Assess the specific generative AI system, configuration, and intended use—not just the vendor’s assurances. Map the data and access paths, examine contractual and technical evidence, test the configured system, document residual risks, and monitor it after approval. The result should be a decision record your security, privacy, legal, procurement, and IT teams can revisit when the system changes.
What the assessment must cover
“Enterprise generative AI” can mean a hosted assistant, an API embedded in an application, a model operated inside the organization, a retrieval-augmented system that searches internal content, a fine-tuned model, or an agent that can take actions through connected tools. These designs have different data flows and failure modes. The service tier, model version, configuration, users, data, and integrations determine what you are actually assessing.
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Set the assessment boundary before reviewing controls. Record the business purpose and approval decision; the product and deployment type; model and service tier or version where known; user groups and administrators; intended and prohibited uses; data classes and sources; retrieval, fine-tuning, and evaluation arrangements; connected applications and tools; and the vendor and subprocessors involved. Include people affected by the system, not only its users.
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- Internally hosted model: Include the infrastructure, model artifacts, deployment pipeline, operational access, and security responsibilities retained by your organization.
- Retrieval-augmented application: Assess the repositories, indexing pipeline, identity and permission propagation, and whether the application can expose content across users or groups.
- Fine-tuned system: Include the origin and handling of training or tuning data, the people and services with access to it, and how updates or removal requests are handled.
- Agent or tool-enabled system: Treat tool permissions and possible actions as part of the system boundary, not as incidental integrations.
NIST’s AI Risk Management Framework (AI RMF) uses its Map function to establish context as a foundation for later measurement and management. That is a useful starting point: controls cannot be evaluated meaningfully until the use and system boundary are clear.
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How does data move through the system?
Build a data-flow description that follows information from the user or source system through the model and connected services, then to output, storage, logs, and deletion. Do not limit the map to prompts: uploaded files, retrieved passages, feedback, support records, telemetry, and administrator access can also expose information.
| Data or path | Questions to answer |
|---|---|
| Prompts and uploads | What categories of information can users submit? Where does it go, who can access it, and is it retained or used for training, fine-tuning, evaluation, or service improvement? |
| Connected repositories | Which sources are indexed or queried? Are source permissions enforced for each user, and how are access changes, deleted content, and stale indexes handled? |
| Outputs and feedback | Can generated responses contain personal, privileged, proprietary, or regulated information? Are outputs or user ratings stored, reviewed, or reused? |
| Logs, telemetry, and support | What content or identifiers appear in logs, diagnostics, and support tickets? Which provider staff or subprocessors can see them, for what purpose, and for how long? |
| Tools and downstream systems | What data can a tool receive, what actions can it perform, and what approval, authentication, and logging apply to those actions? |
| Transfers and storage | Where is data processed and stored? Does it cross organizational or geographic boundaries, and what transfer arrangements and deletion commitments apply? |
For each flow, record the data category, source, purpose, destination, access boundary, retention period, deletion method, and any organizational or geographic transfer. Ask the provider directly whether submitted content is used for training, fine-tuning, evaluation, or product improvement. Confirm the answer against the specific offering and configuration, and identify the contractual term and technical setting that govern it. A general product statement is not a substitute for the terms applicable to your service tier.
Classify information in context. Personal information, health or biometric information, location data, credentials, privileged communications, source code, and other proprietary or regulated material may require different handling. NIST’s Generative AI Profile notes that privacy risks include leakage and unauthorized use, disclosure, or de-anonymization of personally identifiable or sensitive data; what counts as sensitive depends partly on context. Have counsel determine which legal obligations apply to the jurisdiction, data, and use case rather than assuming one rule applies to every deployment.
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What vendor and supply-chain evidence should you review?
Request current evidence for the exact product and scope under consideration. Separate a vendor’s statements from independently reviewed evidence, contractual commitments, and tests your organization has performed. NIST’s Generative AI Profile identifies acquisition due diligence, software bills of materials (SBOMs), service-level agreements (SLAs), and attestation reports as possible inputs to third-party risk management; none is a blanket guarantee that an AI application or integration is safe.
- Data and privacy documentation: Review data-flow and retention descriptions, use-of-content terms, deletion procedures, and relevant privacy commitments. Identify exceptions and the configuration needed to obtain a stated protection.
- Security assurance: Examine relevant attestation reports, security architecture information, vulnerability handling, and incident processes. For each report, check its period, systems and services in scope, exclusions, findings or exceptions, and complementary customer responsibilities.
- Supply-chain visibility: Identify subprocessors and third parties that can access organizational content. Request an SBOM or other component information where relevant, and determine how material vendor or subprocessor changes are communicated.
- Contract and operating terms: Check audit or evaluation rights, incident notice and cooperation, service commitments, change notification, data return and deletion, and exit provisions. Confirm who is responsible for each response task.
- AI-specific limitations: Establish which model and version are used, what configuration options are available, and what the provider says about evaluation, known limitations, and changes that could affect your use.
NIST recommends extending vendor assessments to intellectual-property, privacy, security, and other risks; inventorying third parties with access to organizational content; and maintaining approved AI technology and provider lists. An attestation can support due diligence, but it does not establish that every model behavior, retrieval boundary, or customer-built integration is safe.
How should you test the configured system?
Test the system you intend to deploy, in a representative environment, using scenarios that reflect its data, users, integrations, and impact. Record the objective, configuration and version, test data, steps, results, limitations, and remediation. Scale the depth of testing to the sensitivity of the information and the consequences of an error.
