servers, to validate and certify resources inputted by the plurality of users within the system. In some embodiments, authentication information may refer to any information provided by a user, entity, or the like to verify an identify and/or status of the user, entity, data associated with a user or entity, or the like.

To “monitor” is to watch, observe, or check something for a special purpose over a period of time. The “monitoring” may occur periodically over the period of time, or the monitoring may occur continuously over the period of time. In some embodiments, a system may actively monitor a data source, data stream, database, data archive, data storage location, or other data structure, wherein the system reaches out to the database and watches, observes, or checks the database for changes, updates, and the like. In other embodiments, a system may passively monitor a database or data stream, wherein the database or data stream provides information to the system and the system then watches, observes, or checks the provided information. In some embodiments, “monitoring” may further comprise analyzing or performing a process on something such as a data source or data stream either passively or in response to an action or change in the data source or data stream. In a specific embodiment, monitoring may comprise scanning data artifacts stored in a data storage location. Monitoring of user data streams is performed by the system with approval or authorization by the monitored user device and user.

As used herein, an “interaction” may refer to any action or communication between one or more users, one or more entities or institutions, and/or one or more devices or systems within the system environment described herein. For example, an interaction may refer to a user interaction with a system or device, wherein the user interacts with the system or device in a particular way. In one embodiment, interactions may be received or extracted through use interaction with a data stream (e.g., in real-time). In some embodiments, an interaction comprises a data transfer between devices or locations within the environment of the system described herein. For example, an interaction may require a transfer or migration of data between data storage locations.

In some embodiments, an interaction may further include user interactions with a user interface of a user application (e.g., clicking, swiping, text or data entry, etc.), authentication actions (e.g., signing-in, username and password entry, PIN entry, etc.), account actions or events (e.g., account access, fund transfers, document or record views and/or transfers, etc.) and the like. In another example, an interaction may refer to a user communication via one or more channels (i.e., phone, email, text, instant messaging, brick-and-mortar interaction, and the like) with an entity and/or entity system to complete an operation or perform an action. In yet another embodiment, an interaction may include any change in a data structure and/or data storage location.

FIG.1provides a context-based data scrutinization and remediation system environment100, in accordance with one embodiment of the invention. As illustrated inFIG.1, context-based data scrutinization and remediation system130is operatively coupled, via a network101, to the user device(s)110(e.g., a plurality of user devices110a-110d), the entity system(s)120, and third party systems140. In this way, the context-based data scrutinization and remediation system130can send information to and receive information from the user device110, the entity system120, and the third party systems140. In the illustrated embodiment, the plurality of user devices110a-110dprovide a plurality of communication channels through which the entity system120and/or the context-based data scrutinization and remediation system130may communicate with the user102over the network101.

FIG.1illustrates only one example of an embodiment of the system environment100. It will be appreciated that in other embodiments, one or more of the systems, devices, or servers may be combined into a single system, device, or server, or be made up of multiple systems, devices, or servers. It should be understood that the servers, systems, and devices described herein illustrate one embodiment of the invention. It is further understood that one or more of the servers, systems, and devices can be combined in other embodiments and still function in the same or similar way as the embodiments described herein.

The network101may be a system specific distributive network receiving and distributing specific network feeds and identifying specific network associated triggers. The network101may also be a global area network (GAN), such as the Internet, a wide area network (WAN), a local area network (LAN), or any other type of network or combination of networks. The network101may provide for wireline, wireless, or a combination wireline and wireless communication between devices on the network101.

In some embodiments, the user102is an individual interacting with one or more user devices either actively or passively, wherein the user devices are configured to interact and transmit commands to the context-based data scrutinization and remediation system130. In one embodiment, the data stream may be generated or modified, for example, by one or more entity systems120while the data stream between the user device110and the entity system120is monitored by the context-based data scrutinization and remediation system130over the network101. In an alternative embodiment, the user102is an individual interacting with the context-based data scrutinization and remediation system130over the network101and monitoring a transfer of data between the user devices110and the entity systems120using the context-based data scrutinization and remediation system130(e.g., an employee of the entity operating and/or monitoring the systems120,130).

