Does a C3PAO require a specific AI monitoring product?

No — assessors are barred from recommending products (32 CFR Part 170 conflict-of-interest rules). They evaluate whether your evidence demonstrates the controls. Any architecture that inspects AI traffic inside your boundary, blocks unauthorized flows, and produces attributable, tamper-evident records can satisfy them.

Is a screenshot of our DLP dashboard enough?

Rarely. A dashboard shows that a tool exists; the assessor needs the data-flow diagram showing where inspection happens, the configuration proving blocking is enforced, and log samples they can trace to individuals. Evidence is tested, not displayed.

Do we need AI evidence if we've banned AI tools?

Yes — a ban is a policy, and the assessor's next question is what technically enforces it and what the logs show. An unenforced ban is often weaker evidence than monitored, controlled AI usage, because it claims a control that doesn't exist.

How far back should AI usage logs go?

Follow your audit-retention policy (commonly 1–3 years for 3.3.1 records). For a first assessment, assessors typically sample recent weeks — which is why a 14-day monitored baseline with a signed report is a workable starting artifact while your retention builds.

Answers · CMMC & AI compliance

What evidence does a C3PAO accept for AI usage?

For AI usage, C3PAO assessors accept the same categories of evidence they accept everywhere else: architecture, configuration, records, and policy. Concretely that means four artifacts — a data-flow diagram showing where AI-bound prompts are inspected relative to your boundary, the enforcement configuration (detection patterns and blocking policy), tamper-evident logs attributing allowed and blocked prompt events to individual users, and a written AI acceptable-use policy the technical control actually enforces. A control-mapped assessment report packages the first three into a single reviewable document.

The four artifacts, and what each must show

Artifact	What the assessor checks	Controls it evidences
Data-flow diagram	AI traffic routes through an inspection point INSIDE your boundary — not a vendor cloud	3.1.3, 3.13.1 (AC.2.003, SC.1.001)
Enforcement configuration	Active CUI/PII patterns and a blocking (not just alerting) policy	3.1.3, 3.1.22
Tamper-evident event log	Allowed AND blocked prompts, per-user attribution, integrity protection (e.g. hash chain)	3.3.1, 3.3.2, 3.3.8 (AU.2.001/002/008)
Signed policy + training records	The written rule the control enforces, acknowledged by staff	3.2.x, 3.6.1

What assessors reject

Screenshots of a vendor dashboard with no data-path diagram behind them; a written AI ban with no technical enforcement (the follow-up question is always 'and what stops it?'); logs that only show blocked events with no user attribution; and any architecture where prompt content leaves the boundary to be scanned — because then the scanning service itself becomes part of the assessment scope.

The packaged version

HoundShield's $499 CMMC AI Risk Assessment produces the packaged artifact: after 14 days running on your own infrastructure (self-hosted Docker), it generates a SHA-256-signed PDF risk-scoring every AI prompt event against NIST 800-171 Rev 2 controls — the architecture, configuration, and records evidence in one document you hand across the table. Pair it with the free control-mapped AI use policy template and the four-artifact set is complete.

Frequently asked questions

Use AI without leaking CUI

HoundShield scans every AI prompt locally and blocks CUI before it leaves your network. One URL change. Under 10 minutes. C3PAO-ready.

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