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Test data boundaries and access
- Check whether one user can retrieve, infer, or receive another user’s or group’s information.
- Verify that connected sources enforce the permissions users have in the source system, including after access is revoked or content is deleted.
- Use representative sensitive data to determine whether it can appear in outputs, logs, feedback, or support paths beyond the intended audience.
- Check how the system behaves when a user asks for information outside their authorization or submits malformed or unexpected content.
Test integrations and consequential actions
For each connected tool, document its permissions and the practical consequences of a call. Test whether the system can send data to an unintended destination, perform an action without required approval, or act on instructions embedded in untrusted content. Confirm that authentication, authorization, user confirmation, and audit logging work as configured. Give particular scrutiny to agents that can modify records, send communications, execute code, or make external transactions.
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Record what the tests do—and do not—show
Define pass criteria before testing and retain the results with the assessment. A successful test of selected scenarios is evidence about those scenarios, not proof of safety across all prompts, users, data, or future versions. NIST cautions that pre-deployment evaluations can be inadequate or mismatched to deployment context; benchmark or anecdotal performance alone does not establish validity or reliability in a real domain.
Use an established risk taxonomy to organize test coverage. OWASP’s GenAI Security Project lists a 2026 LLM Top 10 and an Agent Control Standard. Its crosswalk dated September 1, 2026 describes mapping 51 GenAI vulnerabilities across four source lists to controls in 25 frameworks. That figure describes the scope of the crosswalk, not the number of all possible vulnerabilities or a measure of how secure a particular deployment is.
How can you compare two or more options fairly?
Apply the same questions to every candidate and compare evidence for the service tier, geography, and configuration you would actually use. The axes below are a practical synthesis for due diligence, not a prescribed vendor scorecard or a guarantee that a higher score means lower risk.
| Comparison axis | Evidence to compare |
|---|---|
| Data use and retention | Uses of prompts, files, outputs, feedback, and logs; training or improvement settings; retention and deletion terms. |
| Location and third parties | Processing and storage locations, transfer arrangements, subprocessors, and their access to content. |
| Identity and access boundaries | Single sign-on and role controls where applicable, separation between users or tenants, and permission handling for connected data. |
| Security evidence | Attestation scope and period, vulnerability response, incident process, architecture information, and relevant SBOM details. |
| AI-specific behavior | Testing against risks relevant to the intended configuration, the model and version, and documented limitations. |
| Integrations and authority | Connected data sources and tools, permissions granted, approval requirements for consequential actions, and logging. |
| Contract and operations | Audit or evaluation rights, notice and cooperation terms, incident handling, service commitments, change notification, exit, and deletion. |
Where evidence is missing or unclear, record it as an unresolved risk rather than treating silence as a positive control. Decide whether the gap requires a contractual commitment, a configuration change, a compensating control, a narrower use, or rejection of the option.
What should the approval record contain?
Make the assessment useful to the people who must operate and revisit it. A concise decision record should identify:
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- The system boundary, purpose, service tier, model or version, users, data, integrations, and deployment configuration assessed.
- The data-flow map and applicable privacy, security, contractual, and operational requirements identified by the responsible teams.
- Evidence reviewed, its scope and date, unanswered questions, test cases and results, and known limitations.
- Risks, severity or impact rationale, mitigation, accountable owner, due date, and residual risk after controls.
- The approval decision and conditions, including prohibited data or uses, stop criteria, rollback approach, and escalation contacts.
Approval is a decision about a bounded configuration and use, not a permanent certification of a vendor or model. If material risks remain, state who accepts them and what conditions must be met before broader deployment.
How do you monitor and reassess after launch?
Assign an accountable system owner and maintain an inventory of AI systems, approved providers, data sources, and connected tools. Define how incidents are reported, who coordinates with the provider, and who determines whether legal or regulatory notifications are required. Establish a review cadence appropriate to impact, plus event-triggered reassessment when the model, service tier, data, purpose, tool permissions, vendor, or subprocessor changes.
Monitor whether the deployed configuration still matches the approval record: access controls, data retention, integrations, user population, and provider terms can change. Keep a response path for privacy or security incidents, high-risk third-party failures, and unexpected model behavior, including a way to disable integrations or roll back the affected use. NIST’s AI RMF treats governance as continuous and calls for inventory, clear accountability, ongoing monitoring, periodic review, and contingency processes for high-risk third-party failures or incidents.
Which frameworks are useful—and what do they establish?
- NIST AI RMF 1.0: A voluntary framework organized around Govern, Map, Measure, and Manage. NIST says the framework is being revised as part of the White House AI Action Plan. Its functions help structure lifecycle risk work; using them does not certify a system.
- NIST AI 600-1, Generative AI Profile: Published July 26, 2024, this profile applies AI RMF concepts to generative AI risks, including privacy, third-party due diligence, testing, and monitoring.
- OWASP GenAI Security Project: Its materials provide a technical risk and control crosswalk for GenAI systems. Check the current versions of the LLM Top 10, Agent Control Standard, and related mappings when planning an assessment.
- NIST SP 800-218A: Published July 26, 2024, this AI-focused community profile augments the Secure Software Development Framework with practices for AI model development. It is described as useful to model producers, system producers, and acquirers.
Frameworks help organize questions and coverage; they do not replace review of the exact service, contract, configuration, and use. Product terms, features, subprocessors, locations, and framework materials can change, so verify the current documentation for the offering and configuration under consideration.
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