You can’t establish that an AI security tool protects your business from a feature list or vendor demonstration alone. Define what it must protect, test the complete system against realistic business risks, review how it handles those risks without disrupting legitimate work, and keep the evidence current as your environment changes.
What does “protecting your business” mean?
Effectiveness depends on what the tool is expected to protect and how your organization uses it. Start with the business context: identify important systems, data, people, and operations; the threats that could affect them; and the level of risk your organization can accept. NIST’s Cybersecurity Framework describes this kind of risk-based approach to prioritizing cybersecurity work: NIST CSF 1.1 Five Functions.
For an AI-enabled application, define the scope broadly enough to include the components that shape its behavior. Depending on the deployment, that may mean prompts, retrieval sources, connected APIs, tools the AI can use, access permissions, and human review—not just the underlying model. NIST’s AI evaluation guidance emphasizes tailoring assessments to organizational objectives and how a system is used: NIST’s TEVV-Athlon Framework.
How to check whether the tool works
1. Write down the protection claim
State which assets and workflows are in scope, what threats the tool is meant to address, and what actions it is expected to take. Be specific enough that a tester can distinguish a successful outcome from a vague promise such as “improves security.” For example, define whether the tool should flag a particular kind of risky activity, prevent an unauthorized action, or provide evidence that lets staff investigate it.
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2. Define success before testing
Agree on measurable outcomes before running an assessment. Record which test events should be detected or blocked, what response should follow, what evidence should appear in logs or alerts, and how much disruption to normal work is unacceptable. Document assumptions and risk tolerances so the results can be interpreted in context.
This is a practical scorecard, not a universal set of metrics prescribed by NIST. NIST’s guidance supports grounding evaluation in business risk and system objectives; the organization must decide which outcomes matter for its own deployment.
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3. Combine complementary forms of testing
Use more than one kind of evidence. NIST’s ARIA Evaluation Planning Manual, published September 18, 2026, describes an approach combining model testing, red teaming, and user testing. These address different questions:
- Capability or model testing: Do the expected safeguards work on relevant, planned scenarios?
- Red teaming: Can authorized testers expose realistic paths around those safeguards?
- User testing: Does the tool work as intended in the actual workflow, for the people who rely on it?
Keep testing authorized and scoped to the environment. A polished demo or a collection of jailbreak examples is not a substitute for examining the integrated business application and its real operating conditions.
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4. Review operational evidence
Inspect alerts and logs to see whether relevant activity was detected, whether staff could understand its impact, and whether response, containment, and recovery steps worked. NIST’s Cybersecurity Framework includes continuous monitoring, response, recovery planning, and learning from incidents as parts of cybersecurity risk management (NIST CSF 1.1 Five Functions).
Check the chain from detection to outcome: what signal appeared, who received it, what they did, and whether the business process recovered as intended. A detection that cannot be investigated or acted on may not provide the protection the organization needs.
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- FAST & CONVENIENT LOGIN: Plug in your Security Key NFC via USB-A and tap it, or tap it against your phone (NFC) to authenticate. No batteries, no internet connection, and no extra fees required.
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5. Include normal business activity
Test legitimate work alongside suspicious or adversarial scenarios. A tool should not be considered effective simply because it blocks activity that looks risky; assess whether it also interferes with ordinary tasks, creates unmanageable false alarms, or causes staff to work around it. The sources cited here do not establish a universal acceptable false-positive threshold or guarantee results for a particular commercial product, so set limits appropriate to your business.
6. Keep records and repeat the evaluation
Retain the scenarios, dates, configurations, observed results, deviations, incident findings, and remediation decisions. Revisit the assessment after meaningful changes to the model, configuration, connected data or services, business use, or threat assumptions. NIST’s preliminary draft Cybersecurity Framework Profile for Artificial Intelligence discusses continuously evaluating whether defensive AI capabilities are mature enough for an organization’s needs. It is draft guidance, not a finalized requirement, and the cited sources do not prescribe one universal review interval.
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- Dedicated Manager App: Use the Thetis Manager App for the initial hardware PIN setup. Setting the PIN on the device first ensures a smooth registration process. Once the PIN is configured, you can begin registering the key across your favorite FIDO2-compatible online services.
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- Check FIDO2 compatibility before purchase - Known limitations: ID Austria is not supported (requires FIDO2 Level 2). Windows Hello login only works with Windows Enterprise editions that support Entra ID. NFC functionality is not supported.
How to evaluate an external red-team provider or tool
If your team is considering outside help, compare providers and tools by what they can actually assess and how they substantiate their findings. OWASP’s Vendor Evaluation Criteria for AI Red Teaming Providers & Tooling v1.0, dated February 4, 2026, is intended to help assess offerings from simpler GenAI systems through advanced agentic applications.
- Scope: Does the assessment cover only a standalone model, or the integrated application, retrieval, APIs, agent tools, identities, and business logic relevant to your use?
- Threat realism and coverage: Are scenarios tied to your business risks, and are limitations documented?
- Evaluation rigor: Are tests repeatable, methods transparent, and findings supported by useful evidence and human validation where appropriate?
- Operational fit: Can the work fit safely into your development or monitoring process and produce results your team can use?
- Governance: Are authorization, sensitive-data handling, reporting, and remediation responsibilities clear?
These criteria help structure a comparison; they do not independently prove that a particular vendor performs well. The available guidance does not support naming a best provider or tool.
What a reference architecture can—and cannot—show
NIST’s NCCoE SP 1800-26B practice guide offers an example of a cybersecurity reference design and its evaluation. Its lab environment does not represent the full complexity of production, and the guide does not endorse its commercial products. Treat a reference architecture as a starting point for adapting and testing controls, not evidence that the same configuration will protect another organization.
Limits of vendor claims and published evidence
The cited NIST and OWASP materials describe evaluation methods, risk-management concepts, and provider-selection criteria; they do not provide a cross-vendor effectiveness rate or a universal pass score. NIST’s TEVV-Athlon page describes an initial public draft, with comments closing October 6, 2026; as of October 4, 2026, it was still a draft with the comment period open. Use it as draft guidance rather than a finalized standard. The NIST AI Resource Center maintains AI risk-management and TEVV resources at airc.nist.gov.
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