Enterprise AI Security Evidence Pack

# Enterprise AI Security Evidence Pack

AI Security Sales Enablement

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, procurement team, or legal counsel will ask for before approving an AI-enabled product for use.

Enterprise review does not reward ambition. It rewards evidence.

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, remediation dates, and full questionnaire answer sets. 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

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. Four primary blockers remain before the pack is fully buyer-ready.

Metrics

Evidence Pack Snapshot

Buyer-ready controls

Partial controls

Missing controls

Planned controls

Primary blockers

Note

What this artifact is for

Sales teams answering AI security questionnaires without a controlled evidence source introduce legal and commercial risk. This pack gives security, legal, product, and sales the same approved answers, evidence pointers, and ownership model. Only approved answers should be shared with buyers.

What this pack answers

Buyer question map

Buyer question	Category	Evidence artifact	Owner	Status
Is customer data used to train foundation models?	Data use / privacy	Model provider boundary statement	Vendor Management	Draft — legal review pending
Can retrieval bypass authorization?	Data protection	RAG authorization test plan + test results	Search Platform	Partial — test execution needed
Can the AI system take actions in our environment?	Agentic controls	Agent Tool Permission Matrix	AI Platform Engineering	Partial — matrix in draft
What human oversight exists for sensitive actions?	Oversight	Approval workflow design + audit log	Product Operations	Partial — approval flow implemented, audit log pending
Can AI interactions be audited and reconstructed?	Observability	AI trace schema + trace access policy	Security Engineering	Implemented
How long are prompts and AI outputs retained?	Data retention	AI trace retention policy	Security Engineering	Planned — policy in draft
What AI security testing has been performed?	Security testing	AI red team scope + findings register	Product Security	Partial — scope complete, testing in progress

Evidence pack

Enterprise AI Security Evidence Pack

The evidence pack tracks implementation status, owners, control categories, buyer questions, and source evidence. It is the reusable source of truth for customer security reviews. Keep it owned by Trust and Security and update it after each AI architecture change or security assessment.

Synthetic sample evidence pack for answering enterprise AI security review, procurement, legal, and trust-center questions. Tracks control status, evidence ownership, and approved questionnaire answers for the Northstar Support Cloud / Customer Support Copilot system.

implemented

partial

missing

planned

retrieval authorization evidenceagent permission matrix completionAI trace retention and access policybuyer-ready model provider boundary statement

AI system inventory

implemented

strong

Product Security"What AI features are in scope for this product?"

Model provider boundary statement

partial

weak — draft only, not legally reviewed

Vendor Management"Is customer data used to train third-party models?"

Gateway-only model access

implemented

strong

AI Platform Engineering"Can product services call the model provider directly?"

Authorization-preserving retrieval

partial

weak — design only, no test execution

Search Platform"Can the AI system retrieve data the user cannot access directly?"

Prompt injection and retrieval abuse testing

partial

moderate — scope complete, execution in progress

Product Security"Do you test the AI feature against prompt injection and context manipulation?"

Agent tool permission policy

partial

weak — matrix in draft, not reviewed

AI Platform Engineering"What actions can the AI system take?"

Human approval for sensitive actions

partial

moderate — approval flow implemented, audit log not complete

Product Operations"Which AI actions require human review?"

AI trace logging

implemented

strong

Security Engineering"Can you reconstruct AI decisions and tool actions?"

Buyer question

draft

Is customer data used to train foundation models?

No. Customer data is processed under contractual terms that exclude it from provider model training. Final answer subject to legal review.

Unassigned

Sales: No — requires legal approval before shar…

Buyer question

partial

Can a user receive information through AI that they cannot access directly?

Retrieval uses tenant and source authorization filters. End-to-end proof across retrieval, reranking, and prompt assembly is being validated.

Unassigned

Sales: No — do not share a partial answer on re…

Buyer question

partial

Can the AI system take actions in customer environments?

The AI can read, summarize, and draft workflow items. Sensitive actions require human approval. Full permission boundaries are in the agent permission matrix.

Unassigned

Sales: With qualification — describe read/draft…

Buyer question

approved

Can AI interactions be audited?

Yes. AI interactions generate trace records including request metadata, retrieval references, model routing, tool calls, and policy decisions.

