AI Deployment Validation Checklist

Security Controls Verification

Reviewer: [ROLE TITLE], [DEPARTMENT]

System: ________________________

Date: [DATE]

Authentication and Authorization

• [ ] Authentication is required for all AI system endpoints (no anonymous access)
• [ ] Role-based access control (RBAC) is implemented with least-privilege assignments
• [ ] API keys and service credentials are stored in a secrets management system (not hardcoded)
• [ ] Session management includes timeout, revocation, and concurrent session limits
• [ ] Multi-factor authentication is required for administrative access to the AI system

Network Security

• [ ] AI system endpoints are accessible only through approved network paths (VPN, private endpoints, or IP allowlist)
• [ ] TLS 1.2+ is enforced for all data in transit
• [ ] Data at rest is encrypted using AES-256 or equivalent
• [ ] Network segmentation isolates the AI system from other production systems where appropriate

Prompt Injection Defense

• [ ] Input validation controls are implemented per the Prompt Injection Defense Checklist
• [ ] Output filtering is active and tested
• [ ] System prompt is hardened and extraction-tested
• [ ] Prompt injection testing has been completed with zero high-severity findings

Dependency and Supply Chain

• [ ] All model dependencies, libraries, and frameworks have been scanned for known vulnerabilities
• [ ] Model provenance is documented — training data sources, fine-tuning datasets, and model weights are verified
• [ ] Third-party model licenses have been reviewed by Legal for compliance
• [ ] A software bill of materials (SBOM) has been generated and is available for audit

Performance Benchmarks

Load Testing

• [ ] Load testing has been conducted at 1x, 2x, and 5x expected peak traffic
• [ ] Graceful degradation has been verified — system returns appropriate error responses under overload rather than failing silently or producing incorrect outputs
• [ ] Auto-scaling policies are configured and have been tested
• [ ] Resource limits (CPU, memory, GPU) are defined and enforced to prevent runaway consumption

Quality Metrics

• [ ] Model accuracy on the validation dataset meets the defined threshold: ≥ ___%
• [ ] Model accuracy on the out-of-distribution test set meets the minimum threshold: ≥ ___%
• [ ] Output quality has been evaluated by subject matter experts using a standardized rubric
• [ ] Regression testing confirms that the new deployment does not degrade performance on previously passing test cases

Cost Validation

• [ ] Per-request cost has been calculated and is within budget: $____ per 1K requests
• [ ] Monthly cost projection at expected volume is within approved budget: $____ / month
• [ ] Cost alerting is configured with thresholds at 80% and 100% of monthly budget

Bias Assessment

Assessment conducted by: [ROLE TITLE], [DEPARTMENT]

Assessment date: [DATE]

Data Bias Review

• [ ] Training data demographics have been analyzed and documented
• [ ] Known representation gaps in training data have been identified and their potential impact assessed
• [ ] Data labeling processes have been reviewed for annotator bias
• [ ] Historical bias in training data (reflecting past discriminatory practices) has been evaluated

Model Output Fairness

• [ ] Fairness metrics have been computed across protected categories (race, gender, age, disability, religion, national origin)
• [ ] Disparate impact analysis has been performed — no protected group experiences outcomes at a rate less than 80% of the most favored group (four-fifths rule) unless justified
• [ ] Equal opportunity metrics confirm comparable true positive rates across demographic groups
• [ ] Calibration analysis confirms that confidence scores are equally reliable across groups

Testing Scenarios

• [ ] AI system has been tested with prompts that reference protected characteristics to verify consistent, non-discriminatory responses
• [ ] Edge cases involving intersectional identities (e.g., race + gender + age) have been tested
• [ ] System behavior has been evaluated across multiple languages and cultural contexts relevant to the deployment population
• [ ] Adversarial fairness testing has been conducted — attempts to elicit discriminatory outputs through indirect prompting

Documentation and Transparency

• [ ] A model card or equivalent transparency document has been completed
• [ ] Known limitations and potential bias risks are documented with mitigation strategies
• [ ] Bias assessment results are available for review by affected stakeholders
• [ ] An appeal mechanism exists for individuals who believe they have been subjected to biased AI decisions

Fairness Review Outcome: ☐ Pass ☐ Conditional Pass (with mitigations) ☐ Fail

Privacy Impact Assessment

• [ ] A Privacy Impact Assessment (PIA) or Data Protection Impact Assessment (DPIA) has been completed for this AI system
• [ ] The PIA/DPIA has been reviewed and approved by [ROLE TITLE] (Privacy Officer or Data Protection Officer)
• [ ] All personal data processing activities have a documented lawful basis (consent, legitimate interest, contractual necessity, etc.)

