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Do not approve an AI model because it has a good benchmark score, a polished model card, or a reassuring privacy statement. Assess the complete system in the exact context where you will use it: the task, users, affected people, data flows, integrations, deployment controls, and consequences of failure. Then gather provider evidence, test the configured deployment against realistic threats, record what remains uncertain, and approve only the defined use with explicit controls.
This process applies whether you call a hosted model through an API, use an AI feature embedded in another application, or run a model locally. The model itself is only one component of the risk boundary.
What you are actually assessing
The word “model” can describe very different systems. A hosted model may send prompts and files to a provider, retain logs, call external tools, and change when the provider updates it. An embedded assistant may inherit the application’s identity, permissions, retrieval index, and audit controls. A locally deployed model may avoid sending prompts to a third party while leaving you responsible for patching, access control, hardware, telemetry, and incident response.
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|---|---|---|
| Hosted API or chat service | Provider processing, account security, retention, subprocessors, geographic transfer, model updates, and connected tools | Current product terms, data-use settings, retention and deletion behavior, security documentation, incident process, change notices, and test results for your configuration |
| AI feature inside an application | Application permissions, retrieval sources, prompts, plugins, tenant isolation, logging, and the application’s own supply chain | Architecture and data-flow description, permission model, integration inventory, audit evidence, vulnerability handling, and controls for the exact edition |
| Self-hosted or local model | Model files, dependencies, serving stack, hardware, administrators, network exposure, updates, and physical or endpoint access | Artifact provenance, software bill of materials where available, signatures or hashes, patch process, hardening guide, access logs, and recovery procedures |
A generic safety claim cannot answer whether a system is suitable for a particular decision. NIST’s AI Risk Management Framework (AI RMF) treats trustworthiness as a lifecycle concern spanning design, development, deployment, use, and test and evaluation. It is voluntary, and NIST says the framework is being revised; its trustworthiness characteristics still need to be interpreted in context rather than checked independently as if each were sufficient.
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Step 1: Define the use and risk boundary
Write a short assessment scope before collecting vendor material. This prevents a decision about “the model” from silently becoming approval for every future use.
Record the system and people involved
- Model name, release or version, provider, deployment owner, and exact product tier.
- Task the system will perform and what a human is expected to do with its output.
- User groups, administrators, operators, and people affected by the output even if they never use the system.
- Deployment geography, data residency expectations, connected tools, retrieval stores, plugins, agents, and downstream systems.
- Whether the system handles personal, confidential, regulated, proprietary, safety-sensitive, or security-sensitive information.
Identify the authority that can accept residual risk. In a small project this may be the product owner; in a regulated or high-impact setting it may require security, privacy, legal, or senior operational approval.
Describe what failure means
List the consequences of an incorrect answer, a leaked input, an unavailable service, a manipulated response, or an unauthorized action. Include harms to customers, employees, applicants, patients, students, the public, and your organization. Set a decision threshold in advance, such as “no autonomous external action” or “a qualified reviewer must approve every recommendation.”
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| Failure type | Questions to answer | Typical control direction |
|---|---|---|
| Incorrect or misleading output | Could someone lose money, access, safety, employment, or a legal right? | Human review, source display, confidence limits, escalation, and a prohibition on high-impact automation |
| Data disclosure | What is the most sensitive input or output, and who could receive it? | Data minimization, redaction, tenant isolation, encryption, retention limits, and access monitoring |
| Unauthorized action | Can the model send messages, change records, execute code, or call a business tool? | Least privilege, allowlists, approval gates, transaction limits, and reversible operations |
| Unavailability | What work stops if the model, provider, network, or retrieval system fails? | Fallback procedure, cached or manual workflow, service objectives, and recovery testing |
Step 2: Map data and privacy exposure
Draw the complete path of information rather than reading only a privacy-policy headline. Start with the user’s device and follow data through the application, provider, model gateway, logs, safety systems, retrieval databases, external tools, outputs, backups, and support channels.
Answer the data-lifecycle questions
- Collection: Exactly which prompts, files, metadata, identifiers, telemetry, and generated outputs leave the user’s environment?
