Do not approve an AI tool because its vendor advertises strong security. Approve a specific use only after you have mapped its data and dependencies, tested it in conditions that resemble deployment, judged the plausible harm against your risk tolerance, assigned safeguards and owners, and established monitoring and rollback rules. This risk-based process applies whether the system is a hosted service, API, embedded feature, open-source model, or combination of components.
What a security-risk assessment is deciding
The decision is not simply whether an AI model is “secure.” It is whether this tool, used for this task, by these people, with these data and integrations, is acceptable in your organization. The same service might be reasonable for public marketing copy and unacceptable for confidential investigations because the information, permissions, consequences and failure modes differ.
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Security cannot be separated from privacy, supplier and operational questions. A prompt containing personal information can create a privacy exposure; a compromised account can expose model-connected systems; an unavailable provider can interrupt a critical process. Treat those as inputs to one decision rather than separate checkboxes.
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The following sequence turns those functions into a working approval record. Adapt the depth to the impact of the proposed use, your sector, jurisdiction, architecture and risk tolerance.
1. Set scope, ownership and the decision boundary
Write down what is being approved before anyone evaluates a product demo. A vague request such as “use an AI assistant” cannot be tested or governed. Define the task, users, environment and authority that can approve it.
Describe the intended use and exclusions
- State the business task in operational terms, such as drafting internal summaries or classifying incoming requests.
- List intended users, their roles and the decisions they may or may not make from the output.
- Specify uses that are out of scope. An assistant approved for drafting may be prohibited from making final eligibility, disciplinary or safety decisions.
- Define the deployment setting: a browser service, an API in your application, an on-premises model, an embedded feature or a combination.
Draw the system boundary
Include more than the model. Record the user interface, identity provider, storage, retrieval system, plug-ins, connectors, monitoring tools, model host, subcontractors and data sources. Note where your organization’s control ends. A direct vendor service, an API wrapped by your software and an open-source model operated by your team have different responsibilities and failure modes.
Assign accountable people
Name a business owner, a technical owner, a security reviewer and the person or committee with authority to accept residual risk. Record who can restrict access, stop the integration and communicate an incident. Approval should not be implicit in a purchase or a developer’s experiment.
Record the decision context
Capture the expected benefit, affected people and processes, criticality, data classification, geographic or regulatory constraints and the date of assessment. State assumptions, such as a requirement that prompts contain only public information. An assumption becomes a control only when it is enforced and monitored.
2. Map data flows, permissions and access
Data mapping answers what can enter the system, what it can retrieve, where it goes, how long it remains and who can see it. Perform this mapping for normal use and for logs, error reports, backups and support access.
Trace every data path
- List information a user can submit, including text, files, images, audio, code and metadata.
- Identify information the system retrieves from internal repositories, third-party services or the public web.
- Document what the provider stores, retains, uses for service improvement or sends to subprocessors. If a point is unknown, mark it as an unresolved risk rather than assuming the safest behavior.
- Record outputs, conversation histories, telemetry, prompts used for evaluation and administrative logs, including retention and deletion arrangements.
- Draw transfers across organizational, national or cloud boundaries and identify the party controlling each transfer.
Classify information and consequences
Mark personal, regulated, confidential, proprietary, security-sensitive and public information. Link each class to an allowed handling rule. For example, a policy might permit public text in a hosted tool but require redaction or a separately approved environment for confidential records. The rule must cover retrieved data and generated logs, not just the original prompt.
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Check identity and authorization
- How are users authenticated, and can accounts use strong organizational identity controls?
- Are permissions inherited from source systems, copied into the AI tool or granted through a service account?
- Can one user retrieve another person’s prompts, files or outputs?
- Are administrator, developer, support and automation privileges separated?
- Can connectors perform write, send, delete or execute actions, or are they read-only?
Minimize permissions and data by default. A model that can read a repository or call an external action has a larger security boundary than a model that only transforms user-supplied text.
3. Identify threats and plausible impacts
Build a threat list from the mapped architecture and intended use. A generic catalog is a starting point, not a complete assessment. Ask how a failure would affect your organization, customers, staff, partners or the public, and how likely and detectable it would be.
Unauthorized disclosure
Consider accidental exposure through prompts, retrieval, conversation history, logs, support channels, model outputs or a misconfigured connector. Test whether a user can obtain information outside their authorization and whether sensitive content is reproduced in an answer or exported file.
Account and privilege compromise
Assess stolen credentials, weak session controls, excessive service-account permissions and administrative takeover. A compromised AI account may provide an indirect route into connected systems, so examine the permissions of every integration rather than treating the model as an isolated application.
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Malicious or manipulated input
Inputs may attempt to override instructions, cause disclosure, poison retrieved context or induce an unsafe action. Include untrusted documents, web pages, user-generated content and data imported from other systems in testing. Define which actions require confirmation by a person.
Unsafe or misleading output
Generated text can be wrong, incomplete or inappropriate for the task. Security impact arises when people or automation trust it without verification, such as when a generated command changes access or a classification routes a sensitive case. Specify review requirements and prohibit autonomous decisions where the consequences exceed the system’s demonstrated reliability.