FIG.2provides a block diagram of a user device110, in accordance with one embodiment of the invention. The user device110may generally include a processing device or processor202communicably coupled to devices such as, a memory device234, user output devices218(e.g., a user display device220, or a speaker222), user input devices214(e.g., a microphone, keypad, touchpad, touch screen, and the like), a communication device or network interface device224, a power source244, a clock or other timer246, a visual capture device such as a camera216, a positioning system device242, and the like. In one embodiment, the camera216may include a scanner or any other image capturing device. The processing device202may further include a central processing unit204, input/output (I/O) port controllers206, a graphics controller or graphics processing device (GPU)208, a serial bus controller210and a memory and local bus controller212.

The processing device202may include functionality to operate one or more software programs or applications, which may be stored in the memory device234. For example, the processing device202may be capable of operating applications such as the user application238. The user application238may then allow the user device110to transmit and receive data and instructions from the other devices and systems of the environment100. The user device110comprises computer-readable instructions236and data storage240stored in the memory device234, which in one embodiment includes the computer-readable instructions236of a user application238. In some embodiments, the user application238allows a user102to access and/or interact with other systems such as the entity system120via a user interface. In some embodiments, the user application238is an application for interacting with other systems and devices over a network.

The processing device202may be configured to use the communication device224to communicate with one or more other devices on a network101such as, but not limited to the entity system120and the context-based data scrutinization and remediation system130. In this regard, the communication device224may include an antenna226operatively coupled to a transmitter228and a receiver230(together a “transceiver”), modem232. The processing device202may be configured to provide signals to and receive signals from the transmitter228and receiver230, respectively. The signals may include signaling information in accordance with the air interface standard of the applicable BLE standard, cellular system of the wireless telephone network and the like, that may be part of the network101. In this regard, the user device110may be configured to operate with one or more air interface standards, communication protocols, modulation types, and access types. By way of illustration, the user device110may be configured to operate in accordance with any of a number of first, second, third, fourth, and/or fifth-generation communication protocols and/or the like. For example, the user device110may be configured to operate in accordance with second-generation (2G) wireless communication protocols IS-136 (time division multiple access (TDMA)), GSM (global system for mobile communication), and/or IS-95 (code division multiple access (CDMA)), or with third-generation (3G) wireless communication protocols, such as Universal Mobile Telecommunications System (UMTS), CDMA2000, wideband CDMA (WCDMA) and/or time division-synchronous CDMA (TD-SCDMA), with fourth-generation (4G) wireless communication protocols, with fifth-generation (5G) wireless communication protocols, and/or the like. The user device110may also be configured to operate in accordance with non-cellular communication mechanisms, such as via a wireless local area network (WLAN) or other communication/data networks. The user device110may also be configured to operate in accordance Bluetooth® low energy, audio frequency, ultrasound frequency, or other communication/data networks.

The user device110may also include a memory buffer, cache memory or temporary memory device operatively coupled to the processing device202. Typically, the one or more applications238, are loaded into the temporarily memory during use. As used herein, memory may include any computer readable medium configured to store data, code, or other information. The memory device234may include volatile memory, such as volatile Random Access Memory (RAM) including a cache area for the temporary storage of data. The memory device234may also include non-volatile memory, which can be embedded and/or may be removable. The non-volatile memory may additionally or alternatively include an electrically erasable programmable read-only memory (EEPROM), flash memory or the like.

FIG.3provides a block diagram of a context-based data scrutinization and remediation system130, in accordance with one embodiment of the invention. The context-based data scrutinization and remediation system130generally comprises a controller301, a communication device302, a processing device304, and a memory device306.

