System and method for providing security posture management for ai applications

1 . A method for determining an artificial intelligence (AI) security posture management (SPM) of a cloud computing environment, comprising:

inspecting, using an inspector in an inspection environment, the cloud computing environment for components of an AI pipeline;

generating an AI pipeline representation based on a representation of each component of the AI pipeline in a security database;

inspecting, using an inspector in an inspection environment, the cloud computing environment for a cybersecurity object associated with a component of the AI pipeline, wherein the cloud computing environment is a production environment distinct from any inspection environment and any inspector is a workload in an inspection environment;

analyzing the AI pipeline for a cybersecurity risk based on a result of inspecting the cloud computing environment for the cybersecurity object;

detecting a principal deployed in the cloud computing environment as a component of the AI pipeline;

detecting a permission associated with the principal through an identity and access management (IAM) service;

determining that the principal includes excessive permissions, based on the detected permission; and

initiating a remediation action in the cloud computing environment in response to detecting the cybersecurity risk and the excessive permissions.

2 . The method of claim 1 , further comprising:

analyzing the AI pipeline for a combined cybersecurity risk based on detecting a cybersecurity object and a component of the AI pipeline.

3 . The method of claim 2 , wherein the cybersecurity object is a sensitive data, and the component of the AI pipeline is an exposed workload.

4 . The method of claim 1 , further comprising:

detecting the cybersecurity risk based on a combination of secondary cybersecurity risks.

5 . The method of claim 1 , further comprising:

detecting a database in the AI pipeline;

determining that the detected database includes sensitive data; and

generate an alert based on the sensitive data.

6 . The method of claim 1 , further comprising:

detecting a misconfiguration on a component of the AI pipeline; and

initiating a remediation based on the detected misconfiguration.

7 . The method of claim 1 , further comprising:

detecting an AI model stored on a component of the AI pipeline;

determining that the AI model is associated with a cybersecurity risk; and

initiating a remediation based on the cybersecurity risk.

8 . The method of claim 7 , further comprising:

determining that the AI model is vulnerable to a prompt injection, based on the associated cybersecurity risk.

9 . The method of claim 7 , further comprising:

detecting that an output of the AI model is directed to a predetermined sensitive system, the sensitive system being a component of the AI pipeline;

determining that the AI model is configured to generate an executable instruction; and

determining that the AI model is vulnerable to an output handling, in response to determining that the AI model is configured to generate the executable instruction.

10 . The method of claim 1 , further comprising:

detecting a secret in a component of the AI pipeline; and

generating a cybersecurity risk assessment based on the detected secret.

11 . The method of claim 1 , further comprising:

detecting a potential attack path to a component of the AI pipeline.

12 . The method of claim 11 , further comprising:

detecting a network path between an external network and the component of the AI pipeline; and

detecting the potential attack path based on the network path.

13 . The method of claim 1 , further comprising:

configuring the inspector that is inspecting the cloud computing environment for a cybersecurity object associated with a component of the AI pipeline to inspect the component of the AI pipeline for any one of: a vulnerability, an identity, a network exposure, a malware, a sensitive data, a secret, and any combination thereof.

14 . The method of claim 1 , wherein the inspector that is inspecting the cloud computing environment for components of an AI pipeline detects AI components by performing static analysis.

15 . The method of claim 1 , wherein the cloud computing environment includes at least two different cloud computing environments and wherein at least two of the components of the AI pipeline are deployed in different ones of the at least two cloud computing environments.

16 . The method of claim 1 , wherein inspecting for a cybersecurity object further comprises:

causing creation of a clone disk; and

Inspecting the clone disk for the cybersecurity object.

17 . A non-transitory computer-readable medium storing a set of instructions for determining an artificial intelligence (AI) security posture management (SPM) of a cloud computing environment, the set of instructions comprising:

one or more instructions that, when executed by one or more processors of a device, cause the device to:

inspect, using an inspector in an inspection environment, the cloud computing environment for components of an AI pipeline;

generate an AI pipeline representation based on a representation of each component of the AI pipeline in a security database;

inspect, using an inspector in an inspection environment, the cloud computing environment for a cybersecurity object associated with a component of the AI pipeline, wherein the cloud computing environment is a production environment distinct from any inspection environment and any inspector is a workload in an inspection environment;

analyze the AI pipeline for a cybersecurity risk based on a result of inspecting the cloud computing environment for the cybersecurity object;

detect a principal deployed in the cloud computing environment as a component of the AI pipeline;

detect a permission associated with the principal through an identity and access management (IAM) service;

determine that the principal includes excessive permissions, based on the detected permission; and

initiate a remediation action in the cloud computing environment in response to detecting the cybersecurity risk and the excessive permissions.

18 . A system for determining an artificial intelligence (AI) security posture management (SPM) of a cloud computing environment comprising:

a processing circuitry;

a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:

inspect, using an inspector in an inspection environment, the cloud computing environment for components of an AI pipeline;

generate an AI pipeline representation based on a representation of each component of the AI pipeline in a security database;

inspect, using an inspector in an inspection environment, the cloud computing environment for a cybersecurity object associated with a component of the AI pipeline, wherein the cloud computing environment is a production environment distinct from any inspection environment and any inspector is a workload in an inspection environment;

analyze the AI pipeline for a cybersecurity risk based on a result of inspecting the cloud computing environment for the cybersecurity object;

detect a principal deployed in the cloud computing environment as a component of the AI pipeline;

detect a permission associated with the principal through an identity and access management (IAM) service;

determine that the principal includes excessive permissions, based on the detected permission; and

initiate a remediation action in the cloud computing environment in response to detecting the cybersecurity risk and the excessive permissions.

19 . The system of claim 18 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

analyze the AI pipeline for a combined cybersecurity risk based on detecting a cybersecurity object and a component of the AI pipeline.

20 . The system of claim 19 , wherein the cybersecurity object is a sensitive data, and the component of the AI pipeline is an exposed workload.

21 . The system of claim 18 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect the cybersecurity risk based on a combination of secondary cybersecurity risks.

22 . The system of claim 18 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect a database in the AI pipeline;

determine that the detected database includes sensitive data; and

generate an alert based on the sensitive data.

23 . The system of claim 18 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect a misconfiguration on a component of the AI pipeline; and

initiate a remediation based on the detected misconfiguration.

24 . The system of claim 18 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect an AI model stored on a component of the AI pipeline;

determine that the AI model is associated with a cybersecurity risk; and

initiate a remediation based on the cybersecurity risk.

25 . The system of claim 24 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

determine that the AI model is vulnerable to a prompt injection, based on the associated cybersecurity risk.

26 . The system of claim 24 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect that an output of the AI model is directed to a predetermined sensitive system, the sensitive system being a component of the AI pipeline;

determine that the AI model is configured to generate an executable instruction; and

determine that the AI model is vulnerable to an output handling, in response to determining that the AI model is configured to generate the executable instruction.

27 . The system of claim 18 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect a secret in a component of the AI pipeline; and

generate a cybersecurity risk assessment based on the detected secret.

28 . The system of claim 18 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect a potential attack path to a component of the AI pipeline.

29 . The system of claim 28 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect a network path between an external network and the component of the AI pipeline; and

detect the potential attack path based on the network path.

30 . The system of claim 18 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

configure the inspector that is inspecting the cloud computing environment for a cybersecurity object associated with a component of the AI pipeline to inspect the component of the AI pipeline for any one of:

a vulnerability, an identity, a network exposure, a malware, a sensitive data, a secret, and any combination thereof.