Guide · Safety
AI Safety Eval Gates and Refusal Monitoring
A written refusal policy without release gates and live signals will rot. Gate on safety evals, separate them from security jailbreak corpora, and monitor refusals online.
Safety policy without gates and live refusal signals fails Safety & Responsible AI—block bad releases and watch production. Primary control: Safety & Responsible AI
Policy without a gate is a wish
APRF Safety & Responsible AI expects safety eval suite definitions, recent gate results, and online signals—not only a PDF. Teams that ship prompt or model changes without a safety suite learn about regressions from users or press.
A product once "reviewed" refusals manually before big launches. A mid-cycle prompt tweak shipped on a Friday. Self-harm-adjacent compliance dropped; nobody saw it until Monday support volume. A blocking safety job on every prompt digest would have caught it.
Offline gate
1. Corpus: domain harm categories + known bypass attempts aimed at your refusals. Keep it separate from pure adversarial security/jailbreak sets so owners and thresholds stay clear.
2. Graders: automated scorers calibrated against human labels on a schedule.
3. Block release when high-severity classes regress or coverage drops below the agreed floor.
4. Record suite version, model/prompt IDs, and pass rates next to the deploy artifact.
Online monitoring
Track refusal rates by policy ID, false-refuse complaints, safety-classifier error/fail-open counts, and tool-denies for safety reasons. Alert on sudden refusal collapse (possible jailbreak wave or prompt bug) and on spike-to-100% refuse (possible over-block or bad deploy). Retain minimal fields—category, policy ID, request ID—not full sensitive prompts by default.
Pair with Incident Readiness: a safety regression is a SEV with rollback of prompt, model, or filter config. Pair with Evaluation for quality gates so you do not "fix" safety by destroying product usefulness without measuring either.
Next: Safety & Responsible AI
Open the related pillar specification for mandatory checks, artifacts, and pass conditions. Self-attest is optional.
Related
- AI Content Safety and Refusal Policies in Production
- Safety Filters Must Cover Tool Side Effects
- AI Evaluation Gates Before Production Release
- Fairness and Disparity Testing for High-Stakes AI
- AI Incident Response and SEV Playbooks
- AI Decision Traces and Citations for Audit
- Safety & Responsible AI
- Evaluation
Frequently asked questions
- Can I reuse my red-team jailbreak set as the safety suite?
- Overlap is fine; ownership should not blur. APRF recommends separating safety/harm corpora from adversarial security sets so thresholds and reviewers stay appropriate.
- What if refusal rate suddenly drops?
- Treat it as a possible safety regression or attack. Page the owner, freeze related deploys, and compare prompt/model/filter versions using request IDs.
- Do chat-only products need tool safety evals?
- If any tool or plugin can run, yes. If the surface is pure text with no side effects, focus on completion and disclosure suites—and re-open the question when tools appear.