As used herein, the term “controller” generally refers to a hardware device and/or software program that controls and manages the various systems described herein such as the user device110, the entity system120, third party systems140, and/or the context-based data scrutinization and remediation system130, in order to interface and manage data flow between devices and systems while executing commands to control the systems. In some embodiments, the controller may be integrated into one or more of the systems described herein. In other embodiments, the controller may be a separate system or device. In some embodiments, the controller may perform one or more of the processes, actions, or commands described herein.

As used herein, the term “processing device” or “processor” generally includes circuitry used for implementing the communication and/or logic functions of the particular system. For example, a processing device may include a digital signal processor device, a microprocessor device, and various analog-to-digital converters, digital-to-analog converters, and other support circuits and/or combinations of the foregoing. Control and signal processing functions of the system are allocated between these processing devices according to their respective capabilities. The processing device may include functionality to operate one or more software programs based on computer-readable instructions thereof, which may be stored in a memory device.

The processing device304is operatively coupled to the communication device302and the memory device306. The processing device304uses the communication device302to communicate with the network101and other devices on the network101, such as, but not limited to the user device110and the entity system120. As such, the communication device302generally comprises a modem, server, or other device for communicating with other devices on the network101.

As further illustrated inFIG.3, the context-based data scrutinization and remediation system130comprises computer-readable instructions310stored in the memory device306, which in one embodiment includes the computer-readable instructions310of an context-based scrutinization application312, a scanner or crawler bot320, remediation application322, and a machine learning engine324.

The context-based scrutinization application312is configured to analyze the contents of monitored data storage locations and, specifically, those artifacts scanned and identified by the system as containing potentially sensitive information such as unobscured private data. The context-based scrutinization application312may be further configured to identify a usage or context of the artifact and any information within the artifact. The scanner or crawler bot320is configured to be deployed within a data storage location and scan the one or more artifacts or data contents of the location. The scanner or crawler bot320is configured to identify potentially sensitive data or other anomalies with the data storage location and transmit any flagged items for the context-based scrutinization application312. The remediation application322is configured to modify the flagged artifacts containing unobscured private data or other anomalies. The remediation application322is configured to modify the artifacts to remove or alter any unwanted data in the artifacts before reintroducing the artifacts back to the data storage location. The machine learning engine324is configured to work with the context-based scrutinization application312and the scanner or crawler bot320to learn to identify private data and other anomalies in data artifacts as well as artifacts types and usage (i.e., context of information within a particular artifact).

In some embodiments, the memory device306includes data storage308for storing data related to the system environment, but not limited to data created and/or used by the context-based scrutinization application312, scanner or crawler bot320, remediation application322, and machine learning engine324. Data stored in the data storage308may comprise an entity/enterprise data storage314(i.e., data storage locations), internal training data316, and quarantine storage318.

In one embodiment of the invention, the context-based data scrutinization and remediation system130may associate with applications having computer-executable program code that instruct the processing device304to perform certain functions described herein. In one embodiment, the computer-executable program code of an application associated with the user device110, third party systems140, and/or the entity systems120may also instruct the processing device304to perform certain logic, data processing, and data storing functions of the application.

Embodiments of the context-based data scrutinization and remediation system130may include multiple systems, servers, computers or the like maintained by one or many entities. In some embodiments, the context-based data scrutinization and remediation system130may be part of the entity systems120. In other embodiments, the entity systems120are distinct from the context-based data scrutinization and remediation system130. The context-based data scrutinization and remediation system130may communicate with the entity systems120via a secure connection generated for secure encrypted communications between the two systems either over the network101or alternative to the network101.

