How long does a thorough AI launch review take?

Plan for four weeks minimum. The safety review and red-teaming alone typically take 1-2 weeks when you account for finding and fixing issues. Compliance review depends on your legal team's availability but usually takes at least a week. Rushing this process creates risk.

Can I skip red-teaming for a low-risk AI feature?

No. Even seemingly low-risk features can be abused in unexpected ways. The scope of red-teaming can be smaller for lower-risk features, but you should always conduct some adversarial testing. A half-day session with 2-3 people is the minimum.

What if my AI provider already handles safety?

Your provider handles safety for their platform, not for your specific use case. Their content filters may not cover your product's specific requirements. Their acceptable use policy may not match yours. You are responsible for the AI behavior in your product regardless of who provides the model.

How do I prioritize which checklist items to focus on?

Start with items that protect users from harm (safety review, content policies, crisis response). Then focus on items that protect your business (rollback capability, monitoring, incident response). Finally, address items that optimize quality (bias audits, quality monitoring, compliance). ---

AI Product Launch Checklist

What This Template Does

Launching an AI product carries risks that traditional product launches do not. A standard launch checklist covers feature completeness, performance, and marketing readiness. An AI product launch also requires safety reviews, adversarial testing, bias audits, monitoring for model drift, incident response plans for AI-specific failures, and compliance with emerging AI regulations. Skipping any of these can result in reputational damage, user harm, or regulatory penalties.

This checklist provides a structured gate review for AI product launches. It is organized into phases that map to the weeks before launch, with specific verification items for each. Every item includes what to check, who is responsible, and what a passing result looks like. Use it as a go/no-go framework for any AI product or feature release.

Direct Answer

An AI Product Launch Checklist is a structured pre-launch verification framework that covers safety review, red-teaming, monitoring configuration, rollback procedures, compliance checks, and operational readiness. This template ensures your AI product is safe, monitored, and recoverable before it reaches users.

Template Structure

1. Safety and Responsible AI Review (4 weeks before launch)

Purpose: Verify that the AI product meets safety standards and does not pose unacceptable risks to users.

## Safety Review

### Content Safety
- [ ] **Content policy defined**: Document what the AI must never generate (hate speech, violence, PII, medical/legal advice, etc.)
- [ ] **Content filters implemented**: Input and output filters block prohibited content categories
- [ ] **Content filter tested**: Run [N] adversarial prompts through filters, zero bypasses on critical categories
- [ ] **Refusal messages reviewed**: When the AI refuses a request, the message is clear, respectful, and non-judgmental
- [ ] **Content escalation path defined**: Process for handling content policy violations that reach users

### Bias and Fairness Audit
- [ ] **Demographic test suite created**: Test cases covering gender, race, age, disability, religion, and nationality
- [ ] **Bias evaluation completed**: Run full test suite, document results with variance by demographic group
- [ ] **Acceptable variance threshold met**: [< X% performance difference across demographic groups]
- [ ] **Bias mitigation applied**: For any groups below threshold, mitigations are implemented and re-tested
- [ ] **Ongoing bias monitoring planned**: Automated checks will run [frequency] post-launch

### User Safety
- [ ] **Vulnerable user scenarios tested**: How the AI behaves when users express distress, self-harm, or crisis
- [ ] **Crisis response implemented**: AI provides appropriate resources (hotline numbers, professional referrals) when needed
- [ ] **Age-appropriate content verified**: If minors may use the product, content is appropriate
- [ ] **Misinformation risk assessed**: For domains where incorrect AI output could cause harm, safeguards are in place

### Safety Review Sign-off
- **Reviewer**: [Name and role]
- **Review date**: [Date]
- **Result**: [Pass / Conditional pass with mitigations / Fail]
- **Conditions**: [List any conditions for a conditional pass]

2. Red-Teaming and Adversarial Testing (3 weeks before launch)

Purpose: Attempt to break the AI system through adversarial testing to discover vulnerabilities before attackers do.

## Red-Teaming

### Prompt Injection Testing
- [ ] **Direct injection tested**: Attempts to override system prompts via user input
- [ ] **Indirect injection tested**: Malicious instructions embedded in documents, URLs, or context data the AI processes
- [ ] **Prompt leaking tested**: Attempts to extract system prompts, internal instructions, or training data
- [ ] **All critical injection vectors mitigated**: Zero successful prompt overrides on critical behaviors

### Jailbreak Testing
- [ ] **Known jailbreak patterns tested**: [N] known techniques attempted (DAN, role-playing, encoding tricks)
- [ ] **Novel jailbreak attempts**: [N] creative attempts by red team members
- [ ] **Jailbreak success rate**: [< X% success rate, 0% on critical safety categories]
- [ ] **Jailbreak mitigations documented**: For each successful attempt, mitigation applied and re-tested