- Storage: Are inputs and outputs stored, in which systems, and for how long? Are backups and disaster-recovery copies covered?
- Access: Which provider staff, administrators, subprocessors, support personnel, or connected services can view the data?
- Use: Is content used to train, fine-tune, evaluate, moderate, monitor, or improve a service? Is the setting different for your account, tier, geography, or contract?
- Transfer: In which countries or regions is data processed or supported? Does a tool or subprocessor receive it?
- Deletion: Can you delete individual records, accounts, indexes, and backups? What verification and delay apply?
- Output: Can generated text reproduce confidential material, expose another tenant’s data, or be copied into a downstream system without review?
Obtain written, current answers for the exact product and configuration. Terms often differ between consumer, business, enterprise, hosted, and self-managed offerings. An unanswered question is uncertainty; it is not evidence that the favorable answer applies.
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NIST’s Generative AI Profile (2024) warns that third-party generative-AI integrations can increase data-privacy and information-security risks. It highlights transparency about the collection and use of third-party data supplied as model inputs, along with data protection and retention.
Minimize exposure before you test
- Use synthetic or de-identified records during evaluation.
- Remove secrets, credentials, unnecessary identifiers, and whole documents when a relevant excerpt is enough.
- Separate production and test accounts, workspaces, indexes, and API keys.
- Disable persistence, training use, external browsing, plugins, or tool calls unless the task requires them.
- Define an approved-data policy that states what users must never paste or upload.
Privacy review does not end when a response appears on screen. Check exports, conversation history, administrator consoles, monitoring streams, support tickets, and downstream copies.
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Step 3: Threat-model conventional and AI-specific security risks
Use the familiar confidentiality, integrity, and availability model for the whole service, then add machine-learning attack paths. Include the model files and training data, the serving application, identity system, retrieval layer, tools, output consumers, and the infrastructure that runs them.
Conventional security questions
- Identity and access: Are strong authentication, single sign-on, role separation, service-account controls, and rapid key revocation available?
- Confidentiality: Are data in transit and at rest protected? Can one tenant, workspace, or user retrieve another’s content?
- Integrity: Who can change prompts, system instructions, retrieval indexes, policies, model files, or tool permissions?
- Availability: What happens during provider outage, rate limiting, denial-of-service, corrupted indexes, or exhausted compute?
- Software supply chain: What libraries, containers, model artifacts, hardware, and external services are used, and how are they updated?
- Monitoring and response: Are security events, unusual tool calls, data exports, and administrator actions logged with usable timestamps and retention?
- Recovery: Can you suspend the integration, rotate credentials, restore trusted indexes, roll back a model update, and notify affected parties?
AI-specific attack paths
- Adversarial inputs and evasion: Inputs deliberately crafted to evade a classifier, safety control, or detection rule.
- Prompt injection: Instructions hidden in user content, retrieved documents, web pages, or tool results that attempt to override the system’s intended task.
- Data poisoning: Malicious or low-quality records inserted into training, fine-tuning, evaluation, or retrieval data to change behavior.
- Model extraction: Systematic querying intended to reproduce a model, its behavior, or sensitive decision boundary.
- Membership inference: Attempts to determine whether a person’s record or document appeared in training or other data.
- Model or output leakage: Responses that reveal system prompts, secrets, memorized material, hidden metadata, or another user’s content.
- Tool and agent abuse: A manipulated response causing a connector to send data, alter records, execute code, or take an irreversible action.
- Availability attacks: Oversized inputs, recursive tool calls, expensive retrieval, or targeted query floods that consume resources.
NIST notes that existing guidance does not comprehensively address evasion, model extraction, membership inference, availability, and other machine-learning attacks. Treat the threat list as an active engineering concern, not a finished catalogue.
Step 4: Review the provider, model, and integration evidence
Ask for evidence that describes the deployed version and service tier, not a general corporate promise. Keep the answer, date, scope, and any exclusions in your assessment record.
