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Evaluate an AI security tool as a risk decision, not a feature checklist. Before choosing a vendor, define the threats and assets you need to protect, verify how the product handles your data, examine independent test evidence, and run a time-limited pilot against cases that reflect your environment. The purchase is not complete until its security, privacy, operating, and exit commitments are clear in writing.
What should you decide before evaluating vendors?
Write down the problem the tool is meant to solve before reviewing product claims. “AI security” can refer to different capabilities; a tool that addresses one threat may not address another, and a broad claim does not establish coverage for your system.
Describe the system and its risks
Document the AI systems, applications, models, users, and workflows in scope. Identify the assets at risk, such as sensitive prompts, files, model outputs, credentials, business data, or intellectual property. Note applicable regulatory or contractual constraints, where data may be processed, and the operational consequences of a missed threat or an incorrect alert.
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Then identify the specific threats the product must help prevent, detect, or investigate. Depending on your use case, these may include prompt injection, data leakage, model abuse, supply-chain compromise, unauthorized access, or operational failure. Ask vendors to map each claimed capability to the threats it covers—and state what it does not detect. A gap is easier to manage when it is named than when a broad product description obscures it.
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Set acceptable outcomes and failure modes
Decide what success means for your organization before a demonstration or pilot. For example, specify which test cases must be detected or blocked, what kinds of false alarms your team can handle, and how quickly staff must be able to investigate an alert. The thresholds should reflect the business impact and your ability to respond; there is no universal accuracy figure that can substitute for them.
Also record unacceptable outcomes. These might include sensitive data being sent to an unapproved service, a security control failing silently, or an update changing behavior without notice. Distinguish controls that must be present at launch from risks that could be accepted temporarily with a documented compensating control.
How should you check what happens to your data?
Ask for a complete account of the data the tool collects, where it goes, how long it remains, who can access it, and what the vendor may do with it. Treat verbal assurances as incomplete until the relevant commitments are documented in the contract.
Build a data-flow inventory
Ask the vendor to identify whether each of the following leaves your environment, where it is processed and stored, and whether it is retained:
- Prompts, files, model inputs, and model outputs.
- Telemetry, security events, diagnostic data, and logs.
- Account, device, identity, and configuration information.
- Data sent to the vendor or to any subprocessor.
Find out whether any of these data types are used to train or improve vendor models. Ask what controls apply to that use and whether your organization can opt out. Do not assume that a product’s security purpose means its collected data is never used for another purpose.
Turn answers into commitments
For each relevant data type, establish processing and storage locations, retention periods, deletion procedures, encryption, and access controls. Ask how the vendor verifies that data has been deleted at the end of a retention period or when service ends, and what happens to backup copies. If your organization has residency constraints, confirm which service functions and subprocessors are covered by the stated location—not just where the primary service is hosted.
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Request a current subprocessor list and a process for notifying customers of changes. Make sure the contract says what happens if the vendor cannot meet an agreed privacy, residency, retention, or deletion requirement. NIST’s Generative AI Profile recommends updating acquisition and procurement assessments to address intellectual property, data privacy, security, ongoing monitoring, and vulnerability databases; those topics are useful prompts for this review.
What should you inspect in the tool’s architecture?
Review how the product connects to your systems and what access it needs. A useful capability can still add unacceptable risk if it requires excessive permissions, creates poorly controlled data paths, or behaves unpredictably when an integration fails.
Examine access and deployment
Ask for architecture and deployment documentation that explains network placement, tenant isolation, identity controls, and the permissions used by each connector or API. Check whether access follows least privilege and whether administrators can limit scopes, separate roles, and revoke access. Have your technical team validate the proposed configuration against your own environment rather than relying on a generic diagram.
Find out what is logged, what can be exported, and whether the product integrates with the logging, SIEM, or SOAR systems you rely on. Confirm the formats available and whether exported records contain enough context for an analyst to understand and investigate an event. Clarify what the tool does when a connector, network path, or dependent service is unavailable: does it fail open, fail closed, queue activity, or alert an operator? The right behavior depends on the use case, but it should be deliberate and documented.
Review dependencies and the supply chain
Request documentation of dependencies, software components, and the model supply chain relevant to the product. Ask how the vendor tracks vulnerabilities in those components, evaluates model or dependency changes, and communicates material changes to customers. A product’s own controls do not eliminate the need to understand the software and services on which it depends.
