AI Release Governance Framework

A structured framework for governing the full release lifecycle of AI systems, from initial development through production deployment and ongoing monitoring, with specific guidance for regulated industries.

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The Problem

Most software release frameworks were not designed for AI systems. AI releases differ from traditional software releases in critical ways:

• Non-deterministic behavior — the same model can produce different outputs
• Data dependency — model performance degrades as the world changes
• Emergent risks — failure modes that were not anticipated during development
• Regulatory scrutiny — AI decisions in regulated industries face legal accountability
• Stakeholder impact — errors can affect individuals' health, finances, or rights

This framework addresses each of these differences with structured governance gates.

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Framework Structure

AI Release Lifecycle
│
├── 1. PRE-DEVELOPMENT
│   ├── Use case approval
│   ├── Risk classification
│   └── Data governance review
│
├── 2. DEVELOPMENT
│   ├── Model card initiation
│   ├── Bias evaluation plan
│   └── Security threat model
│
├── 3. PRE-DEPLOYMENT (Release Gates)
│   ├── Technical validation gate
│   ├── Governance approval gate
│   ├── Legal/compliance gate
│   └── Infrastructure readiness gate
│
├── 4. DEPLOYMENT
│   ├── Staged rollout plan
│   ├── Monitoring activation
│   └── Incident response readiness
│
└── 5. POST-DEPLOYMENT
    ├── Performance monitoring
    ├── Drift detection
    ├── Periodic governance review
    └── Retirement / decommissioning

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Release Gates

Gate 1: Technical Validation

Check	Requirement	Tooling
Model performance	Meets accuracy/F1 threshold on holdout set	pytest, MLflow
Bias evaluation	Disparate impact ratio ≥ 0.80 across subgroups	Fairlearn, AI Fairness 360
Adversarial testing	Red team report completed	Microsoft PyRIT, Giskard
Latency / throughput	P99 latency ≤ SLA threshold under load	Locust, k6
Security scan	No critical vulnerabilities in dependencies	Snyk, Dependabot

Gate 2: Governance Approval

Check	Approver	Documentation Required
AI governance review	AI Governance Lead	Signed governance checklist
Risk assessment complete	Risk Officer	Risk register entry
Model card complete	Technical Owner	Published model card
Explainability report	Technical Owner	SHAP/LIME analysis report

Gate 3: Legal & Compliance

Check	Requirement
Regulatory mapping	All applicable regulations identified and addressed
Privacy review	GDPR/CCPA impact assessment for personal data
Legal sign-off	Legal counsel review for high-risk systems
Industry-specific review	HIPAA (healthcare) / SR 11-7 (finance) / state regs

Gate 4: Infrastructure Readiness

Check	Requirement
Monitoring configured	Alerts set for performance degradation and drift
Logging enabled	All inputs/outputs/decisions logged with retention policy
Rollback tested	Rollback to previous version validated in staging
Runbook complete	On-call runbook published and reviewed

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NIST AI RMF Alignment

This framework implements the NIST AI RMF Measure and Manage functions:

• MS.1 — AI risk identification methods applied at each gate
• MS.2 — Ongoing monitoring activated at deployment gate
• MS.3 — Evaluation techniques applied at technical validation gate
• MG.2 — Risk treatment plans completed before governance gate
• MG.4 — Rollback and recovery procedures validated at infrastructure gate

Full mapping: docs/nist-rmf-mapping.md

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Related Tools

Tool	Purpose	Link
Release checklist CLI	Validate release checklist YAML from the command line	release-checklist
Regulated AI starter repository	Starting point for governance-ready AI delivery	regulated-ai
Enterprise governance playbook	Full organizational governance playbook	governance-playbook

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Ecosystem

Repository	Purpose
governance-playbook	End-to-end governance playbook
release-checklist	Release gate framework and CLI
nist-rmf-guide	NIST AI RMF practitioner guide
ai-prism	Curated governance resources

Maintained by Sima Bagheri · Connect on LinkedIn