Patent Publication Number: US-2023164174-A1

Title: Techniques for lateral movement detecton in a cloud computing environment

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation in part of U.S. Non-Provisional patent application Ser. No. 18/055,180 filed Nov. 14, 2022, which claims the benefit of U.S. Provisional Application No. 63/264,550 filed on Nov. 24, 2021, U.S. Provisional Application No. 63/283,376 filed on Nov. 26, 2021, U.S. Provisional Application No. 63/283,378 filed on Nov. 26, 2021, and U.S. Provisional Application No. 63/283,379 filed on Nov. 26, 2021, the contents of which are hereby incorporated by reference. All of the applications referenced above are hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to cybersecurity and, in particular, to improved scanning of virtual instances utilizing infrastructure as code. 
     BACKGROUND 
     As users migrate data storage, processing, and management tasks to decentralized, off-location devices, platforms, and services, the limitations of such devices, platforms, and services, also referred to as cloud environments, platforms, and the like, may impact a user&#39;s data operations. Specifically, vulnerabilities within cloud-deployed resources and processes may present unique challenges requiring remediation. Due to the scale and structure of cloud systems, detection of workload vulnerabilities, which detection may be readily-provided in non-cloud deployments, may require numerous, complex tools and operations. 
     Current solutions to cloud workload vulnerability scanning challenges require the deployment of specialized tools, including scanning agents directed to maintenance of virtual machines (VMs), where operation and maintenance of such tools may be costly, time-consuming, or both. Agent-dependent processes fail to provide for scanning of containers, such as containers managed using Kubernetes®, and other, like, container-management platforms, and may fail to provide for coverage of serverless applications. Where such agent-implementation processes fail to provide for full cloud workload vulnerability scanning, additional methods, such as snapshot-based scanning, may supplement implemented solutions. 
     Snapshot-based scanning, wherein static “snapshots” of processes, services, data, and the like, are analyzed in an environment separate from the source environment, provides for agentless scanning. Snapshot-based scanning is applied in various fields, including computer forensics, to provide for analysis of services, processes, data, and the like, in locations or environments other than those from which the snapshots are collected, as well as retrospective analysis. However, the applicability of snapshot-based scanning is limited in multi-tenant systems, such as shared cloud platforms, as cloud tenants may desire high levels of data protection during snapshot generation, transfer, and analysis. Further, snapshot-based scanning methods, as well as hybrid methods including both agent-implemented and snapshot-based methods, may be inapplicable to certain cloud system structures and environments, which may include various objects, processes, and the like, which such methods may not be configured to process, as such processing may require, as examples, separate analysis of container repositories, VM snapshots, and application programming interfaces (API) for serverless applications, where existing solutions fail to provide such integrated functionality. 
     Further complicating matters is deployment of cloud environments utilizing infrastructure as code (IaC) systems. While aimed at decreasing human error when deploying cloud environments, there is often a drift from the original configuration code to the current state of the production environment. A complication may arise due, for example, to different teams working on the development environment (configuration code) and the production environment (deployed instances). Current tools such as Checkov and Accurics allow to scan for misconfigurations and policy violations, but are limited to scanning only configuration code. Cl/CD (continuous integration/continuous deployment) and drifting configurations mean that scanning the configuration code is not always enough to get a precise understanding of where threats and vulnerabilities currently exist, since this is a moving target. 
     It is apparent that it would be advantageous to provide a solution which can scan for vulnerabilities in an improved and efficient manner. 
     Furthermore, it would, therefore, be advantageous to provide a solution that would overcome the challenges noted above. 
     SUMMARY 
     A summary of several example embodiments of the disclosure follows. This summary is provided for the convenience of the reader to provide a basic understanding of such embodiments and does not wholly define the breadth of the disclosure. This summary is not an extensive overview of all contemplated embodiments, and is intended to neither identify key or critical elements of all embodiments nor to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description that is presented later. For convenience, the term “some embodiments” or “certain embodiments” may be used herein to refer to a single embodiment or multiple embodiments of the disclosure. 
     Certain embodiments disclosed herein include a method for detecting lateral movement in a cloud computing environment based on configuration code. The method also includes accessing a configuration code, the configuration code including a plurality of code objects, where a code object of the plurality of code objects corresponds to a cloud entity deployed in the cloud computing environment; selecting an identifier of an exposed cloud entity, the cloud entity associated with a secret; querying a security graph based on the identifier to detect a node representing the secret, where the node representing the secret is connected to a node representing the exposed cloud entity; traversing the security graph to detect a second node connected to the node representing the secret, the second node representing a second cloud entity deployed based on the code object of the plurality of code objects; and generating a mitigation action based on the second cloud entity. 
