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Start with the system you will actually operate
The word model can mean very different risk boundaries. A model accessed through a hosted service, an AI feature embedded in another application, and a model running in your own environment may share a neural-network family but not their privacy, security or governance exposure. Write down which arrangement applies before reviewing evidence.
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Provider-hosted model or API
Your prompts, files and outputs may pass through the provider’s infrastructure, logging systems, abuse-monitoring systems, subprocessors and connected tools. The provider controls much of the update, retention and incident-response process. Your assessment must therefore cover the exact account, service tier, region, API settings and contractual terms.
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Model embedded in an application
An application can add retrieval, plug-ins, agents, analytics, user directories or business workflows around a model. Those components may see more data or take more consequential actions than the model itself. Assess the application, its integrations and its permissions, not just the underlying model documentation.
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Model deployed in your environment
Local or private deployment can reduce transmission to an external provider, but it moves responsibility for hardware, operating-system security, model files, updates, access control, monitoring, backups and incident response to your team. A downloaded model can still contain insecure dependencies or be replaced during the supply chain.
| Question | Why it changes the assessment |
|---|---|
| Who hosts inference? | Determines who controls infrastructure, logs, updates and availability. |
| What surrounds the model? | Retrieval stores, tools, plug-ins and agents create additional data and authorization paths. |
| Which version and configuration are in use? | Behavior, safeguards and terms can change between versions, regions and account tiers. |
| Who can be harmed? | Users, customers, employees, bystanders and the public may face different impacts from one failure. |
| What happens after an error? | An incorrect draft is not equivalent to an unauthorized payment, medical recommendation or access decision. |
Write a use-case statement
Record the model and version if known, provider or deployment owner, intended task, users, affected people, deployment geography, connected tools, data categories and the human or automated decision that follows an output. State what happens if the system gives a wrong answer, reveals information, becomes unavailable or takes an unauthorized action. Set a decision threshold and name the person or group that accepts residual risk. This scope prevents a decision about one low-risk workflow from being mistaken for universal approval of the model.
NIST describes its AI Risk Management Framework (AI RMF) as a voluntary lifecycle framework for incorporating trustworthiness considerations into the design, development, use and evaluation of AI products, services and systems. Its trustworthiness characteristics are not independent checkboxes: improving one can create trade-offs in another, and context determines what is acceptable.
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Privacy cannot be judged from an input box or a short statement saying that data is protected. Draw the path of a representative record from the user’s device through the application, provider, model, logs, retrieval systems, external tools and output recipients. Mark every copy, transformation and person or service that can access it.
Build a data-flow inventory
| Stage | Questions to answer in writing |
|---|---|
| Collection | What prompts, files, metadata, identifiers, telemetry and feedback leave the user’s device? |
| Inference | Which provider, region, model version and subprocessors receive the data? Is it encrypted in transit and at rest? |
| Retention | Are prompts, uploads, outputs, safety-review copies and backups stored? For how long, and can an administrator set a shorter period? |
| Training and improvement | Are inputs or outputs used to train, fine-tune, evaluate or improve a model by default, by opt-in, or under a separate account setting? |
| Access | Which provider staff, administrators, support personnel, contractors and connected services can retrieve content, and what audit trail exists? |
| Subprocessors and tools | Do search, storage, transcription, moderation, analytics or agent tools receive the data? Where are they located? |
| Output and onward use | Where are generated results stored or sent, and can they expose source text, personal information or confidential material? |
| Deletion | How does a user or organization request deletion, what is excluded from deletion, and how is completion verified across replicas and backups? |
Request current, written answers for the exact product tier and configuration. Retention and training terms can differ by account, geography, service and date. Treat an unanswered question as uncertainty; do not silently assume the most favorable interpretation.
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Classify data before allowing it into prompts
Separate public, internal, confidential, personal, regulated and security-sensitive information. Define which classes are prohibited, which require redaction or pseudonymization, and which may be processed only in an approved tenant or local deployment. A policy that merely says “do not share secrets” is weak unless the system can enforce it through access controls, data-loss prevention, filtering or technical isolation.