As illustrated in detail inFIG.4, the environment100further includes one or more entity systems120which are connected to the user device110, the context-based data scrutinization and remediation system130, and the third party systems140. The entity systems120may be associated with one or more entities, institutions, or the like. The entity systems120generally comprise a communication device402, a processing device404, and a memory device406further comprising data storage408. The entity systems120comprise computer-readable instructions410stored in the memory device406, which in one embodiment includes the computer-readable instructions of an entity application412. The entity systems120may communicate with the user device110, third party systems140, and the context-based data scrutinization and remediation system130to provide access to information and accounts stored and maintained on the entity systems120. In some embodiments, the entity system120may communicate with the context-based data scrutinization and remediation system130during an interaction with a user102in real-time, wherein user interactions may be monitored and processed by the context-based data scrutinization and remediation system130. In some embodiments, data storage408comprises a entity/enterprise data storage416(i.e., data storage location) to either supplement or replace data storage of the context-based data scrutinization and remediation system130as previously discussed.

The systems of the environment100are configured to actively monitor one or more data storage locations of an entity or enterprise for detected anomalies within the stored data artifacts (e.g., documents, images, files, other data). These storage locations may store private data that can include personal, private, or secure identifying data that a user may prefer to remain secure and/or private while limiting exposure. The system monitors the storage locations by deploying a bot to crawl and scan through the stored data and identify potential anomalies such as unobscured private information that may lead to data vulnerabilities. The system further comprise a document scrutinizer component configured to receive items identified by the bot and determine a context of each artifact and usage of the unobscured private data. The artifacts associated with the determined anomalies are moved out of a production environment to a separate quarantine storage location until all issues are resolved. In some cases, the system may confirm initial indications of unobscured data or misuse or alternatively identify false positives through additional processing and examination. Those artifacts quarantined by the system and confirmed as requiring remediation may then be modified by the system to remove the unobscured private data through, for example, replacement of the private data with alternative data or other data masking techniques.

FIG.5provides an illustration of data flow within a context-based data scrutinization and remediation system, in accordance with one embodiment of the invention. The system ofFIG.5comprises an existing enterprise artifact storage502. In the exemplary embodiment of the figure, the artifact storage502is an electronic document storage location. In some embodiments, a data storage location may comprise private and/or public databases of one or more entities. In the non-limiting example ofFIG.5, the existing enterprise artifact storage502comprises discovery data504, cloud storage506, and shared folders508but may further include other data storage locations such as hard drives, servers, data banks, removable storage devices, network storage locations, and the like.

The system comprises a scanner or crawler bot510configured to be deployed into the existing enterprise artifact storage502and scan the one or more artifacts or data contents of the location. The scanner or crawler bot510is configured to identify or flag potentially sensitive data or other anomalies within artifacts stored in the data storage location and transmit flagged items for additional processing. In one embodiment the scanner or crawler bot510is a computer program configured to automatically search documents or other data within a collection of a data storage location. The scanner or crawler bot510identifies information from scanned artifacts, such as one or more data fields, and may initially categorize, index, and catalogue the artifacts for additional processing by the system. In some embodiments, once deployed within a data storage location the scanner or crawler bot510is configured to automatically and continuously crawl through and scan the contents of the data storage location even as the location is updated. In this way, the system may continuously monitor a data storage location and proactively identify potentially vulnerable information before it can be improperly used. In some embodiments, the scanner or crawler bot510is initially trained using internal training data512comprising previously identified artifacts, artifact types, and data entries. In some embodiments, the scanner or crawler bot510may further be trained using real-time data and output from a machine learning engine or algorithm526configured for identifying artifacts.

The system further comprises a document scrutinizer component514configured to receive artifacts initially identified by the scanner or crawler bot510and is further configured to analyze the contents of monitored data storage locations and, specifically, those artifacts scanned and identified by the system as containing potentially sensitive information such as unobscured private data. The document scrutinizer514may be further configured to identify a usage or context of the artifact and any information within the artifact. The document scrutinizer514comprises a context identifier component516configured to analyze an artifact, an assigned artifact type, one or more data entries of the artifact, and/or historical artifact information to determine a context or usage of the artifact and/or any contained data such as sensitive or private information. In some embodiments, a machine learning engine such as machine learning engine or algorithm526may be in communication with document scrutinizer514and be trained for identifying artifact type and data entry context.