Unassigned

Sales: Yes — cite trace schema version and that…

inventory

AI System Inventory Record

strong

available · Product Security

architecture

Model Routing Architecture Diagram

strong

available · AI Platform Engineering

test-evidence

RAG Authorization Test Plan

weak — plan only, execution pending

needs-validation · Search Platform

matrix

Agent Tool Permission Matrix

weak — draft, not reviewed

draft · AI Platform Engineering

logging-evidence

AI Trace Schema v2.1

strong

available · Security Engineering

vendor-statement

Model Provider Data Use Statement

weak — not legally reviewed

draft · Vendor Management

Readiness interpretation

Findings

Readiness Findings

Finding · high

The evidence gap is a commercial blocker

The product team can explain many controls verbally, but several buyer questions are not yet backed by clean, legal-approved evidence. Verbal explanations do not satisfy enterprise procurement teams. This creates procurement drag and deal risk.

Finding · critical

Retrieval authorization needs proof, not intent

Enterprise reviewers will not accept architecture intent alone. The company needs test evidence showing that authorization filters survive retrieval, reranking, and prompt assembly end-to-end. The RAG authorization test plan exists; it has not been executed against production.

Finding · high

Agent authority needs a precise, bounded answer

The buyer question is not whether the product uses agents. It is what the agent can do, under whose authority, with what approval requirement, and with what audit trail. The agent permission matrix is in draft; it is not yet buyer-ready.

Finding · high

AI trace retention is not yet buyer-ready

Prompts, outputs, retrieval references, and tool-call records need explicit retention classification, access-control language, and a deletion schedule before the company can answer security questionnaires cleanly. The policy is in draft.

Control evidence summary

Control map

Control Evidence Map

The control map connects AI-specific buyer questions to implemented controls, partial controls, missing controls, evidence artifacts, and accountable owners. Controls marked partial or planned must reach implemented status before those buyer questions can be answered with approved answers.

AI system inventory

implemented

strong

"What AI features are in scope for this product?"

Risk: Buyer cannot assess scope; procurement stalls

Model provider boundary statement

partial

weak — draft only, not legally reviewed

"Is customer data used to train third-party models?"

Risk: Highest-priority buyer question cannot be answered with approved language

Gateway-only model access

implemented

strong

"Can product services call the model provider directly?"

Risk: Data flow controls cannot be verified; buyer cannot assess blast radius

Authorization-preserving retrieval

partial

weak — design only, no test execution

"Can the AI system retrieve data the user cannot access directly?"

Risk: Critical data isolation gap; blocks enterprise review

Prompt injection and retrieval abuse testing

partial

moderate — scope complete, execution in progress

"Do you test the AI feature against prompt injection and context manipulation?"

Risk: Cannot demonstrate adversarial robustness; procurement may flag

Agent tool permission policy

partial

weak — matrix in draft, not reviewed

"What actions can the AI system take?"

Risk: Buyer cannot assess agent authority scope; creates open-ended liability

Human approval for sensitive actions

partial

moderate — approval flow implemented, audit log not complete

"Which AI actions require human review?"

Risk: Oversight gap; cannot demonstrate bounded autonomous authority

AI trace logging

implemented

strong

"Can you reconstruct AI decisions and tool actions?"

Risk: No auditability; blocks enterprise and regulated-industry buyers

Heads up

Evidence pack vs. policy binder

A policy states what the organization intends. An evidence pack shows what the system actually does: who owns it, where proof lives, what the evidence quality is, and what still needs remediation. Buyers evaluate evidence, not intentions.

Questionnaire answer bank

Questionnaire answer bank — controlled answers only

Buyer question	Short answer	Answer status	Evidence artifact	Evidence owner	Approved by	Sales use
Is customer data used to train foundation models?	No. Customer data is processed under contractual terms that exclude it from provider model training. Final answer subject to legal review.	Draft	Model provider boundary statement	Vendor Management	Pending — legal review	No — legal review required first
Can AI outputs be audited?	Yes. AI interactions produce trace records including request metadata, retrieval references, model routing, tool calls, and policy decisions.	Approved	AI trace schema v2.1	Security Engineering	CISO, 2026-05-12	Yes — cite trace schema version
Can the AI retrieve data a user cannot access directly?	Retrieval uses tenant and source authorization filters. End-to-end proof across retrieval, reranking, and prompt assembly is being validated.	Partial	RAG authorization test plan	Search Platform	Pending — test execution	No — partial only, do not share
What actions can the AI take?	The agent can read, summarize, and draft workflow items. Sensitive actions require human approval. Full permission boundaries are documented in the agent permission matrix.	Partial	Agent Tool Permission Matrix	AI Platform Engineering	Pending — matrix finalization	With qualification — describe read/draft scope only
How long are prompts and outputs retained?	A retention policy is being finalized. Current design targets 90-day retention with access controls on sensitive trace data, subject to customer requirements.	Draft	AI trace retention policy draft	Security Engineering	Pending — legal review	No — policy not finalized

