Implement AI guardrails by defining a policy, checking the relevant input or action, enforcing the decision in application code, and testing what happens when the check fails or is unavailable.
Define the policy before choosing the detector
- Name the prohibited content or action and the audience it applies to.
- Write allowed and disallowed examples, including borderline cases.
- Decide whether the result should be allowed, redacted, blocked, or sent for review.
- Assign an owner for exceptions and policy updates.
Enforce actions outside the model
A safety instruction does not authorize a tool call or protect a database. Apply access checks, argument validation, and data minimization in the application. Decide whether detection runs before generation, before a tool executes, or before the answer reaches the user.
Exercise failure paths
Test a detector timeout, malformed response, missed violation, and false alarm. Choose fail-open or fail-closed behavior for the specific operation and its risks. A fallback that silently bypasses a required control defeats the policy.
Review outcomes
Measure false positives and missed failures on reviewed examples. Log the policy and detector version, enforcement result, and final reviewer decision. Verify supported DriftRail integration methods in the documentation; the pattern described here is an application design checklist.
References and further reading
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.