VELOCITY GOVERNANCE FRAMEWORK

10 standards.
Why each one matters when AI is in the loop.

For each standard: why AI integration raises the stakes, what the gap enables, and what good governance looks like at every maturity level.

Score Your TeamsAll 50 Standards
🎯
Why it matters
The specific reason this standard carries higher consequence for AI-accelerated teams — what the gap enables.
⚠️
Risk without governance
Concrete failure modes — not theoretical. These are the production incidents this standard prevents.
📈
Level 3 → Level 5
What good looks like at the target baseline (Defined) and at mature, optimised practice.

Design

2 standards
2.2
Architecture Decision Records
AI integration decisions are irreversible at scale.
2.6
Dependency Management
LLM SDKs ship breaking changes more rapidly than traditional libraries.

Development

4 standards
3.1
Branch Protection
AI-generated code bypassing review is the highest-risk path to production.
3.2
PR Review Quality
AI-generated code looks plausible and passes superficial review.
3.6
Code Ownership
AI integration layers need clear human ownership.
3.9
Secrets Management
LLM API keys grant access to powerful, expensive endpoints.

Testing

2 standards
4.1
CI Pipeline
AI-generated code needs automated validation gates.
4.6
Security Analysis
SAST tools are increasingly aware of AI-specific vulnerabilities.

Release

2 standards
5.7
Rollback Capability
Model behaviour degrades without warning — and without a deployment event to trigger your normal rollback procedures.
5.8
Feature Flagging
Feature flags decouple AI feature activation from deployment.

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