In some embodiment, the document scrutinizer514is configured to approve of an artifact based on the artifact context and usage or flag an artifact, wherein approved artifacts are transmitted back to an original data storage location and a flagged artifact is transmitted to a alarm or quarantine folder518. In some embodiments, the alarm or quarantine folder518is a separate data storage location, wherein flagged artifacts may be temporarily removed from the existing enterprise artifact storage502. In one embodiment, the existing enterprise artifact storage502is a production environment and the alarm or quarantine folder518is a non-production environment. In some embodiments, the system is configured confirm a quarantine of an artifact in the alarm or quarantine folder518. In one embodiment, the presence of a false positive may be identified by a user or entity maintaining the system, wherein false positive processing524of an artifact may reintroduce the artifact back to the existing enterprise artifact storage502and the model contained in the machine learning engine or algorithm526is updated to reflect and correct for the newly identified false positive.

Alternatively, the system is configured to process flagged artifacts contained in the alarm or quarantine folder518using remediator component520. The remediator component520is configured to modify the artifacts to remove or alter any unwanted data in the artifacts before reintroducing the artifacts back to the data storage location. In the non-limiting embodiment ofFIG.5, the remediator component520comprises a data scrubber configured for removing sensitive or unwanted data from the artifact. In some embodiments, remediated or modified artifacts may be reintroduced to the existing enterprise artifact storage502at the and original or new data storage location (e.g., moved to a new subfolder)

FIG.6provides a high level process flow for data storage scrutinization and capture, in accordance with one embodiment of the invention. In some embodiments, the process flow ofFIG.6is executed or performed by the system environments of the previous figures. As illustrated at block610ofFIG.6, the system is configured to first monitor a data storage location using a crawler bot configured for scanning an artifact stored in the data storage location. As previously discussed, a crawler bot is a computer program configured to automatically search documents or other data within a collection of a data storage location. The crawler bot is configured to identify information from scanned artifacts, such as one or more data fields, and may initially categorize, index, and catalogue the artifacts for additional processing by the system. In some embodiments, once deployed within a data storage location the crawler bot is configured to automatically and continuously crawl through and scan the contents of the data storage location even as the location is updated. In this way, the system may continuously monitor a data storage location and proactively identify potentially vulnerable information before it can be improperly used. In some embodiments, crawler bot is initially trained using internal training data comprising previously identified artifacts, artifact types, and data entries. In some embodiments, the crawler bot may further be trained using real-time data and/or output from a machine learning engine or algorithm configured for identifying artifacts.

At block620, the system is configured to scan the artifact, using the crawler bot, for one or more data fields. The system is configured to scan the one or more data fields of the artifact to identify unobscured private data or other anomalies (e.g., misspellings, errors, flagged words, etc.). The unobscured data may comprise, for example, personal, private, or secure identifying data that a user may prefer to remain secure and/or private while limiting exposure of said data. For example, unobscured private data may include a user's social security number, account numbers, financial account holdings values, addresses, usernames, passwords, and the like. In some embodiments the system leverages optical character recognition (OCR) technology to scan the artifact for unobscured private data and/or other anomalies. In one embodiment, the system compares scanned data fields to a known data field data base defining a number of actions for particular data fields, wherein upon the system identifying a particular word, the corresponding action is executed (e.g., moved to quarantine).

At block630, the system is configured to identify an artifact type for the artifact based on the one or more data fields. Non-limiting examples of artifact types include internal documents, published documents, document types (e.g., a check, a bank statement, a screenshot), file types (e.g., an image, a video, an email attachment). In one specific example, the determined presence of an account number, a routing number, a signature, and a dollar amount may indicate that the artifact is a check. An artifact type defines a presumed end use of an artifact by a user or other systems and determine, in part, whether the presence of private data is unwarranted. For example, a user account number on a private bank statement may be permittable use of private data, while inclusion of the account number in a screenshot email attachment designated to be sent to a third party may not be desirable as it may exposure the private data.