### Data Exfiltration Testing
- [ ] **User data extraction tested**: Can the AI be tricked into revealing other users' data?
- [ ] **System data extraction tested**: Can the AI be tricked into revealing API keys, internal URLs, or infrastructure details?
- [ ] **Training data extraction tested**: Can the AI be tricked into reproducing training data verbatim?
- [ ] **Zero successful exfiltration**: All extraction attempts blocked

### Abuse Scenario Testing
- [ ] **Spam and fraud scenarios tested**: Can the AI be used to generate spam, phishing content, or fraudulent materials?
- [ ] **Automation abuse tested**: Can the AI be used to automate harmful activities at scale?
- [ ] **Circumvention of product limits tested**: Can the AI be used to bypass paywalls, rate limits, or access controls?
- [ ] **Abuse mitigations documented**: For each viable abuse scenario, mitigations are in place

### Red Team Summary
- **Red team lead**: [Name]
- **Team size**: [N people]
- **Duration**: [Hours/days]
- **Critical findings**: [N]
- **Findings resolved**: [N/N]
- **Residual risk accepted by**: [Name and role]

3. Monitoring and Observability Setup (2 weeks before launch)

Purpose: Ensure you can detect problems quickly after launch and have the data to diagnose them.

## Monitoring Setup

### Real-Time Metrics
- [ ] **Latency monitoring configured**: p50, p95, p99 latency tracked per endpoint
- [ ] **Error rate monitoring configured**: 4xx and 5xx rates tracked with alerting
- [ ] **Throughput monitoring configured**: Requests per second with capacity planning thresholds
- [ ] **Cost monitoring configured**: Per-request cost tracking with daily and monthly budget alerts
- [ ] **Rate limit monitoring configured**: Track proximity to AI provider rate limits

### Quality Monitoring
- [ ] **Output quality sampling configured**: Automatically sample [N%] of AI outputs for review
- [ ] **User feedback collection live**: Thumbs up/down or rating mechanism on AI outputs
- [ ] **Hallucination detection configured**: Automated checks for common hallucination patterns
- [ ] **Content policy violation tracking**: Count and categorize policy violations that reach users
- [ ] **Drift detection configured**: Alert when output distribution shifts significantly from baseline

### Alerting
- [ ] **Critical alerts configured**: [List alert conditions and who gets paged]
- [ ] **Warning alerts configured**: [List alert conditions and notification channel]
- [ ] **Alert escalation path documented**: [Who gets notified at each severity level]
- [ ] **Alert runbooks written**: For each critical alert, a runbook describes diagnosis and remediation steps

### Dashboards
- [ ] **Operations dashboard**: Latency, error rate, throughput, cost in one view
- [ ] **Quality dashboard**: User feedback scores, sampled output quality, policy violations
- [ ] **Business dashboard**: Feature adoption, user satisfaction, impact on core metrics

4. Rollback and Incident Response (1 week before launch)

Purpose: Ensure you can quickly recover from problems and have a clear process for AI-specific incidents.

## Rollback and Incident Response

### Rollback Capability
- [ ] **Feature flag configured**: AI feature can be disabled instantly without deployment
- [ ] **Feature flag tested**: Verified that disabling the flag removes all AI functionality cleanly
- [ ] **Rollback does not break the product**: Product remains fully functional with AI feature disabled
- [ ] **Rollback authority documented**: [Names] can trigger rollback without additional approvals
- [ ] **Rollback communication template ready**: Pre-written message for users if feature is rolled back

### Incident Response Plan
- [ ] **AI incident severity levels defined**: What constitutes P1, P2, P3 for AI-specific issues
- [ ] **On-call rotation established**: [Team/individuals] covering the first [N] weeks post-launch
- [ ] **Incident communication plan ready**: Templates for user notification, internal communication, and public statements
- [ ] **Post-incident review process defined**: How AI incidents will be analyzed and learnings applied

### AI-Specific Incident Scenarios
| Scenario | Severity | Immediate Action | Resolution Owner |
|----------|----------|-------------------|-----------------|
| AI generates harmful content | P1 | Disable feature immediately | [Name] |
| AI leaks user data | P1 | Disable feature, assess scope | [Name] |
| AI quality drops significantly | P2 | Investigate, consider rollback | [Name] |
| AI provider outage | P2 | Fallback to non-AI experience | [Name] |
| Cost spike beyond budget | P3 | Rate limit, investigate cause | [Name] |
| User complaints about AI quality | P3 | Sample and review, tune prompts | [Name] |

5. Compliance and Legal Review (2 weeks before launch)

Purpose: Verify that the AI product meets legal and regulatory requirements.

