Use AI to support specific cybersecurity tasks—not as a substitute for a security program. Start by identifying the asset or workflow you need to protect, then map the AI system’s data and access, assess its risks, limit what it can do, test its outputs, and monitor it after deployment. AI can add defensive capabilities, but it can also expand the attack surface and help attackers; no framework or tool guarantees stronger security.
Where AI can fit in a business security program
AI may support defensive work such as reviewing security information, helping staff investigate alerts, or assisting with security workflows. Whether it improves a particular business’s security depends on the task, the system’s access and reliability, and the controls around it. NIST describes AI as a technology that can create new defensive capabilities while also enhancing attackers’ capabilities; that is a reason to evaluate a use case, not assume a benefit. See NIST’s overview of AI security and resilience.
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Before adopting a tool, write down the security problem it is meant to address: the asset or workflow, the threat, the control already in place, and the outcome you want. If you cannot explain how the AI changes that outcome, do not deploy it simply because it is available.
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1. Define the use case and owner
Choose a bounded task and name the person accountable for the system. Specify what the AI may recommend or do, who reviews its work, and how staff can escalate a suspicious result or failure. Keep decisions with serious business, privacy, or security consequences subject to human authorization.
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2. Inventory the system, data, and dependencies
Record the service and model or provider where known, the data sources it can reach, connected applications, permissions, retention terms, and responsible staff. Include software, hardware, and vendor dependencies. NIST notes that wider AI use increases the need to understand data dependencies and may require updating asset inventories; see its discussion of cybersecurity and privacy risks in the age of AI.
Pay particular attention to whether sensitive information could be sent to the service, retained, or used in ways your organization has not approved. Check the provider’s data-handling terms against your privacy and security requirements rather than relying on a general claim that a tool is secure.
3. Assess risks beyond the model
Evaluate confidentiality, integrity, and availability across the full system: the AI service, data, software, hardware, integrations, and supporting processes. Also consider privacy and supply-chain exposure. AI-specific risks can include evasion, model extraction, membership inference, and attacks that affect availability. NIST cautions that existing guidance does not comprehensively cover the full AI attack surface.
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For large language model (LLM) applications, account for risks such as prompt injection and data leakage. OWASP’s GenAI LLM Top 10 2026, dated August 3, 2026, addresses application-level risks. OWASP also provides a cybersecurity and governance checklist for business and technology leaders and security teams.
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4. Restrict access and potential impact
Give the AI service and any connected agents only the data and permissions needed for the task. Separate experiments from production systems and sensitive data where feasible. Require a person to authorize consequential actions, and define what the system must not access or change. These are prudent implementation measures, not a claim that a framework prescribes one universal configuration.
5. Test before relying on results
Test ordinary cases, likely errors, adversarial inputs, and failure modes. Check whether the system exposes data, follows malicious instructions, produces unreliable recommendations, or takes an action outside its intended scope. Document its limits and establish when a person must verify a result. Repeat testing when the model, data, permissions, provider, or intended use changes; AI systems and their risks evolve.
6. Monitor, respond, and reassess
Log relevant use and access, review unusual activity, and maintain a fallback process for outages or unreliable outputs. Make sure incident-response and staff-training procedures account for AI-enabled threats. NIST notes that AI voice generators may require organizations to update anti-phishing training. Reassess the system when its business purpose, data, integrations, provider, or permissions change.
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Frameworks help organize risk decisions; they are starting points, not certifications, guarantees, or substitutes for controls suited to your environment.
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| Resource | How a business can use it | Scope and status |
|---|---|---|
| NIST AI Risk Management Framework (AI RMF) | Use its Govern, Map, Measure, and Manage functions to organize AI risk work across design, development, use, and evaluation. | Voluntary. AI RMF 1.0 was released January 26, 2023; the Generative AI Profile followed July 26, 2024. NIST says the framework is being revised. |
| NIST CSF 2.0 Small Business Quick-Start Guide | Use it to organize cybersecurity outcomes and consider where AI use fits within the broader security program. | Published February 26, 2024; aimed at small-to-medium organizations with modest or no cybersecurity plans. |
| NIST SP 1314 | Use it as an introduction to designing an information-security and privacy risk-management program. | Published July 23, 2024; intended for small, under-resourced entities. |
| OWASP GenAI Security Project | Consult its LLM application-security guidance and governance resources when assessing a generative AI deployment. | Community guidance, not a certification or security guarantee. |
NIST also announced an AI RMF profile for trustworthy AI in critical infrastructure at the concept-note stage on April 7, 2026. Check the relevant NIST page for current status before relying on a profile or framework version.
How to evaluate an AI security tool or service
Compare candidates against the same use case and requirements. Ask for evidence that supports performance claims in your environment; the cited guidance does not establish a numerical breach reduction, return on investment, or proven winner among AI security products.
- Security outcome: What workflow does it cover, and what existing control would it supplement or change?
- Data handling: What information does it access, retain, or use for training, and how does that fit your privacy requirements?
- Access and integrations: Which accounts, systems, and data sources can it reach? Can permissions be limited to the task?
- Testing and monitoring: How can you test errors, adversarial inputs, and failure modes, and review activity after launch?
- Human oversight: Which actions require approval, and how are uncertain or consequential outputs escalated?
- Vendor and deployment fit: What dependencies does it introduce, and does its deployment model suit your security and operational needs?
What AI does not replace
AI does not remove the need to secure ordinary software, hardware, data, identities, and business processes. Nor does using a framework make a deployment safe: NIST’s AI RMF is voluntary, not a certification or legal safe harbor, and NIST says it is being revised. Treat AI as part of established cyber risk governance, with an accountable owner, appropriate controls, and continuing review.
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