Policy Enforcement for LLMs is the active, runtime interception and validation of model responses, context files, and tool invocation parameters against strict security constraints prior to execution. This mechanism ensures that incorrect model inferences or malicious prompt injections are caught and corrected before causing damage.
How Policy Enforcement Works
When an LLM decides to call a tool — for example, querying a database, sending a Slack message, or initiating a financial transaction — the request is intercepted by a policy enforcement layer before it reaches the underlying system. The enforcement layer evaluates the request against a set of rules and returns one of three verdicts:
ALLOW— The action matches the defined policy. Execution proceeds automatically.DENY— The action violates a constraint. Execution is blocked and the agent is returned an error.REVIEW— The action falls within a risk threshold that requires human authorization. Execution is suspended until a designated reviewer approves or rejects it.
What Policies Enforce
Policy rules target the structured metadata of each tool call. Common enforcement dimensions include:
- Tool scope: Restricting which tools a given agent identity is permitted to invoke.
- Parameter bounds: Setting numeric thresholds (e.g. maximum transaction amount) or string pattern constraints (e.g. allowed database table names).
- Rate limits: Capping how many times a tool may be called within a session or rolling time window.
- Data access: Blocking reads against tables or APIs that contain sensitive or regulated data.
- Session budgets: Tracking cumulative impact (e.g. total spend) across a session and triggering review when a budget is exhausted.
Policy Enforcement vs. Output Filtering
Output filtering (e.g. blocking toxic or off-policy text in a model's response) is a complementary but distinct mechanism. Output filtering validates what the model says; policy enforcement validates what the model does. For enterprise deployments where agents have write access to real systems, enforcement at the action layer is the critical control — because the damage caused by an unauthorized database write is not recoverable by filtering the model's subsequent text output.
Deployment Models
- Sidecar: Policy engine runs as a co-located process in the same container pod as the agent. Lowest latency; no external network hop required.
- Gateway: All agent clusters route evaluation requests through a centralized policy service. Best for multi-team deployments where consistent policy management outweighs the latency cost.
- In-process SDK: Policy evaluation is embedded directly in the agent application using a native library. Suitable for single-process deployments or edge environments with no sidecar support.