     Certain embodiments disclosed herein also include a non-transitory computer readable medium having stored thereon causing a processing circuitry to execute a process, the process comprising: accessing a configuration code, the configuration code including a plurality of code objects, where a code object of the plurality of code objects corresponds to a cloud entity deployed in the cloud computing environment; selecting an identifier of an exposed cloud entity, the cloud entity associated with a secret; querying a security graph based on the identifier to detect a node representing the secret, where the node representing the secret is connected to a node representing the exposed cloud entity; traversing the security graph to detect a second node connected to the node representing the secret, the second node representing a second cloud entity deployed based on the code object of the plurality of code objects; and generating a mitigation action based on the second cloud entity. 
     Certain embodiments disclosed herein also include a system for detecting a vulnerable code object in configuration code for deploying instances in a cloud computing environment. The system comprises: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: access a configuration code, the configuration code including a plurality of code objects, where a code object of the plurality of code objects corresponds to a cloud entity deployed in the cloud computing environment; select an identifier of an exposed cloud entity, the cloud entity associated with a secret; query a security graph based on the identifier to detect a node representing the secret, where the node representing the secret is connected to a node representing the exposed cloud entity; traverse the security graph to detect a second node connected to the node representing the secret, the second node representing a second cloud entity deployed based on the code object of the plurality of code objects; and generate a mitigation action based on the second cloud entity. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The subject matter disclosed herein is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the disclosed embodiments will be apparent from the following detailed description taken in conjunction with the accompanying drawings. 
         FIG.  1    is a network diagram of a monitored cloud computing environment utilizing infrastructure as code (IaC) utilized to describe the various embodiments. 
         FIG.  2    is a flowchart of a method for inspecting configuration code utilizing a security graph, implemented in accordance with an embodiment. 
         FIG.  3    is a schematic illustration of a portion of a security graph for cybersecurity risk assessment of virtual instances in a cloud computing environment, implemented in accordance with an embodiment. 
         FIG.  4    is a schematic illustration of a code inspector implemented according to an embodiment. 
         FIG.  5    is a code object, shown in accordance with an embodiment. 
         FIG.  6    is a flowchart of a method for detecting lateral movement based on a code object, implemented in accordance with an embodiment 
     
    
    
     DETAILED DESCRIPTION 
     It is important to note that the embodiments disclosed herein are only examples of the many advantageous uses of the innovative teachings herein. In general, statements made in the specification of the present application do not necessarily limit any of the various claimed embodiments. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise indicated, singular elements may be in plural and vice versa with no loss of generality. In the drawings, like numerals refer to like parts through several views. 
     Infrastructure as code (IaC) allows fast and reliable deployment of workloads and accounts in cloud based computing environments. A workload may be, for example, a virtual machine, a container, or a serverless function. A virtual machine may be implemented for example as an Oracle® VM VirtualBox hypervisor, a container may be implemented on a Kubernetes® platform, and serverless function may be implemented as Amazon® Web Services (AWS) Lambda. Accounts may be user accounts, service accounts, roles, and the like. 
     Typically, the deployed environment, also known as a production environment, differs over time from the initial deployment configuration, due for example to upgrades and patches implemented in production but not always updated in configuration code. This can occur for example due to human error. Furthermore, many deployment environments utilize a continuous integration, continuous deployment (Cl/CD) approach, for which a plurality of deployment environments are used. A cloud computing environment is designed, in an embodiment, utilizing infrastructure as code tool and other development tools in a development (i.e., dev) environment, and deployed to a test environment where code is tested. In an embodiment, code which passes tests, benchmarks, and the like, is then deployed to a staging environment which is similar to the production environment. In each of these environments, a single code object can correspond to multiple machines which are deployed based on the code object, each of which can be host to cybersecurity vulnerabilities. For example, a code object includes a code instruction which when executed to deploy a workload, configures a workload having a misconfiguration. If the misconfiguration is detected and corrected in the production environment, the code remains faulty and therefore the next machine deployed based off of the code will also have a misconfiguration. 
     In an embodiment, a security graph includes a representation of a production environment. The security graph is utilized in inspecting the configuration code to ascertain that code objects comply with the specification of the production environment. For example, the security graph is queried, in an embodiment, to detect a node representing a workload, which corresponds to an identifier of a code object. By utilizing information represented in the security graph of the production environment and applying it to code objects, cybersecurity threats can be detected faster, and to the point where they originate. 
     Alerts may be generated to indicate that the configuration code would result in a new production environment which is deficient, for example due to vulnerability, when compared with the current production environment. 
