release-governance
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.
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.
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
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 |
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
Related Tools
| Tool | Purpose | Link |
|---|---|---|
airc CLI |
Validate release checklist YAML from command line | ai-release-readiness-checklist |
| Regulated AI Starter Kit | Template repo with pre-configured governance | regulated-ai-starter-kit |
| Enterprise Governance Playbook | Full organizational governance playbook | enterprise-ai-governance-playbook |
Ecosystem
| Repository | Purpose |
|---|---|
| enterprise-ai-governance-playbook | End-to-end governance playbook |
| ai-release-readiness-checklist | Release gate framework + CLI |
| nist-ai-rmf-implementation-guide | NIST AI RMF practitioner guide |
| awesome-ai-governance | Curated governance resources |
Maintained by Sima Bagheri · Connect on LinkedIn