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| Area | Questions for the provider or integrator | Evidence that reduces uncertainty |
|---|---|---|
| Data handling | Are prompts, files, outputs, and telemetry retained or used for improvement? Who can access them? How are deletion and regional processing handled? | Current contractual terms, configuration screenshots or settings, retention schedule, subprocessor list, and deletion procedure |
| Security operations | How are vulnerabilities reported, prioritized, patched, and communicated? What is the incident-notification process? | Security documentation, contact route, response commitments, audit or attestation report, and recent change notices |
| Architecture | Which model version, hosting environment, retrieval stores, tools, and external components are in the path? | Architecture diagram, integration inventory, software bill of materials (SBOM) where available, and model or artifact provenance |
| Access and isolation | How are tenants, roles, service accounts, keys, and administrative actions separated and monitored? | Access-control description, audit-log examples, test evidence, and independent assessment scope |
| Reliability and change | How are updates announced? Can you pin a version, test in a staging environment, suspend use, or roll back? | Service-level agreement (SLA), version policy, maintenance notices, rollback process, and recovery objectives |
| Limitations | What failure modes, unsupported uses, known biases, data limits, and evaluation gaps are documented? | Version-specific model and system documentation, task-relevant evaluations, and clearly stated exclusions |
NIST recommends procurement due diligence and identifies SBOMs, SLAs, and attestation reports as information organizations may request. Controls can differ for a foundation model, a fine-tuned model, and a model embedded with tools. A certificate or document is evidence to inspect, not a guarantee that your configuration is safe.
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Test the deployment you will actually operate, including its prompts, policies, retrieval sources, permissions, filters, tools, model version, and logging. A public demo or a few successful prompts cannot establish security or reliability.
Build a representative test set
- Normal tasks: Use representative, approved examples from the intended workflow, with expected outputs and acceptance criteria.
- Sensitive-data cases: Verify redaction, refusal, retention settings, access boundaries, export behavior, and treatment of personal or confidential fields.
- Authorization cases: Test users with different roles, tenants, projects, and revoked access. Try to retrieve records or call tools outside each role.
- Manipulated inputs: Include direct override attempts, instructions hidden in documents, malicious links, unusual encodings, oversized content, and conflicting sources.
- Tool and retrieval cases: Confirm that the system sends only permitted fields, uses approved destinations, respects source permissions, and requires approval for consequential actions.
- Failure and recovery: Disconnect dependencies, return malformed results, exceed limits, rotate a key, restore an index, and exercise the manual fallback.
- Output handling: Check whether generated code, queries, links, or decisions are validated before reaching a user or system.
Document what the test proves
Record the model and configuration, test data, threat scenarios, date, evaluator, pass criteria, failures, mitigations, and untested areas. Preserve enough detail to reproduce the result without retaining unnecessary personal data. Retest after a model update, prompt or policy change, new connector, retrieval-data change, infrastructure migration, incident, or material change in users or task.
NIST recommends iterative, documented test, evaluation, validation, and verification (TEVV) throughout the lifecycle. Its Generative AI Profile cautions that available pre-deployment TEVV may be inadequate, applied inconsistently, or mismatched to the deployment context. Anecdotal capability tests and jailbreak exercises can reveal weaknesses, but neither alone establishes validity, reliability, or privacy protection.
Step 6: Use frameworks to structure—not replace—the decision
NIST AI Risk Management Framework
Use the voluntary AI RMF to organize governance, mapping, measurement, and management activities across the lifecycle. Its central value is forcing the team to connect technical behavior with affected people, organizational responsibilities, and trade-offs. NIST emphasizes that addressing trustworthiness characteristics individually does not ensure trustworthiness overall.
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OWASP AISVS 1.0
OWASP’s AI Security Verification Standard (AISVS) 1.0, released in June 2026, contains 191 requirements across 12 chapters. It is a free, vendor-neutral catalogue of testable security requirements covering areas from training data and deployment through monitoring and retirement. Select a verification level appropriate to the risk. AISVS is intended to complement, not replace, general application and infrastructure security verification.
Use the standards to make questions testable: who verifies the control, what evidence is required, which deployment is in scope, and what result causes remediation or rejection. Neither framework grants universal approval to a model.