     While declaratory code is used precisely because it is intuitive for humans to read and write declaratory code, it should be appreciated that inspecting such code for cybersecurity issues is not a task that can be performed by humans. Specifically, inspecting code to detect a cybersecurity issue needs to be performed in a reliable and consistent manner, and done so repeatedly over often thousands of lines of code. Even if it were practical for a human to read through thousands of lines of computer code within any meaningful time frame (cloud computing environments are elastic and constantly changing), doing so while searching for hundreds of thousands of various cybersecurity issues is impossible. Furthermore, humans are not capable of performing such tasks repeatedly and reliably, as they apply objective standards to what is a cybersecurity issue. 
     By contrast, an embodiment of the system disclosed herein applies objective criteria in detection of cybersecurity issues, and does so in a manner which is reliable, consistent, and in a timeframe which is relevant to the operation of a cloud computing environment. Additionally, methods disclosed herein provide for improved efficiency of computer systems, by reducing use of memory, processors, and the like. 
       FIG.  1    is a network diagram  100  of a monitored cloud computing environment utilizing infrastructure as code (IaC) utilized to describe the various embodiments. 
     A client device  110  generates a configuration code file  120  based on input from one or more users (e.g., software programmers). In an embodiment, a client device is a personal computer, a tablet, a laptop, and the like. In some embodiment, a client device  110  is used to access a server (not shown) which provides a computing environment into which input can be provided. It should be apparent that the client device  110  is shown here for simplicity and pedagogical purposes, and that the configuration code file  120  is generated, in other embodiments, by the client device, a virtual workload in a cloud computing environment, a combination thereof, and the like. In certain embodiments, the configuration code file  120  is generated by multiple different client devices. For example, a plurality of users may each utilize a different client device and update a single configuration code file  120 , for example, with code objects. In some embodiments, a single client device  110  generates multiple configuration code files. 
     In an embodiment the configuration code file  120  is implemented in a declaratory computer language. In a declaratory computer language, a user declares resources they would like to have as code objects, and an orchestrator, such as orchestrator  130 , is configured to deploy workloads in a cloud computing environment based on the declarations. For example, an orchestrator  130  is configured, in an embodiment, to translate a declaratory code to a configuration code, which includes instructions which when executed configure a cloud computing environment to deploy a workload, virtual instance, and the like. 
     In certain embodiments, multiple configuration code files  120  may be utilized. For example, a user may operate multiple cloud environments, each with its own configuration code. For example, a first configuration code file is directed to deploying a cloud computing environment over Microsoft® Azure, while a second configuration code file is directed to deploying a cloud computing environment over Amazon® Web Services (AWS). 
     As another example, a user can declare a first resource type (e.g., virtual machine) for a first cloud environment (e.g., AWS) and for a second cloud environment (Google® Cloud Platform—GCP) in a first configuration code file, and a second resource type (e.g., software container) for the first cloud environment (AWS) and the second cloud environment (GCP) in a second configuration code file. 
     In an embodiment, an orchestrator  130  is configured to receive the configuration code file  120 . In certain embodiments, the orchestrator  130  is configured to initiate actions in a cloud computing environment  140 , for example, to deploy workloads, instances, user accounts, service accounts, combinations thereof, and the like, based on declarations of the configuration code file  120 . In an embodiment, an instance is a virtual instance, and may be, for example a virtual machine  142 , software container  144 , a serverless function  146 , and the like. 
     In some embodiments, the orchestrator  130  is configured to deploy workloads by assigning (also known as provisioning) cloud computing environment resources, such as processors, memory, storage, etc. to the workload. In an embodiment, workloads are deployed in a production environment, which is a cloud computing environment having operable code, used for providing access to data and providing software services. In some embodiments, configuration code is implemented in a development (dev) environment, which also utilizes a cloud computing environment. 
     In some embodiments, a plurality of workloads are associated with a first code object (not shown) of the configuration code file  120 . Workloads which are all deployed based on a same code object (i.e., the first code object) are known as a virtual instance (or “instance”) of the first code object. In an embodiment, associating a workload with a code object includes assigning a name to the instance based on an identifier of the code object. 
     This provides an advantage where it is required to deploy multiple instances which share similar configurations, such as web servers providing access to a website. Rather than configure each instance manually and individually, an orchestrator  130  is configured to deploy a number of the same workload based on the configuration code file  120 . 
     In some embodiments, the orchestrator  130  may configure a cloud-native orchestrator (not shown) in the cloud computing environment  140  to deploy the instances. This may be advantageous, for example, where instances need to be deployed in different cloud environments. 
     For example, the same instances may be deployed simultaneously on Google® Cloud Platform (GCP), Amazon® Web Services (AWS), or Microsoft® Azure. This can be achieved by configuring the orchestrator  130  to generate native instructions for a cloud native orchestrator in each environment to deploy such instances. The native instructions are generated by the orchestrator  130  in an embodiment. The instructions are generated based on objects detected in the configuration code file  120 . 