In this way, the system defines a context, end use, or usage of the data within the artifact. In some embodiments, the system is configured to assign a preexisting a context rule set or generate a new context rule set for an artifact based on the scanned data fields of the artifact, the artifact contents, the artifact type, and/or the artifact data storage location. The system may identify based on previously processed data that an artifact is a particular artifact type and apply a particular rule set governing processing and exposure of private data within the artifact type. For example, upon identifying an artifact as a check artifact type, the system may apply a check artifact rule set for removing sensitive information such as an account number, amount, or the like.

In some embodiments, the system is configured to scan an artifact and identify subsets of data within an artifact (e.g., a combination of public data and private data). The system may be configured to divide the data fields of the artifact into individual artifact components requiring different levels of scrutiny and processing based on the type of data contained in the corresponding data fields.

At block640, the system is configured to capture or extract the artifact from an original data storage location based on determining the presence of unobscured private data that is not permittable or desirable for the artifact type. In some embodiments, capturing the artifact comprises removing the artifact from the original data storage location and moving the artifact to a new data storage location. The system may determine a new data storage location for the artifact based on the artifact type, the one or more data fields, and the context rule set for the artifact and move the artifact to the new location. For example, the system may determine that an original data storage location for a particular artifact was improper or an error, wherein the system may move the artifact to a new data storage location for that artifact type.

In some embodiments, the system may only temporarily remove an artifact from an original data storage location. In one embodiment, the system may temporarily remove the artifact from the original data storage location and place the artifact in a quarantine data storage location for additional processing or remediation. In some embodiments, a quarantine data storage location is a non-production storage environment.

FIG.7provides a high level process flow for data storage identification and remediation, in accordance with one embodiment of the invention. As illustrated at block710ofFIG.7, the system is configured to monitor one or more data storage locations. In particular, the system monitors the data storage locations for stored artifacts comprising unobscured private data. The system may monitor and scan the data storage location using a crawler bot and data context identification as described with respect toFIG.6. Similarly, as seen at block720, the system is configured to move an artifact to a new data storage location, such as a quarantine storage location, based on identifying the unobscured private data. The system is also configured to generate a context rule set for the artifact based on an artifact type and one or more data fields of the artifact to determine the context or usage of the private data within the artifact and to determine whether additional processing or action is required.

Based on determining that additional action is required to remove unobscured private data from an artifact based on the context rule set, the system is configured to modify the artifact at block740. In some embodiments, the system is configured to modify the artifact by removing or scrubbing the unobscured private data based on the requirements of the context rule set for the artifact type. In another embodiment, the system is configured to apply a mask to the artifact, wherein at least a portion of the artifact is modified with additional or alternative data. The masked data may be provided to the destination device for meeting the data requirements, while maintaining data anonymity. The masked data may remain usable by the system for directing the resource transfer while masking at least those portions not required by the data requirements. Non-limiting examples of alternative data include text (e.g., XXXX), symbols (e.g., *****), or the like to act as a placeholder for the unobscured private data without exposing the data itself. In some embodiments, the alternative data may be configured to function as proxy data for the unobscured private data, wherein the alternative proxy data enables the artifact to continue to function normally without revealing the unobscured private data. In yet another embodiment, the system may further comprises an encryption function configured to encrypt at least a portion of the artifact. At block750, the system may be configured to reintroduce the modified artifact to an original or new data storage location following modification.

In some embodiments, the system may mistakenly flag an artifact for quarantine or additional processing (i.e., a false positive). In one embodiment, the system may automatically identify a false positive based on an assigned context rule set. In another embodiment, the presence of a false positive may be identified by a user or entity maintaining the system. The system may be configured to reintroduce an artifact back to an original data storage location following confirmation of a false positive. Furthermore, a machine learning model used to initially flag the artifact may be updated to correct for and reflect the newly identified false positive. In this way, the system may continually learn to accurately identify artifacts and anomalies.

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