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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →An AI vendor should pass a Pentagon or other high-risk government review only when it can produce verifiable evidence that its system is authorized for the mission, protects the required data, withstands realistic abuse, and remains under accountable human control. Treat the review as an evidence gate—not a marketing questionnaire.
Start by defining the mission, system boundary, users, interfaces, data classification and consequences of failure. Then require a control mapping to the Department of Defense (DoD) Risk Management Framework (RMF) and its AI-Cybersecurity Risk Management Tailoring Guide, use the NIST AI Risk Management Framework (AI RMF) as a trustworthiness crosswalk, determine whether CMMC and DFARS apply, verify cloud authorization and tenant isolation, and put testing, incident response and change-notification duties in the contract.
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1. Define the mission, data and authorization boundary
Do not evaluate an AI product in the abstract. Evaluate the exact deployment, mission decision and data flows that the government will authorize. A model that is acceptable for drafting unclassified administrative text may be unsuitable for intelligence analysis, weapons maintenance, personnel decisions or any workflow where an incorrect output can create safety, legal or national-security harm.
Record the mission and consequences
- Name the decision or workflow the system supports and identify who is allowed to act on its output.
- Document users, administrators, APIs, plug-ins, retrieval systems, external tools and every interface that can send or receive data.
- Describe the consequence of a wrong, delayed, manipulated or unavailable result, including safety, mission, privacy and civil-rights effects.
- Set the required human decision point: specify when a trained person must review, override or stop the system.
Classify the information
Identify whether the system handles classified information, controlled unclassified information (CUI), federal contract information, covered defense information, export-controlled material, personally identifiable information (PII), operational-technology data or other restricted content. Classification and contract terms determine which controls, facilities, personnel and cloud services are permissible.
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Demand boundary evidence
Require a system-security plan, data-flow diagram, model and dependency inventory, deployment architecture and an explicit authorization boundary. The boundary must include hosted models, vector stores, telemetry, support tooling, administrative consoles, subprocessors and any training or fine-tuning environment. Ask the vendor to identify where prompts, outputs, logs, backups and support-session data reside and who can administer them.
2. Map the product to DoD RMF and AI-specific tailoring
The DoD AI-Cybersecurity Risk Management Tailoring Guide, dated July 14, 2025, supports DoDI 8510.01 and addresses acquisition, development, use, sustainment, monitoring and disposal of AI systems. It is not a one-time checklist. The government owner should tailor controls to the system and data classification at each lifecycle stage and incorporate testing, evaluation, validation and verification (TEVV).
Evidence to request
- A control crosswalk to DoDI 8510.01 and the DoD AI-Cybersecurity Risk Management Tailoring Guide, showing which controls are inherited, implemented by the vendor, implemented by the agency or not applicable.
- Secure-development records covering code review, dependency management, secrets handling, vulnerability remediation and release approval.
- Identity and access-control design, privileged-access procedures, key management, administrative-session recording and separation of duties.
- Supply-chain provenance for models, data sets, libraries, containers, chips, APIs and managed services, including software bills of materials where available.
- Logging, time synchronization, backup, recovery, continuity and secure-disposal procedures, with retention periods and responsible personnel.
- A TEVV plan and results, known limitations, residual-risk statement and the identity of the authorized government official who accepts remaining risk.
Ask how controls change when the model, data, tools, infrastructure or mission changes. A vendor should be able to show version identifiers, approval records, rollback procedures and retest triggers rather than relying on a permanent “approved model” claim.
3. Use NIST AI RMF as a crosswalk, not an authorization
NIST released AI RMF 1.0 on January 26, 2023. NIST describes it as a voluntary framework intended to help developers, users and evaluators manage AI risks affecting people, organizations, society and the environment. NIST released a Generative AI Profile on July 26, 2024 and has said AI RMF 1.0 is being revised. A contract, regulation or agency policy can make selected practices binding, but the framework itself does not authorize a Pentagon deployment.
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Request the four-function mapping
| NIST AI RMF function | Questions for the vendor | Evidence |
|---|---|---|
| Govern | Who owns risk, approves releases, handles exceptions and receives incidents? | Policies, named owners, training records, risk register, exception and waiver process |
| Map | What are the intended uses, foreseeable misuse cases, affected groups, dependencies and failure consequences? | System description, threat model, data map, abuse-case catalog and impact assessment |
| Measure | How are validity, reliability, safety, security, resilience, transparency, explainability, privacy and harmful bias measured? | Test protocols, metrics, dated results, test data description, limitations and remediation status |
| Manage | How are risks prioritized, mitigated, monitored and accepted over the lifecycle? | Mitigation plans, monitoring dashboards, incident records, corrective actions and release gates |
Generative-AI-specific tests
For a generative system, request dated results tied to an exact model and configuration for prompt injection, jailbreaks, retrieval poisoning, data exfiltration, model extraction, unsafe content, tool misuse, unauthorized actions and denial-of-service. The report should state the threat model, test environment, evaluator independence, severity ratings and whether fixes were retested. A generic “AI safety tested” statement is not evidence.
