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AI Governance

Guardrails Policy Guide

Design and enforce runtime safety controls for prompt injection defense, PII handling, and fallback behavior.

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Audience: Risk, compliance, and AI operations teamsRead Time: 11 minUpdated: May 2026

Policy design

  • Define prohibited output classes and required refusal patterns per business domain.
  • Classify data sensitivity levels and specify masking or redaction rules per channel.
  • Set confidence thresholds that trigger clarification, fallback, or human escalation.

Runtime enforcement

  • Apply pre-response and post-response checks to catch unsafe transformations.
  • Use context-aware checks for jailbreak attempts and hidden instruction injection.
  • Route policy violations to deterministic safe responses with incident tagging.

Validation and auditability

  • Simulate policies using historical transcripts and adversarial test prompts.
  • Track policy hit rates and false positives by intent category.
  • Maintain immutable policy change history with approver and deployment timestamp.

Operational Checklist

  • OKPII policy and unsafe-content policy are active.
  • OKFallback responses are user-safe and actionable.
  • OKViolation telemetry is visible in dashboards.
  • OKPolicy change log is reviewable for audits.

Next Action

Apply this guide in your workspace and keep notes for your team in one shared SOP so onboarding and support remain consistent.

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