AI Output Safety Checklist for Production Applications

Use this AI output safety checklist before shipping LLM features to production: screen every user-visible model completion server-side, route IdenticAPI verdicts to allow/review/block actions, render output with encoding or sanitization plus CSP, log moderation metadata safely, and regression-test synthetic unsafe payloads in CI.

Copy this list into your release process. Items assume AI Output Safety or equivalent output moderation — adjust tooling names to match your stack.

1. Threat model and scope

• Document that model completions are untrusted until validated (LLM output as untrusted input)
• List every surface where assistant text appears (web chat, email, tickets, push, PDF export)
• Identify stored vs ephemeral outputs (stored XSS risk)
• Include improper output handling in LLM risk register alongside prompt injection
• Define audiences (consumer, B2B, internal) and content policies per audience

2. Moderation placement

• Output moderation runs after LLM inference, before client delivery, storage, or tool side effects
• Moderation executes server-side only — no production API keys in browser code
• Streaming UIs buffer or gate — no raw token HTML insertion before final check (real-time chat)
• RAG answers moderated as a whole; retrieved snippets moderated if shown directly
• Input moderation considered separately (input vs output)

3. IdenticAPI integration

• POST /api/v1/security/output-safety with body {"text":"..."} wired into chat/RAG path
• Parse verdict (safe, suspicious, unsafe), risk, findings, reasons
• Store request_id with app trace IDs for support
• API key in secrets manager / environment — not in repo
• Timeouts defined with documented fail-closed or degraded mode
• Read AI Output Safety documentation
• Validate sample outputs in AI Output Safety Checker during development

Example request:

curl -X POST https://www.identicapi.com/api/v1/security/output-safety \
  -H "Authorization: Bearer idapi_test_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{"text": "Completion to screen"}'

4. Verdict policy (block vs review vs allow)

• Written policy mapping verdicts to actions (block vs review guide)
• unsafe → block with static fallback — no echo of flagged text
• suspicious → human review or cautious fallback — access-controlled queue
• safe → deliver only after rendering controls (moderation ≠ sanitizer)
• Fallback messages pre-approved by legal/support/comms
• High-stakes domains (support, healthcare adjacent) define escalation SLAs (support bot architecture)

5. Safe rendering

• Default UI renders plain text (encoded) — no unsanitized HTML
• Markdown parsers disable raw HTML or sanitize output
• HTML sanitizer uses strict tag/attribute allowlist (safe AI-generated HTML)
• Block javascript:, inline event handlers, iframe/script unless explicitly required and sandboxed
• Content-Security-Policy on pages displaying AI content (XSS prevention)
• Re-sanitize or re-validate stored messages on read

6. Guardrails and complementary controls

• Prompt injection screening on untrusted inputs where applicable
• PII/secrets detection on logs and outputs if required by compliance
• Tool/agent actions not driven by raw model strings — structured intents with authz
• Evaluate Unified Guard if orchestrating multiple detectors (guardrails vs moderation)

7. Framework implementation

• Next.js: Route Handlers / Server Actions moderate before response (Next.js guide)
• Python: service layer moderates before JSON to clients (Python guide)
• Non-HTTP workers (Celery, queues) moderate before marking jobs complete
• Multiple clients share one policy module — avoid divergent rules

8. Logging and privacy

• Log verdict, risk, categories, request_id — minimize full flagged body in production logs
• Review queue access restricted to trained staff
• Retention policy for blocked/suspicious content documented
• Analytics pipelines do not leak moderated text to third parties without review

9. Testing and regression

• CI tests: synthetic XSS strings, phishing phrasing, benign technical answers
• Assert unsafe payloads never returned to test client
• Assert moderation API failure triggers fail-closed path
• Periodic manual review of sampled suspicious cases in staging/production
• Re-test after model version upgrades or prompt template changes

Synthetic XSS cases to include:

<script>alert(1)</script>
<img src=x onerror=alert(1)>
[click](javascript:alert(1))

10. Operations and incident response

• Runbook for moderation API outage (human-only mode vs read-only chat)
• Alert on abnormal block rate spikes
• Process for customer reports of harmful AI replies
• Post-incident review updates checklist and test corpus
• On-call knows where API status and docs live

11. Documentation and training

• Engineering docs link to What Is AI Output Moderation? and How to Moderate LLM Output
• Support staff trained not to paste blocked model text into customer emails
• Policy owners named for verdict threshold changes

12. Pre-launch sign-off

• Security review of output path complete
• Product accepts false positive / false negative tradeoffs in writing
• Legal/comms reviewed customer-facing fallback language
• No marketing claims of "guaranteed safe AI output"
• All checklist sections above have owners and dates

Quick reference links

Limitations (read before sign-off)

Automated output safety reduces risk but does not eliminate it:

• Verdicts indicate elevated risk, not certainty
• Sanitizers and CSP require maintenance
• Human review remains necessary for ambiguous suspicious content in high-trust products

Treat this checklist as a living document — update when you add new surfaces (mobile app, voice transcripts, exports) or new IdenticAPI capabilities.

When every item has an owner and evidence, you are in a stronger position to ship LLM features without improper output handling gaps.