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Do not treat “AI-powered” as a security verdict. Evaluate the software by the assets it protects, the data it handles, the decisions it can make, the evidence behind its claims, and the controls available when it fails. A sound review covers the provider’s entire AI lifecycle—from design and development through deployment and day-to-day operation—and assigns clear responsibility to people on both sides of the contract.
Start with the security problem, not the AI label
“Uses AI” describes a technology approach, not a defined capability. A product may apply machine learning to endpoint telemetry, use a language model to summarize alerts, correlate identity events, or automate remediation. Those are different jobs with different risks. Begin by writing down the security outcome you need and the environment in which the product will operate.
Define the assets and workflows in scope
- Assets: endpoints, identities, cloud workloads, networks, applications, industrial systems, or data stores.
- Signals: logs, process activity, email content, network flows, authentication events, files, code, or user-submitted text.
- Actions: alerting, ticket creation, account suspension, host isolation, file quarantine, policy changes, or automated blocking.
- Integrations: identity providers, security information and event management systems, ticketing tools, cloud APIs, email systems, and response platforms.
- Operating model: staffed security operations, an outsourced provider, a small IT team, or a mixed model.
Ask the vendor to map every advertised feature to these facts. A generic demonstration can hide unsupported operating systems, missing integrations, excessive permissions, or a workflow your team cannot safely monitor.
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Decide where humans must remain in control
Separate recommendations from actions that change your environment. A model that explains a suspicious login is lower risk than one that disables an administrator account without approval. Require a written description of which actions are automatic, which need approval, how an operator sees the supporting evidence, and how an action can be reversed.
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Use a lifecycle framework to organize due diligence
The NIST AI Risk Management Framework provides four useful functions: Govern, Map, Measure, and Manage. Treat them as an evaluation sequence rather than a certification checklist. CISA and the UK National Cyber Security Centre (NCSC) also publish guidance covering secure design, model development, system development, deployment, and operation. Their joint guidance is aimed primarily at providers, but it explicitly encourages other stakeholders to use it when making deployment and operational decisions. CISA states that its guidelines apply to all types of AI systems, not only frontier models.
Govern: assign ownership and rules
Identify the provider executive responsible for product security, your internal system owner, the incident contact, and the person authorized to disable automation. Require documented access controls, change approval, audit retention, and a route for escalating unsafe behavior. Governance should cover the AI component and the surrounding service, including connectors, dashboards, update mechanisms, and support access.
Map: understand context and exposure
Document the data flows, trust boundaries, user groups, jurisdictions, dependencies, and consequences of an incorrect decision. Record what is outside the product’s protection. For example, endpoint detection may not protect a cloud identity if the identity provider is not integrated; a response assistant may summarize an alert but cannot validate the underlying telemetry.
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Measure: require evidence and limits
Ask how the provider validates detection, classification, explanation, and response claims. Request the test scenarios, data conditions, time period, baseline, and known failure modes. The available guidance does not establish a universal accuracy number for cybersecurity AI, and no single percentage can represent performance across every environment. A vendor’s marketing claim is not independent evidence.
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Manage: prioritize treatment and residual risk
For each material risk, agree on a mitigation, an owner, a deadline, and an acceptance decision. Decide whether to restrict automation, add an approval gate, reduce permissions, retain a second detection source, or reject a use case. Revisit the assessment after major model, connector, policy, or infrastructure changes.
Examine the data the product can see
Data handling is often more consequential than the model name. Obtain a complete inventory of telemetry and content sent from your environment, including fields that may contain personal information, credentials, source code, customer records, or regulated data.
Questions about collection and processing
- What data is collected by default, and what can be disabled?
- Is processing performed in your environment, in a dedicated tenant, or in a shared provider service?
- Which regions process and store the data?
- How long are raw events, prompts, outputs, embeddings, and backups retained?
- Who can access customer data, under what role, and with what logging?
- Is customer data used to train, fine-tune, evaluate, or improve models? Is opt-out contractual and technically enforced?
- How are tenants isolated, and how is data deleted from active systems, replicas, and backups?