     This method of deploying instances decreases errors by eliminating the need for a user to manually deploy each instance and configure each instance separately, and is also thus a faster method of deployment. A human is not able to consistently and reliably initiate deployment of virtual instances, and then configure hundreds or thousands of such instances to match the same specification. In the example above a first load balancer may be deployed in a first cloud computing environment, and a second load balancer may be deployed in a second cloud computing environment, each cloud computing environment having different infrastructure from each other, wherein the first load balancer and the second load balancer are deployed based on the same code object from a configuration code file. 
     In an embodiment, the first cloud computing environment  140  is coupled with a second cloud computing environment  150 , which is configured to inspect the first cloud computing environment  140  for cybersecurity threats. In an embodiment, the second cloud computing environment  150  (also referred to as inspection environment  150 ) is further configured to receive the configuration code file  120 . 
     In some embodiments, the second cloud environment  150  is utilized for inspecting the first cloud computing environment  140  and generating cybersecurity risk assessments for instances deployed in the first cloud computing environment  140 . 
     In certain embodiments, the second cloud environment  150  includes a plurality of inspectors, such as inspector  160 . An inspector is a workload which is configured to inspect another workload for cybersecurity objects, such as a secret, a file, a folder, a registry value, a weak password, a certificate, a malware object, a hash, a misconfiguration, a vulnerability, an exposure, a combination thereof, and the like. In an embodiment, an inspector  180  is configured to inspect for a plurality of cybersecurity object types. 
     For example, in an embodiment, an inspector is configured to inspect the virtual machine  142  for a predetermined cybersecurity object, in response to receiving an instruction to inspect the virtual machine  142 . In an embodiment the instruction is received through an API (not shown) of the first cloud computing environment  140 . In some embodiments, an inspectable disk is generated based on a volume (not shown) attached to the virtual machine  142 , and the inspectable disk is provided to the second cloud computing environment  150  for inspection. In an embodiment, generating an inspectable disk includes generating a clone of the volume, generating a copy of the volume, generating a snapshot of the volume, and the like. In an embodiment, a software container is deployed in the second cloud computing environment  150  and attached to a volume generated in the second cloud computing environment  150  based on the received snapshot. The inspector  160  is configured, in an embodiment, to inspect the attached volume for a predefined cybersecurity object type. In an embodiment, the inspector  160  is configured to generate data which is stored on a security graph  170 . In some embodiments, a node is stored on the security graph  170  to represent an inspected resource. In an embodiment, data generated by the inspector  160  is stored on the node representing the workload which the inspector  160  inspected for a cybersecurity object. 
     In an embodiment, the security graph  170  is stored on a graph database. The security graph  170  includes a representation of a cloud computing environment. In an embodiment, the representation includes a plurality of nodes, at least a portion of which each represent a resource or a principal. A resource is a cloud entity which provides access to a service, computer hardware (e.g., processor, memory, storage, and the like), and the like. In an embodiment, a resource is a workload, such as a virtual machine, serverless function, software container, and the like. A principal is a cloud entity which is authorized to initiate actions in a cloud computing environment, and is authorized to act on a resource. In an embodiment, a principal is a user account, a user group, a service account, and the like. 
     In certain embodiments, the security graph  170  further includes enrichment nodes, which represent certain redetermined functionalities, network access, and the like. For example, an enrichment node may be used to represent access to a public network, such as the Internet. Thus, a node representing a workload which has access to a public network, or can be accessed through a public network, is connected in the security graph  170  to an enrichment node representing public network access. 
     In an embodiment, a code inspector  180  is further deployed in the second cloud computing environment  150 . In some embodiments, a plurality of code inspectors are deployed. In certain embodiments, configuration code is generated by multiple different type of platforms, such as Pulumi®, Terraform®, and the like. 
     In some embodiments, a first code inspector is configured to inspect configuration code generated using Pulumi®, while a second code inspector is configured to inspect configuration code generated using Terraform®. In an embodiment, the code inspector  180  is realized as a workload, such as an application deployed on a software container, configured to receive configuration code and inspect the configuration code to detect a predetermined type of code object. In an embodiment, a type of code object is, for example, a secret (such as a public key, or a private key), a resource type, an application identifier, a policy identifier, a role identifier, a status of a flag, and the like. A flag status indicates, in an embodiment, that a certain object is allowed to perform certain actions, such as network access, or assume a role, such as an administrator role (in the case of a user or service account). 
     In an embodiment, the code inspector  180  is configured to match the detected object to a node in the security graph  170 . This is discussed in more detail with respect to  FIG.  2    below. 