Maintain a risk register
| Field | What to record |
|---|---|
| Asset or data | Model artifact, prompt, document, identity, tool, output, or business record at risk |
| Threat and affected party | Attack or failure, plus the people, organization, or public who could be harmed |
| Likelihood and impact | Reasoned qualitative or quantitative ratings with assumptions stated |
| Evidence | Configuration, contract, test result, audit material, or unanswered question |
| Control and owner | Preventive, detective, and recovery measures, with a named accountable person |
| Residual risk | What remains after controls, including uncertainty and dependencies |
| Decision and trigger | Approval scope, required changes, expiry or review date, and events that force reassessment |
How to compare two models or providers
Compare them under the same task, data class, users, tools, geography, and configuration. Broad claims such as “enterprise-grade” or “private” are not comparable evidence.
| Comparison axis | What to compare |
|---|---|
| Data handling | Input and output use, retention, deletion, training use, logging, administrator access, subprocessors, and processing location |
| Security evidence | Identity controls, vulnerability handling, incident response, supply-chain information, and independently reviewed evidence |
| Deployment fit | Hosting and integration model, connected tools, update control, availability, isolation, and ability to suspend or restrict use |
| Evaluation evidence | Task-relevant tests, realistic threat coverage, reproducibility, known limitations, and relevance to production |
| Governance and recourse | Named ownership, change notification, auditability, support, rollback, and a practical route to stop processing |
| Residual risk | Severity and likelihood of remaining harms for the specific decisions and people involved |
A model that performs better on a benchmark may still be the worse choice if it retains sensitive inputs, cannot isolate tenants, exposes powerful tools, or provides weaker change control. Compare the complete risk profile, not just output quality.
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- Approved for the defined use: Named data classes, users, configuration, controls, reviewer duties, and reassessment triggers are recorded.
- Approved after specified changes: List the blocking remediation, owner, evidence required, and deadline before production access.
- Limited to lower-risk use: Permit synthetic, public, or non-sensitive data and low-impact tasks while excluding autonomous actions and high-impact decisions.
- Rejected: Residual harm, uncertainty, unavailable evidence, or inadequate controls exceed the organization’s threshold.
Approval is not a statement that the model is safe everywhere. It is permission for one documented use under stated conditions.
Reassess when the system or context changes
Set a periodic review, then trigger an earlier assessment when any of the following occurs:
- The provider changes the model, service tier, retention setting, hosting region, or safety behavior.
- You add a retrieval source, plugin, agent, connector, external tool, or new downstream system.
- User roles, affected populations, task importance, or permitted data classes change.
- Monitoring finds leakage, prompt injection, unauthorized access, harmful output, unexplained drift, or unusual usage.
- A dependency, model artifact, library, credential, or infrastructure component is compromised or reaches end of support.
- Law, contract, internal policy, or sector requirements change.
Keep the previous decision and test results so you can identify what changed. If you cannot explain the current data path, permissions, model version, and recovery plan, treat that gap as a reason to pause or narrow use.
Common assessment mistakes
- Assessing the model name instead of the deployment: The same model can have different privacy, tool, and access risks in different products.
- Accepting a privacy slogan: Require retention, training-use, access, deletion, and subprocessor details for the exact tier.
- Testing only normal prompts: Include malicious documents, role changes, tool abuse, data-boundary violations, and outage recovery.
- Treating a benchmark as a security test: Capability scores rarely cover confidentiality, integrity, availability, or your integration.
- Running a jailbreak contest and stopping there: Jailbreaks are useful probes, not proof of reliability, privacy, or resistance to every attack.
- Ignoring uncertainty: Mark unanswered questions and unsupported claims explicitly; do not convert missing evidence into approval.
- Forgetting the fallback: An AI feature is not resilient if staff cannot continue safely when it is unavailable or produces unusable output.
Legal compliance is separate from this technical and operational assessment. Obligations depend on jurisdiction, industry, data type, contractual terms, and the current product configuration. Obtain applicable legal and privacy advice rather than treating a framework as a compliance determination.
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