# AI Governance Evidence Matrix
Sample Deliverable
Executive Summary
This matrix connects AI governance controls to the evidence that proves them. It is designed for enterprise procurement, security review, audit preparation, trust-center work, and executive reporting.
The key idea is simple: a control without evidence is a claim. The matrix shows each control, owner, status, evidence artifacts, buyer question, and refresh rule.
Heads up
Public sample notice
This is a shortened, synthetic excerpt prepared as a public sample. A client version would include system-specific evidence, implementation references, architecture screenshots, control test results, owner sign-offs, and full supporting documentation. This sample uses Northstar Support Cloud / Customer Support Copilot as the synthetic reference system.
This sample is not legal advice, not a compliance certification, not an audit opinion, not a warranty, and not proof that any unreviewed system is secure.
Decision · conditional
Evidence matrix decision
Use the evidence matrix as the source of truth for AI governance claims. Do not publish or reuse buyer-facing answers unless the corresponding evidence row is current.
Metrics
Evidence Matrix Snapshot
Evidence domains
7
Controls mapped
13
Buyer questions mapped
13
Partial controls
9
Planned controls
1
Note
Governance proof is a graph, not a binder
The buyer asks a question. The answer points to a control. The control points to evidence. The evidence has an owner, status, freshness rule, and remediation path.
Evidence matrix
Control map
AI Governance Evidence Matrix
The matrix maps AI governance controls to evidence artifacts, buyer questions, owners, status, and freshness rules.
AI system inventory
implemented
"Do you know which AI systems are in use and who owns them?"
AI risk tiering
partial
"How do you classify AI risk and apply controls?"
AI trust boundary mapping
implemented
"Where does customer data cross AI system, provider, retrieval, or tool boundaries?"
Model provider boundary statement
partial
"Is customer data used for model training, retained by providers, or shared with subprocessors?"
RAG authorization controls
partial
"Can users receive information through AI that they cannot access directly?"
Source trust and instruction isolation
partial
"Do you test for prompt injection through retrieved content?"
Agent tool inventory
partial
"What systems can the AI agent access and what actions can it perform?"
Agent permission matrix
partial
"Which AI actions are allowed, blocked, conditional, or human-approved?"
Control-to-evidence table
Control-to-evidence map
| Control | Domain | Status | Owner | Evidence |
|---|---|---|---|---|
| AI system inventory | Governance | Implemented | Product Security | AI System Inventory |
| AI risk tiering | Governance | Partial | Product Security | Maturity Scorecard, Operating Model |
| AI trust boundary mapping | Architecture | Implemented | AI Platform Engineering | Trust Boundary Map, Architecture Review |
| Model provider boundary statement | Enterprise readiness | Partial | Vendor Management and Legal | Provider Boundary Statement |
| RAG authorization controls | Data access | Partial | Search Platform | RAG Authorization Review, RAG Test Plan |
| Source trust and instruction isolation | Testing | Partial | Product Security | RAG Test Plan, Red-Team Findings |
| Agent tool inventory | Agentic controls | Partial | AI Platform Engineering | Tool Inventory, Permission Matrix |
| Agent permission matrix | Agentic controls | Partial | AI Platform Engineering | Permission Matrix, Release Gate |
| Approval context bundles | Agentic controls | Partial | Product Operations | Permission Matrix, Red-Team Findings |
| AI traceability | Operations | Implemented with gap | Security Engineering | Trace Schema, Evidence Appendix |
| AI release gate | Operations | Partial | Product Security | Release Gate, Remediation Roadmap |
| AI incident response playbook | Operations | Planned | Security Operations | Incident Response Playbook |
| Enterprise AI answer bank | Enterprise readiness | Partial | Trust and Security | Answer Bank, Buyer FAQ |
Buyer questions
Buyer questions mapped to controls
| Buyer question | Control |
|---|---|
| Do you know which AI systems are in use and who owns them? | AI system inventory |
| How do you classify AI risk and apply controls? | AI risk tiering |
| Where does customer data cross AI boundaries? | AI trust boundary mapping |
| Is customer data used for model training? | Model provider boundary statement |
| Can users receive restricted data through AI? | RAG authorization controls |
| What tools can the AI agent access? | Agent tool inventory |
| Which AI actions require approval? | Agent permission matrix |
| Can AI behavior be audited after an incident? | AI traceability |
| Do AI changes pass security review? | AI release gate |
| How do you handle AI-specific incidents? | AI incident response playbook |
Findings
Findings
Evidence Matrix Findings
Finding · high
Most governance controls still have partial evidence
Evidence: ai-governance-evidence-matrix
The matrix shows a functional governance structure, but many controls are still partial rather than validated.
Finding · medium
Buyer answers need freshness rules
Evidence: enterprise-ai-security-questionnaire-answer-bank
Customer-facing answers should refresh after changes to providers, retrieval sources, tools, trust-center claims, or retention policy.
Finding · medium
Incident response is still planned
Evidence: ai-incident-response-playbook
AI incident response exists as an intended artifact but needs tabletop validation before strong buyer claims.
Freshness rules
Checklist
Evidence refresh triggers
✓Refresh provider evidence after any model provider or route change.
✓Refresh RAG evidence after any retrieval source, index, or reranker change.
✓Refresh agent evidence after any tool, credential, or action policy change.
✓Refresh buyer answers after any trust-center or procurement claim changes.
✓Refresh trace evidence after any logging, retention, access, or redaction change.
Artifact
Related artifact: Enterprise AI Security Evidence Pack
The evidence pack packages these controls for enterprise review.
/deliverables/enterprise-ai-security-evidence-pack
Artifact
Related artifact: AI Control Gap Assessment
The control gap assessment explains which mapped controls are missing, partial, implemented, or validated.
/deliverables/ai-control-gap-assessment