       FIG.  2    is an example flowchart  200  of a method for inspecting configuration code utilizing a security graph, implemented in accordance with an embodiment. In an embodiment, configuration code in a development (dev) environment is inspected based on a security graph which is generated at least in part based on a production environment. 
     A production environment is rarely, if at all, identical to the environment which is deployed initially by code. This is due to, for example, upgrades and patches implemented in the production environment to address issues caused by the code deployment. Drifting configuration, or configuration drift, describes how a production environment, over time, ‘drifts’ further away from the initial configuration code design. Therefore, inspecting only one environment for cybersecurity threats is not enough, and it is advantageous to inspect both. 
     In an embodiment, the security graph includes representations of the configuration code (e.g., representing code objects) and the production environment (e.g., representing resources and principals). By inspecting a configuration code file based on a security graph generated from data of a production environment, insight can be gained, and deployment issues may be caught early on, for example to identify instances which if deployed based on a current version of configuration code would include a version of software which the production environment has already upgraded to a newer version. In an embodiment, the method is performed by a configuration code inspector, such as the code inspector  180 . 
     At S 210 , configuration code is received. In an embodiment, the configuration code includes a plurality of code objects. In certain embodiments, a portion of the code objects correspond to instances which are deployed in a cloud computing environment. In an embodiment, the configuration code is scanned or otherwise inspected as a textual object. For example, a configuration code is searched for regular expressions (regex), strings, and the like. 
     At S 220 , a first code object is extracted from the received code. Extracting a code object includes, in an embodiment, searching the text of a configuration code file for a predetermined string. For example, a code object may be a text field identifying a type of workload, a name of a workload, a network address, a name in a namespace, a role, a permission, and the like. In some embodiments, a plurality of code objects are extracted from the received code. 
     At S 230 , a security graph is traversed to detect a node in the graph corresponding to the extracted first code object. In an embodiment, traversing the security graph includes sending a request through an API of a graph database hosting the security graph to search the graph for a string, a value, and the like, which corresponds to the first code object. For example, if the first code object includes a secret, such as a private key (i.e., an alphanumerical representation), the security graph is traversed to detect a node which represents a matching public key (e.g., public key node). In an embodiment, the public key node is connected to a resource node representing a resource which utilizes the public key. 
     In some embodiments, a query directed at the security graph includes a plurality of clauses. In an embodiment, multiple-clause query is generated to search for container nodes (i.e., nodes representing containers) which are connected to a node representing the public key. It is noted that detecting a node which corresponds to the extracted first object includes, in an embodiment, detecting a node which is not a node representing a workload corresponding to the first object. 
     For example, executing code of the first code object results, in an embodiment, in deploying a first load balancer in a virtual private cloud (VPC). In an embodiment, a node is generated in a security graph to represent the first load balancer deployed in a cloud computing environment. The node representing the load balancer is connected to a node representing the VPC. 
     An advantage of the disclosed method is that attributes of the first code object detected in the graph allows detecting nodes representing cybersecurity issues, nodes representing workloads, enrichment nodes, and the like, prior to the generation of an instance based on the code object. This allows detecting a security risk in an instance prior to it being deployed in a computing environment. In the above example, as the code of the first code object includes instructions to deploy in the VPC, the VPC node is detected (based, for example, on detecting an identifier of the VPC in the code) in the security graph. Cybersecurity risks represented by nodes connected to the VPC node are detected, for example by querying the security graph. 
     At S 240 , a check is performed to determine if a node is detected. If ‘no’ execution may continue at S 270 . In an embodiment, if a node is not detected (e.g., the node does not exist), a new node is generated in the security graph to represent the first code object. If a node is detected execution continues to S 250 . 
     At S 250 , a check is performed to determine if the detected node corresponds to a previously determined cybersecurity issue, such as a cybersecurity risk factor, vulnerability, misconfiguration, and the like. A risk factor, vulnerability, misconfiguration, and the like, may be, for example, access to a network resource (such as the internet), access from a network resource, outdated software, privilege escalation, and the like. In an embodiment, a risk factor score is further determined. In some embodiments, the score indicates the severity of the risk, such as ‘low’, ‘medium’, ‘high’, and ‘critical’. In an embodiment, the previously determined cybersecurity issue is detected by inspecting a disk for a cybersecurity object. In some embodiments, a detected cybersecurity issue is represented as a node in a security graph, connected to a node representing a resource on which the cybersecurity issue was detected. 
     In an embodiment, a mitigation instruction corresponding to the risk factor score is executed. In some embodiments, the risk factor is indicated by metadata associated with the detected node of S 240 . If the detected node corresponds to a previously determined cybersecurity issue execution continues at S 260 ; otherwise, execution continues at S 270 . 