4. Determine CMMC, CUI and DFARS obligations
DoD describes the Cybersecurity Maturity Model Certification (CMMC) program as verification that contractors protect federal contract information and CUI. DFARS subpart 204.75 governs how CMMC levels are included in DoD contracts. Applicability depends on the solicitation, the contractor’s role and the information actually handled.
Resolve applicability before comparing vendors
- Identify whether the supplier is a prime contractor, subcontractor, cloud service provider or software supplier handling CUI or covered defense information.
- Read the solicitation and contract for the required CMMC level, assessment route, affirmation requirement, evidence-retention period and any permitted plan of action and milestones (POA&M).
- Verify the assessment source, date, scope and enclave boundary. Confirm that managed services, support personnel and subcontractors are included where required.
- Check that the vendor’s evidence covers the environment delivering your service, not merely a corporate office or unrelated product.
A SOC 2 report or ISO certificate can provide useful control evidence, but neither automatically substitutes for a contract-required CMMC or NIST SP 800-171 determination. Record the exact clause and level that applies to the proposed deployment.
5. Verify cloud authorization and tenant isolation
FedRAMP status must be checked for the exact service, authorization boundary and impact level. Authorization of a parent cloud platform may not cover a newly procured AI feature, model endpoint, plug-in or data-processing service. FedRAMP’s AI criteria identify capabilities such as single sign-on (SSO), SCIM provisioning, role-based access control, analytics, data separation and protection, demonstrated federal demand, GSA availability and the ability to meet FedRAMP 20x authorization requirements. Service status and criteria can change, so obtain current documentation during procurement.
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Cloud evidence checklist
- Current authorization package, agency authorization documents, boundary diagram, impact level and any agency-specific conditions.
- Technical proof of tenant isolation, encryption in transit and at rest, key ownership, privileged-administrator controls and support-access restrictions.
- SSO, SCIM, RBAC, logging, analytics, retention, backup, deletion and restoration capabilities.
- A written statement saying whether prompts, outputs, telemetry, fine-tuning data or customer documents are used to train models, and whether any of that data leaves the authorized environment.
- Continuous-monitoring artifacts, vulnerability-disclosure process, incident-notification commitments, subcontractor list and exit or portability procedures.
Ask specifically what happens to data in backups, abuse-monitoring systems, debugging tools and human support channels. “Not used for training” does not by itself answer where data is stored, who can access it or how long it remains available.
6. Require capability, limitation and adversarial testing
High-impact and national-security use requires more than an accuracy demonstration. The vendor should provide a capability-and-limitation report, threat model, abuse-case catalog, independent test results and evidence that mitigations work under realistic conditions.
Test the attack surface
- Prompt injection and indirect instruction attacks through documents, web content or retrieved records.
- Data poisoning, retrieval manipulation, model tampering and supply-chain compromise.
- Sensitive-data leakage, membership inference, model extraction and unauthorized cross-tenant access.
- Unauthorized tool use, privilege escalation, unsafe code generation and actions outside the approved mission.
- Denial of service, resource exhaustion, degraded dependencies and loss of connectivity.
- Bias, unlawful discrimination, hallucination, unsafe recommendations and failure under distribution shift.
Make tests reproducible
For a high-risk mission, establish government witness rights, a reproducible test environment, pinned model and dependency versions, protected test data, rollback capability and mandatory retesting after material changes. Ask whether appropriately cleared personnel and facilities can support controlled or classified evaluation. Do not infer that a commercial test report is sufficient for a classified or mission-critical acceptance decision.
7. Put accountable humans and monitoring around the system
The national-security memorandum’s minimum high-impact practices include benefit-and-risk assessment, data-quality assessment, sufficient testing and evaluation, mitigation of unlawful discrimination and harmful bias, human training and oversight, ongoing monitoring, safeguards and accountability. Convert those principles into operating rules.
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Define human authority
- Name the decisions that require a trained human and the information that person must receive, including uncertainty, provenance and known limitations.
- Specify when the human may override, pause or shut down the system and how that action is logged.
- Train operators, reviewers, administrators and incident responders for automation bias, adversarial inputs and degraded performance.
- Assign a named accountable owner and maintain an inventory entry for high-impact AI at least annually or whenever the mission changes.