## Compliance Review

### Data Privacy
- [ ] **Privacy impact assessment completed**: Document reviewed and approved by legal
- [ ] **Data processing agreements in place**: With all AI providers and sub-processors
- [ ] **User consent mechanisms implemented**: For data collection, AI processing, and data sharing
- [ ] **Right to deletion implemented**: Users can request removal of their data from AI systems
- [ ] **Data residency requirements met**: Data stays in required geographic regions

### AI-Specific Regulations
- [ ] **AI disclosure requirements met**: Users are informed they are interacting with AI
- [ ] **Transparency requirements met**: Users can understand why AI made specific decisions (where applicable)
- [ ] **EU AI Act classification assessed**: Risk level determined and requirements identified
- [ ] **Industry-specific regulations reviewed**: Healthcare, finance, education, or other sector requirements

### Terms and Policies
- [ ] **Terms of service updated**: Cover AI feature usage, limitations, and user responsibilities
- [ ] **Privacy policy updated**: Reflect AI data processing, third-party AI providers, and data usage
- [ ] **Acceptable use policy updated**: Define prohibited uses of the AI feature
- [ ] **AI-specific disclosures published**: How AI is used, what data it processes, its limitations

### Legal Sign-off
- **Reviewer**: [Name and role]
- **Review date**: [Date]
- **Result**: [Approved / Approved with conditions / Not approved]

6. Operational Readiness (1 week before launch)

Purpose: Verify that the team and infrastructure are ready to support the AI product in production.

## Operational Readiness

### Infrastructure
- [ ] **Capacity tested**: Load tested at [X]x expected launch traffic
- [ ] **Auto-scaling configured**: For AI-dependent services
- [ ] **Redundancy verified**: No single points of failure in AI pipeline
- [ ] **Backup AI provider ready**: If primary provider goes down (if applicable)

### Team Readiness
- [ ] **Support team trained**: Customer support can answer common AI feature questions
- [ ] **Support escalation path defined**: How AI-specific issues reach the product/engineering team
- [ ] **FAQ and help documentation published**: For users of the AI feature
- [ ] **Internal communication sent**: All teams aware of launch date and feature capabilities

### Go-Live Checklist
- [ ] **All previous sections signed off**: Safety, red-teaming, monitoring, rollback, compliance
- [ ] **Launch announcement prepared**: Blog post, email, in-app notification
- [ ] **Rollout percentage confirmed**: Starting at [X%] of users
- [ ] **War room scheduled**: Team available for first [N] hours after launch
- [ ] **Success metrics dashboard visible**: Everyone can see launch metrics in real time

How to Use This Template

Begin working through this checklist at least 4 weeks before your target launch date. AI launches require more preparation time than traditional features because safety reviews, red-teaming, and compliance checks take time and often surface issues that need remediation.

Assign owners to each section. Safety review should be owned by a senior engineer or AI safety specialist. Red-teaming should involve people outside the feature team. Compliance should involve legal counsel. Do not assign all sections to one person.

Treat any unchecked item in sections 1-4 as a launch blocker. Safety, red-teaming, monitoring, and rollback are non-negotiable. If you cannot check every box, delay the launch.

Run through the checklist in a formal review meeting. Walk through each item with the responsible person confirming completion. Document any exceptions or accepted risks with explicit sign-off from a senior leader.

Keep this checklist as a living document post-launch. Some items (bias monitoring, quality sampling) are ongoing. Update and re-run relevant sections whenever you make significant changes to the AI component.

Tips for Best Results

Schedule red-teaming as a dedicated event. Assign 3-5 people a half-day to try to break the AI system. Include people from outside the product team -- they bring fresh perspectives and different attack strategies.

Set up monitoring before you need it. Configuring dashboards and alerts after launch means you are flying blind during the most critical period. Everything should be working and generating data at least a week before launch.

Write incident runbooks in advance. When something goes wrong at 2 AM, you do not want the on-call engineer figuring out the response plan from scratch. Pre-written runbooks save time and reduce errors.

Do not skip the compliance review. AI regulations are evolving rapidly. What was acceptable six months ago may now require additional disclosures or safeguards. Legal review catches requirements you may not be aware of.

Practice the rollback procedure. Do not just document it -- actually execute it in a staging environment. Verify the product works correctly with the AI feature disabled. Time how long the rollback takes.

Plan for the post-launch monitoring burden. AI products require more ongoing attention than traditional features. Budget engineering time for weekly quality reviews, prompt tuning, and evaluation suite maintenance for at least the first three months.

Key Takeaways

AI product launches require safety reviews, red-teaming, and compliance checks that traditional launches do not

Set up monitoring and rollback capability before launch -- not after problems emerge

Red-teaming should involve people outside the feature team who bring fresh attack perspectives

Treat unchecked safety and rollback items as hard launch blockers

AI products require ongoing monitoring and quality management beyond the initial launch period

About This Template

Created by: Tim Adair

Last Updated: 2/9/2026

Version: 1.0.0

License: Free for personal and commercial use