     In an embodiment, a vulnerability is represented on the security graph by a node. As an example, a node representing a workload is connected to a node representing a vulnerability. Where a workload node is the detected node, a cybersecurity vulnerability is associated with the code object. 
     At optional S 260  a notification is generated to indicate that a security risk has been detected in the configuration code. In an embodiment the notification is sent to a client device, a user account, a combination thereof, and the like, which authored the code. Code authors are determined, in an embodiment, by a user account identifier present in the configuration code. 
     In some embodiments, the notification includes an indicator to specify why the notification is generated. In certain embodiments an instruction to perform a mitigation action is generated. In the example above, an alert (i.e., notification) is generated in response to detecting that a workload includes an outdated software version, and the alert includes the current software version which would need to be configured in the configuration code in order to mitigate the risk of deploying a workload with an outdated software version. 
     At S 270  a check is performed to determine if another code object should be inspected. If ‘yes’ execution continues at S 220 , otherwise execution terminates. 
       FIG.  3    is a schematic illustration of a portion of a security graph  300  for cybersecurity risk assessment of virtual instances in a cloud computing environment, implemented in accordance with an embodiment. The graph  300 , which in an embodiment is stored in a graph database, includes a plurality of nodes. In an embodiment, a node represents a resource, principal, metadata, enrichment data, and the like. 
     In an embodiment, the graph  300  includes a first cloud key node  310  (representing a first cloud key) and a second cloud key node  320  (representing a second cloud key), which are connected to a user account node  340  (representing a user account). A third cloud key node  330  (representing a third cloud key) is connected to a service account node  360  (representing a service account). The user account node  340  and service account node  360  are connected to an identity and access management (IAM) object node  350  (representing an IAM object). 
     In an embodiment, a cloud key provides temporary access, permanent access, and the like, between a first workload and a second workload. In some embodiments, one or more first workloads and one or more second workloads may be on the same tenant, on different tenants, or on a combination thereof. In an embodiment, cloud keys are embedded into text configuration files, structured configuration files (e.g., JSON, YAML, XML, etc.), scripts, source code, and the like. Example implementations of cloud keys include AWS IAM access keys, OAuth® refresh tokens, access tokens, and the like. 
     By generating a security graph  300  including such nodes and populating it with data representing the cloud computing environment allows assessing of cybersecurity risks. For example, if a first cloud key is compromised, it is readily apparent what other objects are vulnerable as a result, by querying the security graph  300  and detecting cloud entities which are represented by nodes connected to, for example, a node representing the first cloud key. In an embodiment each node further stores metadata and data relating to the object. For example, a cloud key node  320  may include therein a unique account identifier. 
     In some embodiments, the security graph  300  further includes a representation of a cybersecurity issue. For example, a misconfiguration is represented by a node in the security graph, in an embodiment. In an embodiment a node representing a cybersecurity issue is connected to a node which represents a resource. This is performed to indicate that the resource includes the cybersecurity issue. For example, an inspector is configured to detect a cybersecurity issue, and detects the cybersecurity issue on a software container which is inspected by the inspector. In an embodiment, a security graph is updated to include a node representing the software container connected to a node representing the cybersecurity issue. 
     In certain embodiments, generating a node representing a cybersecurity issue allows to reduce redundant information stored in a graph database, where storing a connection requires less resources than storing information about the cybersecurity issue in each node representing a resource where the cybersecurity issue is detected. This allows compact representation, thereby reducing computer resource consumption. This further allows to rapidly detect all resources having a certain cybersecurity issue, as rather than querying each node to determine if the node includes information on a specific cybersecurity issue, a single node is queried to detect nodes connected to it. This reduces the amount of processing required on a database search. 
     In an embodiment, the first cloud key node  310  is connected to a code object node  305 . In certain embodiments, a code object of a configuration code is represented in the security graph by a code object node  305 . In certain embodiments, storing a connection between the first cloud key node  310  to the code object node  305  is performed in response to detecting the first cloud key in the code object. 
     In certain embodiments, the code object represented by the code object node  305  is utilized to deploy instances of a virtual machine. An instance of a virtual machine is represented by VM node  303 . In an embodiment, the VM node  303  is connected to the cloud key node  310 . In some embodiments the connection is generated in response to detecting, for example by an inspector workload, such as inspector  160 , the cloud key on a disk of the virtual machine. 
     Such a representation as detailed above is advantageous for example when a node is compromised in order to detect potential lateral movement. For example, where the user account represented by the user account node  340  is determined to be compromised, exposed, vulnerable, and the like, the security graph  300  is queried, according to an embodiment, to detect nodes representing secrets connected to a node representing the vulnerable user account (i.e., user account node  340 ). 
