Capability 04 of 04 · Delivery Management
Governance & Compliance
Controls around AI-generated code - licensing, security scanning, and audit trails.
5
maturity levels
15
practices
15
guides
L1 · Stage 01Ad-hoc
Practices - what it looks like3 guides
- Individual devs use their own AI subscriptionsShadow AI refers to the use of AI tools by developers through personal subscriptions and accounts that operate entirely outside the organization's awareness, approval, or oversight.guide→
- AI usage not yet auditedA zero audit trail state means that when an auditor, security team, or incident investigator asks "what AI systems were involved in producing this code change?" there is no answer.guide→
- AI usage is informal, policy not yet definedIn 2023 and early 2024, many organizations responded to AI coding tools by banning them outright.guide→
Criteria - what to measure
- 01The team knows which AI tools are in use
- 02AI-generated code follows the normal review and merge process
- 03Team is aware of shadow AI usage (developers using private subscriptions)
- 04Organization has moved past "ban AI" as a policy position
L2 · Stage 02Guided
Practices - what it looks like3 guides
- Official AI tool policy; per-session spend caps + kill switches; auto/unattended modes as explicit opt-in (Claude Code defaults to Manual)An official AI tool policy is the organization's first structured governance response to AI in the delivery pipeline.guide→
- Basic audit: who uses whatBasic audit at L2 means the organization has established visibility into which developers are using which AI tools, at what frequency, and for what purposes.guide→
- EU AI Act awareness (Digital Omnibus finalized June 29: high-risk duties deferred to Dec 2027 / Aug 2028); China's agent-specific regulation (effective July 15, world's first); data-residency routingThe EU AI Act (Regulation 2024/1689) is the world's first comprehensive legal framework for artificial intelligence, entering force in August 2024 with phased compliance deadlines now stretching to 2028.guide→
Criteria - what to measure
- 01Official AI tool policy exists and is communicated to all developers
- 02Basic audit tracking is in place (which developers use which AI tools)
- 03EU AI Act awareness training or briefing has been conducted
- 04AI tool policy is reviewed at least annually
- 05Approved tool list is maintained and accessible
L3 · Stage 03Systematic
Practices - what it looks like3 guides
- Minimum viable audit trail: model, timestamp, context, approverThe minimum viable audit trail (MVAT) is the smallest set of structured metadata that, when captured consistently for every AI-assisted change, creates a defensible provenance recoguide→
- Policy-as-code; enterprise managed settings for agent clients (GitHub managed-settings.json GA - "governance is only as strong as its least-covered surface"); lint/review agent config as security-sensitive; mandate model/vendor portability + self-hostable fallbackPolicy-as-code means expressing compliance rules as executable code that runs automatically in the CI/CD pipeline, rather than as documents that developers are expected to read and follow manually.guide→
- Compliance gates in CICompliance gates in CI are automated checks that must pass before a pull request can be merged, specifically focused on governance and compliance requirements rather than functional correctness.guide→
Criteria - what to measure
- 01Minimum viable audit trail is captured per AI-assisted change: model identifier, timestamp, context description, human approver
- 02Policy-as-code enforces compliance rules in CI (OPA or equivalent)
- 03Compliance gates run on every PR to in-scope repositories
- 04Audit trail fields are validated by CI (missing fields fail the build)
- 05Policy exceptions are logged and require follow-up within 48 hours
L4 · Stage 04OptimizedMost teams aim here
Practices - what it looks like3 guides
- Full provenance tracking per change (cryptographic agent traces; commit-to-prompt lineage; agent identity - Entra-bound agents under MXC/Agent 365)Full provenance tracking means that for every change that reaches production, you can reconstruct the complete lineage: the business requirement that originated the work, the tickeguide→
- Automated compliance checks; MCP/skill supply chain governed by sandboxed execution, not static scan (evasion beats scanners >90%); MCP 2026-07-28 authorization + EMAAutomated compliance checks at L4 go beyond the process gates of L3 (did the developer fill in the right fields?) to evaluate substantive compliance questions automatically: does tguide→
- AI code vs human code distinction in VCS (Kubernetes model: disclosure mandatory, AI commit messages banned; humans write the why)AI code vs. human code distinction in version control means that the repository's history explicitly tags which lines, commits, or files were generated by AI systems versus writtenguide→
Criteria - what to measure
- 01Full provenance tracking per change: model version, prompt context, agent session ID, iteration count
- 02Automated compliance checks run without manual intervention on every merge
- 03AI-generated code is distinguishable from human-written code in version control (metadata, labels, or attribution)
- 04Provenance data is queryable (e.g., "show all changes made by model X in the last 30 days")
- 05Compliance check results are aggregated into a governance dashboard
L5 · Stage 05Autonomous
Practices - what it looks like3 guides
- Continuous compliance: agent monitors regulatory changesContinuous compliance with agent-based regulatory monitoring means that an AI agent continuously tracks regulatory changes - new EU AI Act implementing regulations, updated SOC2 guguide→
- Self-documenting audit trailA self-documenting audit trail is one where the documentation of AI involvement in a change is generated automatically by the AI system itself, without requiring human effort to produce it.guide→
- Enterprise-grade RBAC per agent (Stripe Toolshed model: 400+ MCP tools with access control)Enterprise-grade RBAC (Role-Based Access Control) per agent means that every AI agent operating in the organization's systems has an explicit, audited identity with a specific setguide→
Criteria - what to measure
- 01Continuous compliance: agent monitors regulatory changes (EU AI Act updates, SOC2 changes) and proposes policy updates
- 02Audit trail is self-documenting (agent decisions include reasoning, not just outcomes)
- 03Enterprise-grade RBAC is enforced per agent (Stripe Toolshed model: each agent has scoped permissions for specific tools and repositories)
- 04Policy update proposals from compliance agent are auto-tested against existing codebase before rollout
- 05Agent RBAC permissions are audited automatically for least-privilege compliance
From the Field
Recent releases, projects and discussions the AI Radar classified into this capability.
article
Vorim AI – Identity, permissions, and audit trails for AI agents
release
kodustech/kodus-ai
release
crewAIInc/crewAI
release
langchain-ai/langchain
release
Kilo-Org/kilocode
discovered
AtomicBot-ai/Atomic-Chat
discussion
How are you keeping on top of security these days?
discovered
Deterministic CI scanner and surface-risk scoring for MCP (Model Context Protocol) servers.
Which level is your team at in Governance & Compliance?
The criteria above are what we check in a facilitated assessment. Run it yourself to place this capability, see which gates you have passed, and compare it against the other 3 in Delivery Management.
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