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What evidence should you ask an AI security vendor for?
Request evidence that shows how the product was tested, what was in scope, when testing occurred, what was found, and what changed afterward. A product demonstration or a single accuracy claim does not establish that the tool will work against your threats or within your environment.
Review independent testing
Ask for penetration-test or red-team reports, including the scope, dates, methods, limitations, and unresolved findings. Request remediation evidence and retest results for material issues. Establish whether the assessment covered the product and deployment model you would buy, rather than a different configuration or a limited part of the service.
Check who performed the work and whether the tester was independent. The UK Code of Practice for the Cyber Security of AI says, “System Operators shall conduct testing prior to the system being deployed with support from Developers,” and recommends independent testers. The Australian Cyber Security Centre’s guidance on choosing secure and verifiable technologies advises: “Choose technology that can be independently tested and verified.” These principles support asking for evidence your team can scrutinize, not just a vendor’s summary of results.
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Use a security baseline, not a badge
Map your requirements to OWASP’s AI Security Verification Standard (AISVS) 1.0. The OWASP Foundation describes it as containing 191 requirements across 12 chapters and three appendices, with three verification levels; OWASP says most production systems should target at least Level 2. Choose a level based on the sensitivity of the data, business impact, and attacker capability in your case. A mapped assessment helps expose missing controls, but it does not replace testing of the actual deployment.
The NIST AI Risk Management Framework 1.0 was released on January 26, 2023, and NIST released its Generative AI Profile on July 26, 2024. The framework is voluntary guidance for managing AI risks. Use it as a way to organize risk questions and procurement review, not as evidence that a particular vendor or product has been independently certified.
How do you run a meaningful pilot?
Run a time-boxed pilot before deployment using representative data that is safe to share, prewritten attack and failure cases, agreed success thresholds, enabled logging, and a rollback plan. A pilot should test the product in conditions close enough to your intended use that the results can inform a purchase decision.
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- Define the boundary. Specify which users, systems, integrations, data types, and product capabilities are in scope. Keep the pilot away from sensitive production data unless your approvals and data-handling controls allow it.
- Prepare test cases. Include benign cases as well as adversarial and failure cases tied to your threat model. Cover the threats you expect the tool to address and situations in which it could miss an issue, produce an unnecessary alert, or disrupt a workflow.
- Agree on success criteria in advance. Set thresholds for required detections or prevention, false alarms, investigation effort, and operational behavior. Make the thresholds measurable and define how the results will be recorded.
- Enable logging and observe the workflow. Capture alerts and relevant product behavior so reviewers can see what happened and how an analyst would act on it. Measure precision, recall, and analyst workload in your environment rather than treating vendor figures as a substitute for local results.
- Test integration and failure behavior. Check permissions, exports, alert routing, and the behavior of connectors or dependencies when unavailable. Confirm that a failure is visible to the people responsible for the system.
- Compare results with the baseline. Record which cases passed or failed, how the tool explained its decisions, and what manual work remained. Separate product limitations from issues caused by configuration or pilot scope.
- Exercise rollback. Confirm that the team can disable or remove the tool and revoke its access without losing necessary records or disrupting dependent systems.
CISA and NSA guidance on AI system security focuses on confidentiality, integrity, availability, vulnerability mitigation, and response to malicious activity. Use those areas to check that the pilot considers more than detection: the tool should also be assessed for the ways it might affect the availability and integrity of the systems it touches.
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Use the same weighted scorecard for every finalist, based on your threat model and operating constraints. A vendor’s single accuracy number cannot capture differences in coverage, privacy, integration effort, evidence quality, or the workload created for your team.