     Quick detection of potential lateral movement paths allows such paths to be blocked, thus reducing the time an attacker has to take advantage of an exploit. It is an advantage in cybersecurity to be able to rapidly detect and respond to cybersecurity threats as soon as they appear, as the damage caused by an attacker is often directly correlated with the amount of time the attacker has to take advantage of a weakness, vulnerability, exposure, and the like, in a cloud computing environment. 
       FIG.  4    is a schematic illustration of a code inspector  180  implemented according to an embodiment. The code inspector  180  may be implemented as a physical machine or a virtual workload, such as a virtual machine or container. 
     When implemented as a physical machine, the code inspector  180  includes at least one processing circuitry  410 , for example, a central processing unit (CPU). In an embodiment, the processing circuitry  410  may be, or be a component of, a larger processing unit implemented with one or more processors. The one or more processors may be implemented with any combination of general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate array (FPGAs), programmable logic devices (PLDs), controllers, state machines, gated logic, discrete hardware components, dedicated hardware finite state machines, or any other suitable entities that can perform calculations or other manipulations of information. In certain embodiments it may be advantageous for the at least one processing circuitry  410  to further include one or more general purpose graphic processor units (GPGPUs). For example, for comparing and generating digests, a GPGPU may have improved performance over a CPU. 
     The processing circuitry  410  is coupled via a bus  405  to a memory  420 . The memory  420  may include a memory portion  425  that contains instructions that when executed by the processing element  410  performs the method described in more detail herein. The memory  420  may be further used as a working scratch pad for the processing element  410 , a temporary storage, and others, as the case may be. The memory  420  may be a volatile memory such as, but not limited to random access memory (RAM), or non-volatile memory (NVM), such as, but not limited to, Flash memory. The memory may further include a memory portion  425  which is used to store objects extracted from a configuration code. 
     The processing element  410  may be coupled to a network interface controller (NIC)  430 , which provides connectivity to one or more cloud computing environments, such as the first cloud computing environment  140  of  FIG.  1   , via a network. 
     The processing element  410  may be further coupled with a storage  440 . The storage  440  may be used for the purpose of holding a copy of the method executed in accordance with the disclosed technique. The storage  440  may include a storage portion  445  containing a configuration code for deployment in a cloud computing environment. 
     The processing element  410  and/or the memory  420  may also include machine-readable media for storing software. Software shall be construed broadly to mean any type of instructions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Instructions may include code (e.g., in source code format, binary code format, executable code format, or any other suitable format of code). The instructions, when executed by the one or more processors, cause the processing system to perform the various functions described in further detail herein. 
       FIG.  5    is an example of a code object, shown in accordance with an embodiment. A code object  500  includes an object type  510 . The object type  510  indicates, in this example, that this code object is a resource type, i.e., executing instructions related to this object will deploy a resource in a cloud computing environment. The object type further includes data fields, such as instance type data field  512  and network association data field  514 . The instance type  512  specifies what type of resource is to be deployed, in this case the instance type is a t2.micro, which is a processing instance used in the AWS cloud computing environment. The network association field  514  indicates, in this example, that the instance should be associated with a specific virtual private cloud (VPC). In this example the code object is a data structure having parameters (or data fields) which can be customized to generate resources, accounts, and the like, in a cloud computing environment. 
       FIG.  6    is an example flowchart  600  of a method for detecting lateral movement based on a code object, implemented in accordance with an embodiment. Detection of potential lateral movement paths is advantageous as detection allows to increase the speed at which mitigation is initiated. This in turn reduces the amount of time, and therefore damage, an attacker has in a cloud computing environment. 
     At S 610 , an exposed cloud entity is selected. In some embodiments, a cloud entity is determined to be exposed based on inspection of the cloud entity. For example, a resource, such as a software container is determined to be exposed in response to an inspector detecting a misconfiguration on the software container. In certain embodiments, an inspector is configured to select an exposed cloud entity, for example based on a generated list of exposed cloud entities. 
     In an embodiment, the exposed cloud entity is a potentially exposed cloud entity. This is advantageous for example to simulate a potential lateral movement. In an embodiment, a cloud entity is a resource, a principal, and the like. A resource is a cloud entity which provides access to a service, computer hardware (e.g., processor, memory, storage, and the like), and the like. In an embodiment, a resource is a workload, such as a virtual machine, serverless function, software container, and the like. A principal is a cloud entity which is authorized to initiate actions in a cloud computing environment, and is authorized to act on a resource. In an embodiment, a principal is a user account, a user group, a service account, a role, and the like. 