Provider, supply-chain and dependency failure
Model updates, subcontractor changes, compromised packages, service outages and account suspension can alter behavior or availability. Identify critical dependencies and a fallback process. A tool that cannot be safely disabled or replaced may create operational risk even when its normal behavior is acceptable.
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Misuse and secondary use
Consider deliberate abuse, accidental use outside the approved purpose, and gradual expansion into a higher-impact workflow. Define monitoring and access boundaries that make prohibited uses visible.
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4. Assess the supplier and every dependency
Vendor claims are evidence to examine, not a substitute for an assessment. NIST’s Generative AI Profile (NIST AI 600-1), published July 26, 2024, recommends updating procurement due diligence for generative AI to address intellectual property, data privacy, security and other risks. It also recommends use-case-based supplier assessment, approved-provider inventories, monitoring and contract terms that support evaluation of third-party processes.
Questions to put to the provider
- Which model, hosting, storage, moderation, retrieval and support components process your data?
- What information is retained, for how long, in which regions and for what purposes?
- Can your data or prompts be used to train or improve a service, and can that use be controlled?
- How are vulnerabilities and security incidents detected, reported and remediated?
- What independent assurance, audit information or testing evidence is available for the service and relevant subprocessors?
- How will you be notified of material changes to the model, processing, permissions, subprocessors or terms?
- What availability commitments, recovery arrangements, export options and termination procedures exist?
- Can the organization test the service, review relevant controls and obtain evidence without exposing other customers’ information?
Evaluate evidence for this use case
Match each answer to your architecture and impact. A general security certification may not address prompt retention, a newly added connector or the behavior of a particular model version. Record evidence, its date and scope, along with gaps and compensating controls. Include contractual provisions for incident notification, cooperation with evaluation, data handling, material-change notice, deletion or return, and access to the information needed for risk review.
Manage the provider relationship
Maintain an inventory of approved providers and the uses for which each is approved. Assign an owner to review service notices, incidents, model changes and performance. Reassess when a provider, model, subprocessor, data path, permission or intended use changes materially.
5. Test in conditions that resemble deployment
Laboratory demonstrations rarely reveal deployment risk. Test with representative tasks, data formats, integrations, user roles and permissions, while protecting real sensitive information. Document the plan, tools, cases, findings, limitations and residual risks.
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Test security boundaries
- Attempt to retrieve information belonging to another role or tenant.
- Use untrusted documents and instructions to see whether the system reveals data or bypasses safeguards.
- Check connector permissions, confirmation prompts and the ability to revoke access.
- Review logs and exports for secrets, personal information and excessive retention.
- Test account recovery, administrator actions, rate limits and behavior after a credential or integration is disabled.
Test resilience and safe failure
Simulate provider unavailability, slow responses, malformed inputs, quota exhaustion and model or connector errors. Verify that the workflow stops safely, does not repeat an action unexpectedly and provides a usable manual or alternate path. If staff must make a decision during an outage, document who takes over and what information remains available.
Test human oversight
Give reviewers realistic outputs, including incorrect, ambiguous and adversarial cases. Measure whether they can detect problems with the time, information and training available. A nominal approval step is not an effective control if reviewers cannot inspect source data, understand uncertainty or stop an action.
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State test limitations
Testing cannot prove that no harmful behavior exists. Record untested languages, data types, user groups, integrations, scale conditions or future model versions. Treat those limitations as residual risk and define what additional evidence is required before expanding use.
6. Decide, treat risk and document residual exposure
Use the evidence to compare expected benefits with negative risks and your organization’s tolerance. The result should be an explicit decision, not a general statement that the tool is “secure.”
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| Disposition | When it fits | Required record |
|---|---|---|
| Proceed | Benefits justify the residual risk and required safeguards are implemented. | Approved scope, controls, owners, monitoring and review date. |
| Restricted use | The tool is acceptable only with limits such as approved users, data classes, read-only integrations or mandatory human review. | Enforced restrictions, exception process and evidence that limits work. |
| Defer | Important evidence, testing, contractual terms or controls are missing. | Open findings, owner, due date and conditions for reconsideration. |
| Reject | Potential harm exceeds tolerance, cannot be controlled or lacks a viable fallback. | Reason, affected proposal and any safer alternative process. |
Specify safeguards and owners
For every material risk, name the control, accountable owner, implementation status and verification method. Controls may include data minimization or redaction, role-based access, read-only connectors, approval gates, environment separation, logging, alerting, staff training, contractual requirements and a tested shutdown procedure.
Accept residual risk deliberately
List what remains after controls, why it is acceptable or not, who accepts it and until when. Risk acceptance belongs to the designated decision authority, not solely to the tool’s user or supplier. NIST’s Manage guidance describes weighing risks against benefits; the NIST risk-management process also calls for reporting, remediation and updated system plans.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Monitor the system after launch
Deployment is the start of monitoring, not the end of assessment. Track functionality and behavior in production, security events, privacy incidents, access changes, provider performance and user reports.
