System and method for detecting cybersecurity risks in managed artificial intelligence models
A system and method for detecting a combined cybersecurity risk for an artificial intelligence (AI) model is presented. The method includes: inspecting a computing environment for an AI model deployed therein; generating a representation of the AI model in a security database, the security database including a representation of the computing environment; detecting a first cybersecurity risk respective of the AI model; inspecting the computing environment for a cybersecurity object; determining that the AI model is exposed to a toxic combination cybersecurity risk based on the detected first cybersecurity risk and the cybersecurity object; and initiating a mitigation action based on the toxic combination cybersecurity risk.
1. A method for detecting a combined cybersecurity risk for an artificial intelligence (AI) model, comprising:
detecting an AI model deployed in a computing environment;
generating a representation of the AI model in a security database, the security database including a representation of the computing environment;
detecting in the AI model a first cybersecurity object;
detecting in the computing environment a second cybersecurity object;
determining that the AI model is exposed to a toxic combination cybersecurity risk based on the detected first cybersecurity object and the detected second cybersecurity object; and
initiating a mitigation action based on the toxic combination cybersecurity risk.
2. The method of claim 1 , further comprising:
initiating the mitigation action based on the first cybersecurity object.
3. The method of claim 1 , further comprising:
initiating the mitigation action further based on the second cybersecurity object.
4. The method of claim 1 , further comprising:
generating an inspectable disk based on an original disk of a resource deployed in the computing environment; and
inspecting the inspectable disk for any one of: the AI model, the second cybersecurity object, and a combination thereof.
5. The method of claim 4 , further comprising:
detecting an artifact of the AI model on the inspectable disk.
6. The method of claim 5 , wherein the artifact is any one of: a file, a software library, a software binary, a folder name, a hash, and any combination thereof.
7. The method of claim 1 , further comprising:
detecting that the AI model is configured to execute a code object.
8. The method of claim 1 , further comprising:
detecting a metadata of the AI model, wherein the metadata indicates that the AI model is associated with a cybersecurity risk.
9. The method of claim 1 , further comprising:
detecting in the AI model any one of: a secret, a certificate, a code, and any combination thereof.
10. The method of claim 1 , further comprising:
applying a policy on the representation of the AI model.
11. The method of claim 10 , further comprising:
initiating the mitigation action based on a result of applying the policy.
12. The method of claim 1 , wherein the toxic combination cybersecurity risk is any one of: a vulnerability, an exposure, a misconfiguration, and a combination thereof.
13. A non-transitory computer-readable medium storing a set of instructions for detecting a combined cybersecurity risk for an artificial intelligence (AI) model, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device to:
detect an AI model deployed in a computing environment;
generate a representation of the AI model in a security database, the security database including a representation of the computing environment;
detect in the AI model a first cybersecurity object;
detect in the computing environment a second cybersecurity object;
determine that the AI model is exposed to a toxic combination cybersecurity risk based on the detected first cybersecurity object and the detected second cybersecurity object; and
initiate a mitigation action based on the toxic combination cybersecurity risk.
14. A system for detecting a combined cybersecurity risk for an artificial intelligence (AI) model comprising:
a processing circuitry;
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
detect an AI model deployed in a computing environment;
generate a representation of the AI model in a security database, the security database including a representation of the computing environment;
detect in the AI model a first cybersecurity object;
detect in the computing environment a second cybersecurity object;
determine that the AI model is exposed to a toxic combination cybersecurity risk based on the detected first cybersecurity object and the detected second cybersecurity object; and
initiate a mitigation action based on the toxic combination cybersecurity risk.
15. The system of claim 14 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
initiate the mitigation action based on the first cybersecurity object.
16. The system of claim 14 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
initiate the mitigation action further based on the second cybersecurity object.
17. The system of claim 14 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
generate an inspectable disk based on an original disk of a resource deployed in the computing environment; and
inspect the inspectable disk for any one of:
the AI model, the second cybersecurity object, and a combination thereof.
18. The system of claim 17 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
detect an artifact of the AI model on the inspectable disk.
19. The system of claim 18 , wherein the artifact is any one of:
a file, a software library, a software binary, a folder name, a hash, and any combination thereof.
20. The system of claim 14 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
detect that the AI model is configured to execute a code object.
21. The system of claim 14 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
detect a metadata of the AI model, wherein the metadata indicates that the AI model is associated with a cybersecurity risk.
22. The system of claim 14 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
detect in the AI model any one of:
a secret, a certificate, a code, and any combination thereof.
23. The system of claim 14 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
apply a policy on the representation of the AI model.
24. The system of claim 23 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:
initiate the mitigation action based on a result of applying the policy.
25. The system of claim 14 , wherein the toxic combination cybersecurity risk is any one of:
a vulnerability, an exposure, a misconfiguration, and a combination thereof.