Privacy enhancement is one of the trustworthy-system characteristics identified in the NIST-aligned guidance, but the guidance does not impose one universal retention period. Put the exact retention, deletion, residency, and training terms in the contract and verify that the product configuration matches them.
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Check permissions and connected services
List every API scope and service account required. Prefer read-only access for investigation and narrowly scoped, time-limited permissions for response. Ask whether the product supports separate credentials for collection and remediation, approval workflows for high-impact actions, and immediate revocation if the service is compromised.
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Assess how the AI system is secured
An AI feature inherits risk from its models, training or reference data, software dependencies, deployment pipeline, interfaces, and connected services. CISA’s secure-by-design roadmap calls for adapting established security practices to AI systems; the CISA/NCSC guidance addresses the full lifecycle.
Development and supply-chain controls
- Secure coding standards, peer review, threat modeling, and automated testing.
- Inventory and version control for models, datasets, prompts, plugins, libraries, and containers.
- Dependency monitoring, signed releases, protected build systems, and reproducible rollback.
- Access separation between developers, operators, support staff, and model-evaluation teams.
- A vulnerability disclosure process with severity definitions, response targets, and customer notification rules.
Model- and interface-specific protections
Ask how the provider defends against prompt injection, malicious or poisoned reference data, data exfiltration through outputs, unauthorized tool calls, model extraction, and abuse of administrative interfaces. Determine whether inputs and outputs are filtered, whether tool use is allow-listed, and whether sensitive values are redacted before they reach a model. A product that can execute actions through an API needs stronger isolation and logging than one that only displays a score.
Demand product-specific evidence, not impressive demos
A demonstration shows that a product can produce a result; it does not show how often the result is correct in your environment. Ask for an evaluation package that you can reproduce or at least audit.
What to request
- Threat scenarios that match your assets, attack paths, and regulatory obligations.
- Definitions for true positives, false positives, false negatives, alert duplicates, and analyst overrides.
- Validation results, test-set provenance, time period, and environmental assumptions.
- Examples of incomplete, contradictory, delayed, or adversarial input and the resulting behavior.
- Known blind spots, unsupported configurations, and conditions under which automation is suppressed.
- A pilot plan using your telemetry, with success criteria agreed before the trial.
Require the vendor to distinguish laboratory results, customer-reported outcomes, and independent assessments. The available official guidance does not provide comparable product benchmarks or a universal score. If a supplier cannot explain its method or limitations, record that as a procurement risk rather than filling the gap with a marketing percentage.
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Verify human oversight and accountability
Every automated decision needs an accountable owner. Establish who investigates a missed threat, who approves disruptive actions, and who can turn off the feature without waiting for the vendor.
Controls to verify in a pilot
- Trigger a benign test event and confirm that the alert shows the input, reasoning or contributing signals, confidence information where available, and timestamps.
- Submit an ambiguous or incomplete event and observe whether the system requests review, expresses uncertainty, or confidently recommends an action.
- Test an approval-gated response and verify that only authorized users can approve it.
- Execute a reversible action, then use the documented rollback path and confirm that the audit record is complete.
- Disable the AI feature or connector and confirm that core monitoring and manual response still function.
Explainability should be practical: an analyst must be able to understand what to check next, not merely receive a technical description of a model. Keep audit records of inputs, outputs, approvals, actions, policy versions, and model or rule versions.
Plan for change, drift, and failure
AI behavior can change when models, prompts, data sources, policies, or connected services change. CISA identifies machine-learning drift detection and AI system assurance as areas of interest, and its roadmap emphasizes red teaming and secure-by-design work.
Questions about ongoing assurance
- How often are models, detection logic, prompts, and dependencies changed?
- What release validation is performed before a change reaches customers?
- Does the provider red-team the AI system, the surrounding application, and its tool integrations?
- How are changes in data distribution, sensor quality, and alert volume detected?
- Can customers pin a version, stage an update, or defer a material change?
- How quickly are customers notified about vulnerabilities, degraded detection, or behavior changes?
