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Three cybersecurity problems sit under “AI for cyber security”
AI can be part of the security problem, part of the defense, or part of an attack. Keeping those roles distinct helps teams choose appropriate controls instead of assuming that adding an AI tool automatically improves security.
Secure AI systems
Protect the model or agent and the environment around it: its data, accounts, integrations, connected tools, and operating process. NIST’s May 18, 2026 synthesis of responses about AI agent security says respondents identified novel threats and that established cybersecurity practices may need adaptation for agents. It summarizes submitted views; it does not measure how often particular attacks happen or how severe they are.
Use AI in cyber defense
AI may assist with defensive analysis or planning, but the task and its authority should be explicit. A system that drafts an assessment for review is different from one that can change configurations, access sensitive records, or take action in production.
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Address AI-enabled attacks
NIST’s Cyber AI Profile frames thwarting AI-enabled attacks as a distinct area alongside securing AI systems and using AI for cyber defense. NIST described the profile as a preliminary draft in December 2025; treat it as emerging guidance, not a finalized universal control standard.
Where AI can help—and what the examples do not prove
NIST’s August 19, 2026 initial public draft, SP 1353, gives examples of using generative AI to support work with the Cybersecurity Framework (CSF) 2.0. These are ways to assist analysis and drafting, not evidence that a model can certify compliance or provide assurance.
Review governance material
AI can help review policies, strategy, and risk-governance material against CSF outcomes. Treat the result as a review aid: people still need to determine whether the material is current, relevant, and sufficient for their organization.
Draft a current-state profile
A model can help map organizational documents and interview notes to a current-state profile. Record the assumptions behind the mapping and the evidence gaps, so reviewers can see where the draft rests on incomplete or ambiguous information.
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Draft a target-state profile
AI can help draft a target-state profile based on mission needs, stakeholder expectations, risk, and requirements. The organization—not the model—must decide which outcomes matter and whether the proposed target is appropriate.
SP 1353 is an initial public draft, with a comment period listed through October 15, 2026, at 11:59 p.m. Check NIST’s current publication status before relying on that deadline or treating the draft as final.
Decide what the system may access and do
Before deployment, map the AI system’s data, accounts, connected services, tools, and operating dependencies. This is especially important for agents that can interact with systems or take actions: access that is unnecessary for the assigned task increases exposure without establishing a security benefit.
Use a permission boundary that matches the use case. For example, an assistant preparing a draft CSF profile may need approved documents and interview notes; that task alone does not justify access to production systems or authority to change security settings. This is an operational design recommendation, not a claim that one permission model fits every organization.
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- Identify the information the system can receive, including sensitive or confidential material.
- List the accounts, integrations, and tools it can use, and limit them to the task.
- Separate recommendations and drafts from actions that affect systems or people.
- Specify which consequential actions require approval and how staff can stop or escalate them.
Make human oversight concrete
“Human in the loop” is not a control unless people know what they are responsible for and have a practical way to exercise that responsibility. NIST’s AI RMF Playbook governance guidance recommends defining human roles, distinguishing oversight from use or interaction, and addressing policies, proficiency, training, and risk information about human-AI configurations.
Name the roles
Document who operates the system, who uses its outputs, who oversees its behavior, and who monitors it after deployment. One person may hold more than one role in a small team, but the responsibilities should still be clear.
Set review and escalation rules
Specify which outputs can be used as low-risk drafts, which require independent verification, and which proposed actions require an authorized person’s approval. Define where staff escalate uncertainty, suspected misuse, or a potentially harmful result.
Prepare people for the assignment
Set proficiency expectations and provide training appropriate to each role. A reviewer should understand the task, the system’s permitted use, and how to identify missing evidence or assumptions—not merely know how to operate the interface.
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Validate outputs and preserve reviewable evidence
AI-generated analysis can be useful without being authoritative. Require reviewers to check important claims against source records and to distinguish documented facts from assumptions or gaps. For consequential work, retain enough information to reconstruct what the system was asked to do, what it produced, who reviewed it, and what action followed.
For CSF profile drafting, NIST SP 1353’s examples specifically point to recording assumptions and evidence gaps. Extending that discipline to approvals and actions is a practical way to make later review possible; it should be adapted to the organization’s risks and operating context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor after deployment and plan for response
Putting an AI system into service does not end the security work. NIST’s March 9, 2026 summary of AI 800-4 describes deployed-AI monitoring as important because AI systems have novel properties, variability, and potentially unpredictable behavior. The report maps monitoring categories and challenges using literature and practitioner workshops; it identifies an active practice area, not one proven, mature monitoring standard.
Decide what the organization will monitor and who responds before deployment. Depending on the system and use case, that plan may include checking whether outputs remain suitable, reviewing consequential actions and approvals, identifying misuse or unexpected behavior, and escalating incidents. The monitoring approach should fit the system’s actual access and authority rather than rely on a generic checklist.
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For organizations participating in CISA’s Joint Cyber Defense Collaborative (JCDC), the agency’s January 14, 2025 AI Cybersecurity Collaboration Playbook describes voluntary processes for sharing information about AI system incidents and vulnerabilities, along with protections, sharing mechanisms, and CISA’s actions after receipt. It is a partner-oriented collaboration route, not a mandatory reporting rule.
Compare AI security options by control, not by hype
When assessing a tool or deployment approach, compare the same questions across candidates. These criteria are an evidence-based way to structure an internal assessment, not a published product-scoring standard.
| Assessment area | Question to answer |
|---|---|
| Purpose | Is the system meant to protect an AI application, assist cyber defense, or help address AI-enabled threats? |
| Authority | What may it recommend, prepare, or execute, and which actions require approval from a named human role? |
| Access and exposure | Which data, accounts, systems, and tools can the model or agent reach, and are they necessary for the task? |
| Evidence | Can staff inspect relevant inputs, assumptions, outputs, approvals, and actions? |
| Monitoring and response | How will the organization detect and escalate unsuitable outputs, misuse, incidents, or changing behavior? |
| Operational fit | Does the approach fit existing governance, incident handling, and information-sharing arrangements? |
A practical adoption sequence
- Choose one bounded task. State the intended outcome and what the system is not allowed to decide or do.
- Map access and dependencies. Identify the data, accounts, integrations, tools, and operating process involved; remove access the task does not need.
- Assign accountable roles. Name the operator, users, oversight owner, and monitoring or response responsibility. Set approval and escalation points.
- Test the workflow before relying on it. Have qualified people check outputs against source material, note assumptions and evidence gaps, and confirm that the review process works for the task.
- Record consequential decisions. Preserve enough evidence to understand the input, result, human review, approval, and resulting action.
- Monitor and adjust in operation. Review whether the system remains fit for its use, follow up on issues, and change its access or authority when risks or needs change.
This sequence is a practical synthesis, not a universal recipe or a substitute for a control baseline tailored to the organization, system, and operating context.
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