AI systems need governance, not replacement.
Model outputs drift. Behavior changes between versions. Training-time guardrails do not enforce runtime boundaries.
What's missing is deterministic policy enforcement before execution.
ActivePolicy is a policy enforcement boundary that evaluates proposed actions before execution. Policy decisions are deterministic and replayable. Every evaluation produces one of four finite outcomes: ALLOW / CONDITIONAL / ESCALATE / FORBIDDEN, with cryptographic receipts.
Model outputs drift. Behavior changes between versions. Training-time guardrails do not enforce runtime boundaries.
What's missing is deterministic policy enforcement before execution.
ActivePolicy operates as a runtime enforcement gate between AI systems and execution. Proposed actions are evaluated against policy before they proceed.
Models, inference pipelines, and application logic remain unchanged. ActivePolicy governs which actions are permitted to execute.
Action proceeds immediately
Requires human confirmation
Routed to authority
Blocked with reason
Given the same action and policy state, ActivePolicy produces the same decision. Policy evaluation does not drift.
Every policy decision generates a signed receipt. Decisions can be replayed from receipts for audit and compliance verification.
Policy evaluation happens before execution, not after the fact.
ActivePolicy evaluates actions, not model internals. Compatible with any LLM, framework, or inference infrastructure.
ActivePolicy enables AI deployment in healthcare, insurance, finance, and legal—industries where deterministic policy enforcement and audit trails are required.
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