Define an operational fallback for outages, model degradation, and provider lockout. That may include native platform logs, a second detection source, manual playbooks, or a safe mode that permits investigation while blocking automated changes.
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Include all three AI-specific risk classes
A CISA-hosted summary of federal guidance groups system-level AI risks into three broad classes:
| Risk class | What to ask the vendor |
|---|---|
| Attacks using AI | Could attackers use generative or learning systems to scale phishing, malware, reconnaissance, or fraud against your environment? Which product controls address those scenarios? |
| Attacks targeting AI systems | How are models, prompts, training or reference data, APIs, plugins, and outputs protected from poisoning, manipulation, extraction, and unauthorized use? |
| Failures in AI design and implementation | What happens when the model is wrong, uncertain, out of distribution, biased, unavailable, or connected to misleading data? Which safeguards limit the impact? |
The same summary lists event detection among critical-infrastructure AI use categories. That identifies a use case, not proof that AI detection is automatically superior to other methods.
Compare candidates with a consistent scorecard
Use the same questions, evidence requirements, and pilot scenarios for every candidate. Score each category with a written rationale and record unresolved assumptions.
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|---|---|---|
| Coverage and fit | Supported assets, environments, integrations, prerequisites, and operating effort | Does it protect the systems that create your highest risk without forcing an unsafe architecture? |
| Security and privacy | Data flow, access, retention, isolation, update controls, and vulnerability response | Can you meet legal, contractual, and internal data-handling requirements? |
| Evidence and limits | Validation method, test conditions, failure modes, and pilot results | Do you understand when the product works and when it does not? |
| Human control | Alert explanations, approval gates, permissions, audit records, and rollback | Can staff review and reverse high-impact decisions? |
| Ongoing assurance | Red teaming, release controls, drift monitoring, incident response, and notifications | Will you know when behavior or risk changes? |
| Accountability and operations | Named contacts, support model, escalation, ownership, and workflow fit | Is there a clear response when the system misses a threat or disrupts legitimate work? |
This is a practical comparison framework synthesized from risk-management and secure-development guidance, not a published universal scoring standard.
A procurement process that exposes weak claims
- Write the use-case brief. State assets, threats, data classes, integrations, actions, staffing, and unacceptable failure outcomes.
- Send the same questionnaire. Require architecture diagrams, data-flow details, permissions, retention terms, lifecycle controls, evidence methods, and incident contacts.
- Reject unexplained automation. Require approval gates or a documented rationale for every action that can affect availability, access, or data.
- Run a controlled pilot. Use representative telemetry and pre-agreed measures for detection value, analyst effort, false alarms, latency, and safe failure.
- Test adverse conditions. Include missing logs, noisy data, delayed feeds, adversarial inputs, connector outages, and model or policy changes.
- Negotiate operational terms. Cover notification deadlines, audit access, deletion, subcontractors, version changes, support escalation, and exit assistance.
- Approve residual risk. Document what remains unprotected, who accepts it, and which compensating controls are required.
- Review continuously. Reassess after major releases, new integrations, incidents, drift alerts, or changes in the organization’s threat model.
Red flags that should pause a purchase
- The supplier cannot identify what data leaves your environment or how long it is retained.
- “Autonomous” actions are advertised without permission details, approval controls, or rollback.
- Performance claims lack test conditions, definitions, dates, or known limitations.
- The model is treated as a black box and no audit trail is available.
- There is no clear vulnerability disclosure, incident-notification, or emergency-disable process.
- Material model or connector changes can occur without customer notice or release controls.
- The product requires broad administrative privileges that are unrelated to its stated job.
- The vendor refuses a use-case pilot or cannot explain how it handles incomplete and misleading signals.
What a strong answer from a vendor sounds like
A credible provider can describe its system without hiding behind the word “AI.” It will show the data path, list permissions, identify human decision points, provide test methods and limitations, explain update and drift controls, and name people responsible for security and incident communication. It will also tell you what the product does not protect. That level of specificity is more valuable than a model brand, a large feature list, or an unsupported accuracy claim.
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