Decision · conditional

Sales answer governance

Sales must use only approved answers from this bank when responding to AI security questionnaires. Unanswered or draft questions must be routed to Trust and Security, not improvised. Legal-approved provider language must be kept separate from engineering assumptions.

Required evidence artifacts

Checklist

Evidence required before enterprise review

✓AI system inventory — complete with system name, model, environment, and data classification.

✓Model provider boundary statement — legally approved, covering training use, data residency, and sub-processors.

✓Prompt envelope minimization design — showing what customer data is and is not sent to the model.

✓RAG authorization test results — executed against production, not just the test plan.

✓Agent Tool Permission Matrix — complete, showing read/suggest/draft/queue/approve/execute authority per action.

✓Approval context bundle — showing which actions require human review and the audit trail.

✓AI trace schema — with access-control and classification documented.

✓AI trace retention and access policy — finalized, legally reviewed.

✓AI incident response playbook — covering AI-specific incident scenarios.

✓AI release gate checklist — showing what security review is completed before each AI feature release.

Evidence remediation roadmap

Priority	Work item	Owner	Effort	Buyer value	Due	Acceptance criteria
P1	Execute RAG authorization test suite against production	Search Platform	2 weeks	Proves RAG does not bypass user-level access controls	2026-06-20	Test results document signed off by Product Security; authorization bypass scenarios covered
P2	Finalize and sign agent permission matrix	AI Platform Engineering	1 week	Provides a precise answer to "what can the AI do?"	2026-06-13	Matrix reviewed and approved by Trust & Security and Legal
P3	Complete legal review of provider boundary statement	Vendor Management / Legal	2 weeks	Answers training and data-use questions with approved language	2026-06-27	Statement approved by Legal; safe for sales use marked
P4	Finalize AI trace retention and access policy	Security Engineering	1 week	Answers prompt and output retention questions	2026-06-20	Policy reviewed by Legal and Security; retention schedule and access tiers documented
P5	Publish AI incident response playbook	Security Operations	3 weeks	Shows buyers that operational AI security is in place	2026-07-11	Playbook tested in tabletop exercise; approved by CISO
P6	Mark approved answers in questionnaire bank	Trust & Security	3 days	Gives sales a controlled, safe answer set	2026-06-13	Each answer has an approved-by record; sales guidance is explicit

Appendix: operating instructions

Checklist

How to use this pack

✓Keep the pack owned by Trust and Security, not Sales or Engineering alone.

✓Every buyer answer must map to a named evidence artifact and a named owner.

✓Mark all draft answers clearly — do not allow sales to use unapproved answers.

✓Route unanswered buyer questions into the remediation backlog, not into improvised responses.

✓Keep legal-approved provider language separate from engineering assumptions.

✓Update the pack after each AI architecture change, security assessment, or control status change.

✓Review the pack before every major sales cycle or enterprise procurement event.

Artifact

Related artifact: AI Trust Boundary Map

The trust boundary map supplies the architecture and data-flow evidence that makes the evidence pack credible. Use it to answer questions about where customer data flows, what the model provider receives, and where authorization boundaries are enforced.

/deliverables/ai-trust-boundary-map

Artifact

Related artifact: RAG Authorization Review

The RAG authorization review is the source of the retrieval authorization control status. Its test results feed directly into the questionnaire answer bank for retrieval-related buyer questions.

/deliverables/rag-authorization-review

Artifact

Related artifact: Agent Tool Permission Matrix

The agent permission matrix defines what the AI can do, what requires human approval, and what is blocked. It is the primary evidence artifact for buyer questions about agent authority and oversight.

/deliverables/agent-tool-permission-matrix