Data Minimization

• [ ] The AI system collects only the minimum personal data necessary for its stated purpose
• [ ] Data retention periods are defined and automated deletion is configured
• [ ] Personal data is not used for secondary purposes without additional consent or legal basis
• [ ] Anonymization or pseudonymization is applied where full identification is not required

Data Subject Rights

• [ ] Mechanisms exist for data subjects to exercise their rights: access, rectification, erasure, portability, objection
• [ ] Response procedures for data subject requests are documented with defined SLAs
• [ ] The AI system can identify and extract all personal data associated with a specific individual upon request
• [ ] The right to human review of automated decisions is available and documented

Cross-Border Data Transfers

• [ ] Data processing locations have been identified and documented
• [ ] Cross-border data transfers comply with applicable requirements (Standard Contractual Clauses, adequacy decisions, etc.)
• [ ] Data residency requirements are met — data classified as requiring in-country storage is not processed outside approved jurisdictions

Vendor Privacy

• [ ] Data Processing Agreements are executed with all AI vendors who process personal data
• [ ] Vendor DPAs include: data processing purpose limitations, sub-processor controls, breach notification obligations, and data deletion requirements
• [ ] Vendor privacy practices have been validated through questionnaire, audit, or certification review

Privacy Review Outcome: ☐ Approved ☐ Approved with Conditions ☐ Not Approved

Deployment Approval Record

AI System Name: ________________________

Version / Release: ________________________

Target Environment: ☐ Production ☐ Staging ☐ UAT

Planned Deployment Date: [DATE]

Deployment Window: [TIME] to [TIME] [TIMEZONE]

Stakeholder Approvals

Conditional Approvals

If any approver has granted conditional approval, document the conditions here:

Deployment Authorization

All required approvals obtained: ☐ Yes ☐ No

All conditional items resolved: ☐ Yes ☐ No ☐ N/A

Deployment authorized by: ________________ (must be AI System Owner or delegate)

Authorization date: [DATE]

Note: Deployment must not proceed unless all required approvals are obtained and all conditions are resolved. Any stakeholder may revoke their approval at any time before deployment execution by notifying the AI System Owner in writing.

Rollback Strategy

Rollback Type: ☐ Blue-Green ☐ Canary ☐ Feature Flag ☐ Manual Revert ☐ Other: ____________

Rollback Triggers

Immediate rollback (automated or manual within 15 minutes) must be executed if any of the following conditions are observed:

• [ ] Error rate exceeds ___% (baseline: ___%) for more than 5 consecutive minutes
• [ ] Average latency exceeds ___ms (baseline: ___ms) for more than 5 consecutive minutes
• [ ] Security monitoring detects active exploitation or data exfiltration
• [ ] AI outputs contain consistently harmful, biased, or nonsensical content
• [ ] Customer-facing impact reports exceed ___ per hour
• [ ] Model confidence scores drop below ___% on standard validation queries

Rollback Procedure

Rollback Validation

• [ ] Rollback procedure has been tested in staging within the last 30 days
• [ ] Rollback can be executed within ___ minutes (target: under 15 minutes)
• [ ] Previous model version / system state is verified and available for immediate deployment
• [ ] Database schema changes (if any) are backward-compatible and do not block rollback
• [ ] Rollback does not result in data loss for transactions processed by the new version

Post-Rollback Actions

1. Conduct immediate incident triage meeting (within 2 hours of rollback)
2. Document the failure mode, impact assessment, and root cause
3. Update the deployment checklist to prevent recurrence
4. Re-enter the deployment approval process before the next attempt
5. Notify affected customers if the issue resulted in degraded service or incorrect outputs

Rollback Plan Verified By: ________________

Date: [DATE]