Consider indirect exposure as well as the original prompt. A retrieval index may contain records that a user is not entitled to see; a generated summary may combine fragments into a new disclosure; logs may preserve sensitive text after the user deletes a chat. NIST’s Generative AI Profile identifies data protection, data retention and transparency around third-party data supplied as model inputs as material concerns, particularly when external generative-AI integrations are involved.
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Threat-model both ordinary and AI-specific attacks
Use a conventional confidentiality, integrity and availability analysis for the application, model files, training data, infrastructure and outputs. Then add threats that arise from machine-learning behavior or from the way an AI system interprets untrusted content.
Core threat categories
| Threat | What can go wrong | Useful control or test |
|---|---|---|
| Confidentiality failure | Prompts, retrieved documents, credentials or generated outputs reach an unauthorized person. | Least-privilege access, tenant isolation, redaction, canary secrets and retrieval-boundary tests. |
| Integrity failure | Manipulated inputs or data cause an incorrect answer, altered record or unauthorized action. | Input validation, signed or versioned data, approval gates and tests with conflicting or poisoned content. |
| Availability failure | Rate limits, outages, resource exhaustion or malicious prompts make the workflow unusable. | Quotas, timeouts, fallback procedures, capacity tests, monitoring and a tested rollback path. |
| Evasion and adversarial input | Carefully crafted text, images or files bypass classifiers, safeguards or business rules. | Threat-specific adversarial cases, independent validation and conservative handling of uncertain results. |
| Prompt injection | Instructions in a document, web page or retrieved record override the intended task or tool policy. | Treat retrieved content as untrusted, isolate instructions from data, restrict tools and require confirmation for side effects. |
| Model extraction | Repeated queries reveal proprietary behavior or facilitate a competing copy. | Authentication, query controls, output monitoring and limits on high-volume probing. |
| Membership inference | An attacker learns whether a particular record appeared in training or evaluation data. | Minimize sensitive training data, assess memorization and restrict revealing outputs. |
| Data poisoning | Malicious or low-quality examples corrupt training, fine-tuning, retrieval indexes or feedback loops. | Provenance checks, review gates, dataset versioning, anomaly detection and the ability to restore a known-good version. |
| Supply-chain compromise | A model file, dependency, container, plug-in or update introduces malicious code or behavior. | Trusted acquisition, hashes or signatures, dependency scanning, isolated execution and change review. |
NIST notes that existing guidance does not comprehensively address every machine-learning security concern, including evasion, model extraction, membership inference and availability attacks. Treat the threat catalogue as an active engineering area, not a finished checklist.
Give connected tools their own authorization review
If the system can send email, alter a record, call an API, execute code, browse a private knowledge base or approve a transaction, model accuracy is only one control. Verify the identity under which each tool runs, the resources it can reach, the maximum effect of one call, the approval required for irreversible actions and the audit record. Test whether an untrusted document can cause a tool call, whether a user can make the model cross a tenant boundary and whether the system fails closed when a tool or policy service is unavailable.
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Demand evidence from the provider and supply chain
Ask for evidence that describes the deployed version and architecture, not a generic marketing statement. For a procurement review, preserve the document title, version, date, scope and unanswered questions in your risk register.
Questions to put to a provider
- What exact model, revision, context limits, safety layers and regions serve this account?
- Are prompts, files, outputs or feedback retained, reviewed by people, or used for training or product improvement? Which settings change that behavior?
- How do deletion requests work across primary storage, logs, replicas, backups and subprocessors?
- Which subprocessors and external tools receive customer data, in which countries, and under what access controls?
- How are tenants isolated, administrators authenticated, privileged actions logged and credentials rotated?
- How are vulnerabilities reported, triaged and disclosed? What is the incident-notification process and customer support path?
- How are model, dependency, safety-policy and infrastructure updates announced, tested and rolled back?
- What task-relevant evaluations, red-team results, known limitations and failure rates can be shared for this deployment?