Set measurable operating thresholds
Define acceptable ranges for performance, drift, bias, safety and security; monitoring frequency; alert owners; and suspension criteria. Preserve audit logs, model and version identifiers, lawful records of prompts or inputs, outputs, overrides, incidents and corrective actions. State what evidence must be retained for an investigation and how privacy restrictions affect that retention.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Control AI changes, privacy and the supply chain by contract
OMB Memorandum M-24-18, issued October 3, 2024, directs agencies to identify AI in acquisitions, involve privacy officials throughout the lifecycle, evaluate vendor protection of PII and privacy risks, and require notice before new AI features are integrated into systems or services delivered under contract. Make these duties explicit rather than relying on website terms.
Contract provisions to include
- Advance notice and approval for new models, AI features, data sources, tools, subprocessors or material control changes.
- Approved data-use terms, no-training or limited-training commitments, retention limits, secure deletion or return, government data ownership and records-access rights.
- Incident definitions, notification deadlines, evidence preservation, cooperation, forensic access and corrective-action obligations.
- Disclosure and flow-down of security, privacy, provenance, testing and incident duties to subcontractors, model providers and API providers.
- Audit rights, independent-assessment access, government witness rights for tests and access to relevant logs and version records.
- Intellectual-property and provenance representations, indemnities where appropriate, continuity plans, termination assistance and portability of prompts, outputs, configurations and approved data.
- A right to suspend unsafe, nonconforming or unauthorized functionality without waiting for a routine renewal.
9. Compare vendors on evidence, not slogans
When two or more suppliers remain, compare them against the same mission and boundary. The strongest vendor is not necessarily the one with the longest compliance page; it is the one that can demonstrate authorization fit, isolation, testing quality and operational accountability with the least unresolved evidence.
| Comparison axis | What to verify |
|---|---|
| Authorization and contract fit | Exact DoD RMF mapping, CMMC/DFARS applicability, FedRAMP service and impact level, and agency conditions |
| Data location and isolation | Tenant separation, encryption, key control, administrator access, backups, deletion and cross-border processing |
| Model and dependency transparency | Model versions, training-data statements, software and service dependencies, provenance and change records |
| Independent testing | Threat model, evaluator independence, reproducibility, mitigation efficacy, retest triggers and residual risk |
| Human oversight and monitoring | Operator authority, training, drift and bias monitoring, suspension thresholds, logs and accountable owner |
| Incident and change commitments | Notification timing, evidence preservation, new-feature notice, vulnerability disclosure and subcontractor transparency |
| Portability and exit | Export formats, transition support, secure destruction, continuity and cost of replacing the service |
| Total evidence burden | How much additional agency testing, documentation, cleared support and ongoing oversight the deployment requires |
10. A practical approval sequence
- Write the mission, users, data classes, interfaces, failure consequences and authorization boundary.
- Determine whether classified information, CUI, federal contract information, PII or export-controlled data is involved.
- Request the vendor’s DoD RMF and AI-tailoring crosswalk, system-security plan, architecture and inventories.
- Map the service to NIST AI RMF Govern, Map, Measure and Manage outcomes, clearly labeling voluntary practices and contractually binding requirements.
- Resolve CMMC and DFARS level, assessment, affirmation, scope and evidence-retention requirements from the solicitation.
- Verify the exact FedRAMP service, boundary, impact level, authorization package, isolation controls and data-use statement.
- Review capability, limitation, threat, red-team and mitigation-efficacy evidence; conduct government-witnessed or controlled testing where risk warrants it.
- Approve human-oversight rules, monitoring thresholds, inventory ownership, incident escalation and suspension authority.
- Negotiate change notice, privacy, data-use, subcontractor, audit, incident, portability, deletion and termination terms.
- Record residual risk and obtain acceptance from the authorized government official before production use; repeat the review after material model, data, tool or infrastructure changes.
Common failures and how to correct them
“We have an AI certificate”
There is no universal certificate that substitutes for mission-specific authorization. Require the underlying controls, scope, dates, boundary and assessor evidence.
“The cloud is authorized, so the AI feature is authorized”
Check the exact endpoint, model, feature, impact level and authorization boundary. Obtain written confirmation that the procured service is covered.
“The vendor does not train on customer data”
Ask about prompts, outputs, telemetry, backups, abuse monitoring, support access, retention and subprocessors. Obtain enforceable terms, not only a marketing statement.
“A commercial red-team report proves safety”
Examine the threat model, test dates, model version, evaluator independence, classified-data limitations, remediation and retest evidence. Add government-controlled testing when the mission requires it.
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“CMMC is someone else’s problem”
Trace CUI and covered defense information through every contractor, cloud service and subcontractor. Confirm the required level and scope for the actual solicitation.
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