     At S 620 , a secret is detected on the exposed cloud entity. In an embodiment, detecting a secret on a cloud entity includes generating a query for a security graph to detect a node representing the cloud entity. For example, according to an embodiment, a query is generated based on an identifier of a virtual machine to detect a node in the security graph which represents the virtual machine. The security graph is then traversed to detect a node representing a secret connected to the node representing the virtual machine. 
     In an embodiment, a secret is a cloud key, a certificate, a private key, a public key, a password, a passphrase, a combination thereof, and the like. In some embodiments, the secret is represented by a node in the security graph. Connecting a node representing the secret to a node representing, for example, a resource, indicates that the secret was detected on the resource, according to an embodiment. For example, in an embodiment a cloud key is detected in a layer of a serverless function by an inspector workload which is configured to inspect the serverless function for a predetermined type of cloud key. 
     At S 630 , a query is generated to detect a code object. In an embodiment, the security graph is queried with the generated query to detect a node representing the code object. In some embodiments, the code object is a code object which is utilized in a declaratory code of an infrastructure as code file to deploy instances of a resource in a cloud computing environment, deploy user accounts, deploy service accounts, combinations thereof, and the like. 
     According to an embodiment, detecting a code object incudes traversing the security graph to detect a code object node connected to the node representing the secret. In an embodiment, a node representing a code object is connected to a node representing a secret in response to detecting the secret in the code object, for example by an inspector workload. 
     At S 640 , a second cloud entity is detected. In an embodiment, the second cloud entity is detected in the security graph. For example, according to an embodiment, a node representing the second cloud entity is connected to node representing the code object. In certain embodiments, a node representing a second cloud entity is connected to a node representing a code object in response to determining that the second cloud entity is deployed in the cloud computing environment based on the code object. For example, according to an embodiment, a virtual machine is deployed in a virtual private cloud of a cloud computing environment based on a code object of a declaratory code, such as discussed in more detail herein above. 
     In some embodiments, detecting the second cloud entity includes traversing the security graph to detect a cloud entity node connected to the code object node. In certain embodiments a plurality of second cloud entity nodes are detected. Detecting the second cloud entity (i.e., the node representing the second cloud entity) is advantageous as it allows to detect a potential lateral movement path. For example, where an attacker gains access to a cloud key utilized by a first virtual machine, the cloud key also present on a second virtual machine (which is not an instance of the first virtual machine), can allow the attacker to move from one virtual machine to another. However, by detecting such a potential lateral movement, mitigation can occur faster. 
     In an embodiment, a lateral path movement is stored. For example, a lateral path movement includes a first node, a second node, and a vertex connecting the first node to the second node, according to some embodiments. In certain embodiments, the lateral movement path includes a direction (i.e., movement is from the first node to the second node). 
     At S 650 , a mitigation action is initiated for the second cloud entity. In an embodiment, a mitigation action includes generating a notification, an alert, a combination thereof, and the like based on detecting the second cloud entity. In an embodiment detecting the second cloud entity is detection of a lateral movement path. In some embodiments, the mitigation action includes revoking permission of a user account, revoking permission of a service account, forcing a cloud key to expire, forcing a certificate to expire, invalidating a cloud key, invalidating a certificate, a combination thereof, and the like. 
     In some embodiments, the mitigation action includes initiating an inspection of the second cloud entity. For example, in an embodiment, an inspector workload, such as the inspector  160  of  FIG.  1    above, is configured to inspect the second cloud entity, a storage of the second cloud entity, disk of the second cloud entity, and the like, for example for a cybersecurity object. 
     The various embodiments disclosed herein can be implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium consisting of parts, or of certain devices and/or a combination of devices. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such a computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit. Furthermore, a non-transitory computer readable medium is any computer readable medium except for a transitory propagating signal. 
     All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the disclosed embodiment and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the disclosed embodiments, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. 
     It should be understood that any reference to an element herein using a designation such as “first,” “second,” and so forth does not generally limit the quantity or order of those elements. Rather, these designations are generally used herein as a convenient method of distinguishing between two or more elements or instances of an element. Thus, a reference to first and second elements does not mean that only two elements may be employed there or that the first element must precede the second element in some manner. Also, unless stated otherwise, a set of elements comprises one or more elements. 
     As used herein, the phrase “at least one of” followed by a listing of items means that any of the listed items can be utilized individually, or any combination of two or more of the listed items can be utilized. For example, if a system is described as including “at least one of A, B, and C,” the system can include A alone; B alone; C alone; 2A; 2B; 2C; 3A; A and B in combination; B and C in combination; A and C in combination; A, B, and C in combination; 2A and C in combination; A, 3B, and 2C in combination; and the like.