Executive Summary
This evidence pack turns AI security posture into buyer-ready proof. It collects the system facts, control status, ownership, evidence artifacts, and questionnaire answers an enterprise security reviewer will ask for before approving an AI-enabled product. The key message is simple: enterprise review does not reward ambition. It rewards evidence.
Buyer readiness decision
The product can enter serious enterprise review once the retrieval authorization evidence, agent permission matrix, model provider boundary statement, and AI trace retention policy are completed.
Evidence Pack Snapshot
Commercial context
Buyer question map
| Buyer question | Evidence artifact | Owner | Status |
|---|---|---|---|
| Is customer data used to train foundation models? | Model provider boundary statement | Vendor Management | Draft |
| Can retrieval bypass authorization? | RAG authorization test plan | Search Platform | Partial |
| Can the AI system take actions? | Agent Tool Permission Matrix | AI Platform Engineering | Partial |
| What human oversight exists? | Approval context bundle | Product Operations | Partial |
| Can AI interactions be audited? | AI trace schema | Security Engineering | Implemented |
| How long are prompts retained? | AI trace retention policy | Security Engineering | Planned |
Enterprise AI Security Evidence Pack
The evidence pack tracks implementation status, owners, control categories, buyer questions, and source evidence. It should be the reusable source of truth for customer security reviews.
Readiness Findings
The evidence gap is now commercial
The product team can explain many controls verbally, but several answers are not yet backed by clean buyer-facing evidence. That creates unnecessary procurement drag.
Retrieval authorization needs proof, not intent
Enterprise reviewers will not accept architecture intent alone. The company needs test evidence showing that authorization survives retrieval, reranking, and prompt assembly.
Agent authority needs a precise answer
The buyer question is not whether the product uses agents. The buyer question is what the agent can do, under whose authority, with what approval, and with what audit trail.
AI trace retention is not yet buyer-ready
Prompts, outputs, retrieval references, and tool-call records need explicit retention and access-control language before the company can answer security questionnaires cleanly.
Control Evidence Map
The control map connects AI-specific buyer questions to implemented controls, partial controls, missing controls, evidence artifacts, and accountable owners.
This is not a policy binder
Sample questionnaire answer bank
| Question | Answer posture | Evidence | Owner |
|---|---|---|---|
| Is customer data used for model training? | Draft answer ready for legal review | Provider data-use statement | Vendor Management |
| Are AI outputs logged? | Yes, through AI trace schema | AI trace schema | Security Engineering |
| Can AI actions be audited? | Partially, pending permission matrix completion | Tool-call trace design | AI Platform |
| Are prompts retained? | Policy in progress | Trace retention policy draft | Security Engineering |
| Are retrieval results permissioned? | Designed, not fully proven | RAG authz test plan | Search Platform |
Evidence required before enterprise review
Sales enablement decision
Do not let sales answer AI security questionnaires from scratch. Use this pack as the controlled answer source, and route unanswered buyer questions back into the evidence backlog.
Evidence remediation roadmap
| Priority | Work item | Owner | Buyer value |
|---|---|---|---|
| 1 | Complete retrieval authorization tests | Search Platform | proves RAG does not bypass access |
| 2 | Finalize agent permission matrix | AI Platform | clarifies agent authority |
| 3 | Approve provider boundary statement | Vendor Management / Legal | answers training and data-use questions |
| 4 | Finalize AI trace retention policy | Security Engineering | answers prompt/output retention questions |
| 5 | Publish AI incident playbook | Security Operations | shows operational readiness |
Operating instructions
Related artifact: AI Trust Boundary Map
The trust boundary map supplies the architecture and data-flow evidence that makes this pack credible.