| Evaluation area | What to compare | Evidence to record |
|---|---|---|
| Threat coverage | Coverage of the specific threats and failure modes in your scope; known blind spots. | Threat-to-capability mapping and pilot results for agreed cases. |
| Detection and response | Precision, recall, false-positive and false-negative handling, and analyst workload in your environment. | Observed results, alert examples, and the work needed to investigate them. |
| Independent evidence | Scope, recency, methods, independence, open findings, remediation, and retests. | Reports and supporting evidence, with gaps noted. |
| Privacy and residency | Data collected, processing and storage locations, retention, deletion, training use, and subprocessors. | Data-flow answers and contract commitments. |
| Deployment and integrations | Permissions, configuration effort, network placement, tenant isolation, logging, and failure behavior. | Architecture documentation and pilot observations. |
| Explainability and auditability | Whether alerts are interpretable and evidence can be exported and reviewed. | Sample alerts, exported records, and analyst feedback. |
| Supply-chain transparency | Documentation of dependencies and how vulnerabilities or changes are handled. | Dependency information and the vendor’s change and vulnerability processes. |
| Operations and incidents | Monitoring, versioning, update testing, incident notification, support, and rollback. | Operating procedures and written service commitments. |
| Portability and exit | Documented APIs, exportability, migration assistance, and termination arrangements. | Export examples and contract terms for transition and termination. |
| Lifecycle cost | Five-year total cost, including deployment and ongoing operational effort. | Comparable cost assumptions and internal workload estimates. |
Assign weights before scoring so one attractive feature cannot silently outweigh a requirement that matters more to your organization. Keep the underlying evidence beside each score, label assumptions, and distinguish a demonstrated result from an answer that has not been verified. Include internal deployment, integration, review, and analyst effort in the lifecycle comparison; a purchase price alone does not show the cost of operating the product.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What belongs in the contract and ongoing operating plan?
Contract terms should preserve the security, privacy, and operating conditions on which your approval depends. The operating plan should make clear how the tool will be monitored and what happens when its model, rules, or integrations change.
Document privacy, security, and incident terms
Translate agreed data flows, processing locations, retention, deletion, training use, subprocessors, encryption, and access controls into written commitments. Include how and when the vendor will report incidents that affect your data or service, what information the report should provide, and how the parties will coordinate response. Establish who is responsible for vulnerability handling and for informing you of relevant changes or unresolved issues.
Control changes and maintain an exit path
Ask how models, detection rules, prompts, and connectors are versioned; how updates are announced and tested; and how drift or unusual behavior is monitored. Agree on support and escalation arrangements, rollback options, and what happens when the product reaches end of life. For material changes, define what will trigger a reassessment or new testing before the change is relied on.
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Require interpretable alerts, exportable evidence, documented APIs, migration assistance, and a clear termination path. UK procurement guidance warns against black-box algorithms and vendor lock-in. These requirements matter both for routine auditing and for the point when you need to switch providers or remove the product.
How do you document the buying decision?
Keep a decision record that makes the approval understandable to someone who did not attend the vendor meetings. It should connect the organization’s needs to evidence, unresolved issues, and the person accountable for accepting any remaining risk.
- Business problem, assets, threat model, users, data types, and applicable constraints.
- Requirements, scoring weights, success thresholds, and the reason each was chosen.
- Pilot scope, test cases, results, limitations, and configuration assumptions.
- Independent evidence reviewed, open findings, and any remediation or retest information.
- Contract commitments, operational owners, compensating controls, and support arrangements.
- Open risks and the name of the person accepting residual risk.
Reassess after major model, connector, or policy changes. NIST’s procurement guidance emphasizes ongoing monitoring as well as acquisition assessment; a tool’s risk profile can change after the initial decision, so approval should be treated as a lifecycle responsibility rather than a one-time purchase gate.
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What is the difference between an AI security tool and an AI governance tool?
The labels alone do not establish what a product does. Ask each vendor to map its functions to your concrete threats and procurement or operating needs; use that mapping to identify whether it provides technical controls, helps organize risk management, or claims to do both.
Can a vendor’s SOC report or certification prove that its AI tool is secure?
A report or certification only speaks to its stated scope, period, and criteria. It cannot by itself establish that the product covers your threat model or that your configuration is safe, so review the scope and pair it with product-specific evidence and a pilot.
Should an AI security tool be allowed to block activity automatically?
That depends on the consequences of both a missed threat and a mistaken block. Decide which actions can be automated safely, which require human review, and how staff can reverse an action; test those choices in the pilot before enabling them broadly.
When should I repeat an AI security tool assessment?
Revisit the decision when a material change could alter risk or performance, such as a major model, connector, or policy change. Your operating plan can also set review triggers tied to incidents, unresolved findings, or changes in data handling.
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