Define routine checks
- Review access, connector permissions, administrative activity and unusual volume.
- Sample outputs for disclosure, unsafe instructions, unacceptable errors and policy violations.
- Monitor provider incidents, service health, model versions, subprocessors and material notices.
- Confirm that logs, retention and deletion still match the approved design.
- Reconcile actual use with the approved task, users and data classes.
Set reassessment triggers
Require a new review after a model or provider change, a new integration, a permission expansion, a change in data sensitivity, a new user group, a security incident, a significant outage, a change in intended use or evidence that safeguards are failing. Do not rely on an annual date alone when a material change occurs.
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Define how users report suspected disclosure, account compromise, harmful output or unsafe automation. Identify who can suspend accounts, revoke tokens, disable connectors, block a model version, notify affected parties and restore a manual process. Test those actions before a crisis. Preserve relevant evidence while following your privacy and retention requirements.
Questions that belong in the assessment record
Use the following prompts as a working record, tailoring them to the system. They are practical questions derived from NIST’s functions, not a regulator-approved checklist or proof of compliance.
- What task is the system meant to perform, and which uses are explicitly outside scope?
- Which users, data, models, services, integrations and third parties are inside the boundary?
- What information can be submitted, retrieved, stored, retained or used by the provider?
- Which plausible security, privacy, supply-chain and operational failures could affect the organization or people?
- What evidence supports the provider’s security claims, and how will the organization learn about incidents or material changes?
- What tests reflect the real deployment setting, and what are their limits?
- Which safeguards, monitoring, owners, incident procedures and rollback conditions are required?
- Who accepts residual risk, and what change will trigger reassessment?
A compact decision-record format
Use and scope: [task, users, exclusions, environment]
System boundary: [model, provider, data sources, integrations, subprocessors]
Data handling: [inputs, retrieval, retention, training use, regions, logs]
Threats and impacts: [scenario, affected party, likelihood, consequence]
Evidence and tests: [source/date, test conditions, findings, limitations]
Controls and owners: [safeguard, owner, status, verification]
Decision: [proceed, restricted use, defer, reject]
Residual risk acceptance: [authority, rationale, expiry or review date]
Monitoring and triggers: [metrics, incident path, reassessment conditions]
Rollback: [disable, revoke, notify, manual fallback]How to compare two AI tools fairly
Compare tools against the same task, data, users, integrations and fallback assumptions. A feature checklist or a vendor’s general ranking is not a risk assessment. Use evidence from testing and supplier review for each axis.
| Comparison axis | Questions to answer |
|---|---|
| Data handling and retention | What enters, leaves, persists, trains the service or appears in logs, and can those settings be controlled? |
| Identity and access | Can organizational authentication, least privilege, role separation and rapid revocation be enforced? |
| Integrations | What can connectors read or change, and are high-impact actions gated by a person? |
| Dependencies and transparency | Are model, hosting, subprocessors and material changes disclosed sufficiently for your use case? |
| Supplier assurance | What incident, vulnerability, audit and cooperation evidence is available, and what is contractually committed? |
| Testing and monitoring | Can you test realistic behavior, inspect relevant logs and detect drift or misuse in production? |
| Resilience and fallback | What happens during outage, degradation, suspension or termination, and how quickly can you revert? |
| Human oversight | Can reviewers understand, challenge and stop outputs or actions before harm occurs? |
| Residual risk | After controls, is the remaining exposure within the organization’s stated tolerance? |
Do not turn these axes into a universal vendor ranking. The appropriate choice depends on the specific deployment context and the strength of controls you can actually enforce.
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“The provider says it is enterprise secure, so approval is automatic.”
Correction: Ask whether the evidence covers your data paths, integrations, retention, model version and use case. Record gaps and compensating controls.
“We tested prompts in a demo account.”
Correction: Re-test with representative data, roles, connectors, adversarial inputs, outage conditions and human review. Document what the demo could not establish.
“The model is the whole system.”
Correction: Include identity, storage, retrieval, plug-ins, logs, support access, subprocessors and fallback operations in the boundary.
“A policy says not to enter sensitive data.”
Correction: Enforce the rule with technical restrictions, redaction, permissions, training, monitoring and an incident path. Treat unverified assumptions as open risks.
“Approval is permanent.”
Correction: Set reassessment triggers for provider, model, permission, integration, data or use-case changes, and monitor behavior after launch.
What this process cannot decide for you
General guidance does not determine the legal requirements, assurance evidence or technical tests applicable to a particular organization. Those depend on jurisdiction, sector, data, architecture, affected people and intended use. A voluntary framework does not guarantee security or replace obligations imposed by law, contract, internal policy or a regulator. Obtain specialized legal, privacy, safety or security advice when the consequences or requirements demand it.
The defensible outcome is a documented, use-specific decision: proceed, restrict, defer or reject. It names the system boundary, data handling, threats, supplier evidence, realistic test results, safeguards, residual-risk owner, monitoring plan and conditions that force a new review.
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