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General

AI Policy Enforcement

Every serious AI system operates under rules, whether they come from law, company policy, product scope, or user trust requirements. Policy enforcement is the runtime machinery that turns those rules into actual behavior.

Examples of Enforced Policies

  • Blocking disallowed requests
  • Restricting access to sensitive tools or data
  • Requiring citations for certain answer types
  • Forcing human review before critical actions
  • Logging and auditing high-risk interactions

Why This Is Hard

Policies are often written in natural language, while software needs precise conditions. Translating broad standards into consistent technical controls is one of the most important parts of AI system design.

Good Enforcement

Effective policy enforcement is visible in the system architecture, not buried in a single prompt. Durable controls live in code, permissions, workflows, and review processes.