"LLMs are being introduced into engineering workflows. Developers are using them to generate infrastructure code, security rules, and now even access control policies."
"Instead of writing policy logic manually, teams can describe intent in plain language and let the model generate the enforcement logic."
"LLM-generated policies are often syntactically valid but semantically incorrect. One missing condition, a misinterpreted attribute, or an incorrect action can completely redefine who gets access to what."
The shift to policy as code is accompanied by increased use of AI, particularly LLMs, to generate code for organizational security and compliance. While AI improves efficiency, it can produce policies that appear correct but grant inappropriate access. These issues arise from AI's tendency to favor simpler solutions over secure ones. Vatsal Gupta's research indicates that LLM-generated policies often have semantic errors that do not trigger alerts but can significantly alter access permissions, revealing recurring failure patterns in policy generation.
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