- Can the organization restrict network access, disable tools, set retention, export logs and suspend the integration quickly?
- Will the provider supply or discuss a software bill of materials (SBOM), service-level agreement (SLA) and independent attestation report where appropriate?
NIST’s procurement guidance points organizations toward due diligence and identifies SBOMs, SLAs and attestation reports as evidence they may request. Controls can differ for a foundation model, a fine-tuned model and an application that embeds tools, so ask about the whole service rather than only the base model.
Check that evidence matches your deployment
A security report for an enterprise region may not cover a consumer tier, a plug-in, a newly released model or a different geographic endpoint. Confirm the dates, scope, exclusions, testing configuration and corrective-action status. A certificate or attestation demonstrates that a defined control was examined; it does not prove that your particular workflow is safe.
Test the configured system before exposing real data
Pre-deployment testing should resemble production, including identity, prompts, retrieval content, tools, rate limits, logging and human approvals. Keep a copy of the configuration and test set so results are reproducible.
- Set the test boundary. Record model and application versions, system prompts or policies, enabled tools, data sources, user roles, region, date and decision owner. Define what counts as an unacceptable disclosure, action or outage.
- Create representative cases. Include normal tasks, ambiguous requests, multilingual or formatted inputs, long context, empty or malformed files, sensitive-data examples and the edge cases that matter to affected people.
- Test authorization boundaries. Use separate accounts and documents to verify that one user, tenant or role cannot retrieve another’s information. Include requests that combine an authorized record with an unauthorized one.
- Test privacy behavior. Place unique canary strings in prompts and retrieval data, then check outputs, logs, support exports and analytics for unexpected copies. Exercise retention and deletion controls and record what remains visible and for how long.
- Test hostile inputs. Try prompt injection in user text and retrieved documents, attempts to reveal hidden instructions, requests for credentials, encoded or obfuscated instructions, poisoned records and inputs designed to evade filters. Test images or files if the deployment accepts them.
- Test tool and action controls. Attempt to trigger every connected tool from untrusted content, exceed the caller’s permissions, repeat an action, alter an approval target or perform an irreversible operation without confirmation.
- Test failure and recovery. Simulate provider outage, timeout, malformed response, rate limit, dependency failure, corrupted index and revoked credentials. Verify safe fallbacks, alerts, manual procedures, rollback and restoration from a known-good version.
- Review quality and uncertainty. Measure task-relevant errors, harmful omissions, inconsistent answers and the rate at which the system appropriately refuses or escalates. Do not treat a fluent answer as evidence of correctness.
- Document limitations and retest. Preserve inputs, expected outcomes, observed outputs, logs, severity, tester, date and environment. Record tests you could not run and why. Retest after material changes.
NIST recommends iterative, documented test, evaluation, validation and verification processes informed by representative AI actors. It cautions that available pre-deployment processes may be inadequate, applied inconsistently or mismatched to the eventual deployment. A handful of anecdotal capability checks or jailbreak demonstrations cannot establish validity or reliability.
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Use frameworks to organize—not replace—judgment
NIST AI Risk Management Framework
AI RMF 1.0 is voluntary and is intended to improve the ability to incorporate trustworthiness into design, development, use and evaluation. Apply its lifecycle thinking before design, during development, in deployment and use, and throughout testing and evaluation. NIST is revising the framework, so verify the current version when you begin a new assessment.
“The Framework is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.”
The framework does not certify a model or determine legal compliance. It helps you assign owners, identify affected people, evaluate trade-offs and keep evidence as the system changes.
OWASP AISVS
OWASP AISVS 1.0, released in June 2026, contains 191 testable requirements across 12 chapters. It provides verification levels and covers areas including training data, deployment, monitoring and retirement. Select a level that matches the impact of your use and use the requirements alongside ordinary application, infrastructure and identity-security verification; AISVS is not a substitute for those controls.
