Organizations are increasingly using LLMs to generate policy-as-code (in languages like Rego and Cedar) to speed development, but AI-generated policies can be syntactically correct yet semantically flawed and grant unintended access. Research by Vatsal Gupta highlights recurring failure modes—missing contextual restraints, absent deny logic, hallucinated attributes, dropped temporal conditions, and action misclassification—and urges validation, testing, and deny-by-default controls before deployment. #VatsalGupta #Rego
Keypoints
- LLMs are being adopted to generate policy-as-code for access control and compliance.
- Generated policies often compile but are semantically incorrect and grant excessive permissions.
- Missing contextual restraints and absent deny logic commonly lead to over-permissioned environments.
- Hallucinated attributes and dropped temporal or session conditions cause unpredictable runtime behavior.
- Experts recommend validation layers, testing, and explicit deny-by-default enforcement before deployment.