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Run the same task, data boundary, user roles, tools, region and test protocol for each candidate. Broad claims such as “enterprise-grade” or “private” are not comparable evidence.
| Comparison axis | Evidence to compare |
|---|---|
| Data handling | Input use, retention, deletion, training use, logging, staff access, subprocessors and geographic processing. |
| Security evidence | Access controls, vulnerability handling, incident response, supply-chain information and independently reviewed evidence. |
| Deployment fit | Hosting and integration model, connected tools, update control, availability, isolation options and the ability to suspend or roll back. |
| Evaluation evidence | Task-relevant tests, realistic threat coverage, reproducibility, limitations and relevance to production. |
| Governance and recourse | Named owners, change notification, auditability, support, escalation and contractual remedies. |
| Residual risk | Severity and likelihood of remaining harm for the specific people, data and decisions involved. |
A model that performs better on a benchmark may still be the worse choice if it retains sensitive prompts, requires broad tool permissions or cannot provide an acceptable deletion and incident process.
Record residual risk and make a scoped decision
Maintain a risk register with one row per material scenario. Include the asset or data, threat, affected party, likelihood, impact, evidence, control, control owner, residual risk, decision and review date. Distinguish evidence from assumptions, and mark unavailable information explicitly.
Use four possible outcomes
- Approved for the defined use: The stated controls and evidence meet the threshold, with owners and monitoring assigned.
- Approved after specified changes: Deployment waits for concrete fixes such as disabling a tool, shortening retention, adding human approval or obtaining missing evidence.
- Limited use: The system may handle lower-risk tasks or sanitized data while higher-impact uses remain blocked.
- Rejected: Residual harm, uncertainty or lack of control exceeds the organization’s threshold.
Write the scope directly into the decision: model version, service tier, region, user groups, permitted data, enabled integrations, approved tasks and the trigger for reassessment. Approval is not a statement that the model is safe in every context.
Reassess when the system or context changes
Set reassessment triggers before launch. Repeat the relevant threat, privacy and performance tests after a model or safety-policy update, provider or region change, new subprocessor, new retrieval source, new tool permission, retention change, security incident, material drift in outputs, changed user population or new legal or contractual requirement. Revisit the assessment when the task expands from drafting to recommendation or automated action, because the consequences of failure have changed.
Monitor production for access violations, unusual query volume, sensitive strings in logs or outputs, tool-call anomalies, rising error rates, refusals that block legitimate work, and unexplained behavior changes. Define who can pause the system, preserve evidence, notify affected parties and restore a known-good configuration.
Common assessment mistakes
- Evaluating the base model while ignoring the application, retrieval store, plug-ins, agent and permissions around it.
- Assuming that “not used for training” means prompts are not logged, reviewed, retained in backups or sent to subprocessors.
- Relying on a benchmark, model card, certification or a single jailbreak test instead of deployment-specific testing.
- Sending real personal or confidential data into a trial account before retention, access and deletion are understood.
- Accepting an unanswered provider question as a favorable answer.
- Testing only normal users and ideal inputs, rather than hostile documents, compromised accounts, malformed files and outages.
- Approving a model without naming an owner, a suspension procedure or a date and trigger for reassessment.
- Treating a voluntary framework as a legal-compliance determination. Jurisdiction, industry, data type and current product terms still govern those obligations.
Keep legal and regulatory review separate
This process helps identify technical and governance risk; it does not determine whether a use complies with a particular country’s privacy, employment, consumer-protection, sectoral or AI law. Confirm requirements for the jurisdictions, industries and data types involved, and have qualified counsel review current provider terms, cross-border transfers, records obligations and required notices. If the provider cannot describe the data path or your organization cannot enforce the promised controls, pause the use rather than inferring compliance from a general framework.
Make the decision explicit
The defensible question is not “Is this AI model safe?” It is “Is this configured system, for this task, data, people and consequence profile, controlled well enough for us to accept the remaining risk?” A documented answer should show the data path, threat coverage, provider evidence, test results, limitations, owners, scope and reassessment trigger. That record lets you approve a narrow use, require changes, restrict data or reject the deployment for reasons that can be explained and revisited.
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