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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteAI is changing cybersecurity on two fronts: it can help defenders analyze threats and respond, while also expanding capabilities available to attackers and creating new risks inside AI systems. In 2026, a sound cyber strategy has to address both—protecting AI systems and deciding carefully where AI belongs in security operations. The tools may offer speed or scale, but the cited guidance does not establish that they reliably improve security in every organization.
AI changes both defense and attack
AI is not just another tool for a security operations center to adopt. It is also part of the environment that security teams must protect. NIST’s AI Research – Security and Resilience describes this dual-use effect: AI may give defenders new tools, and it may enhance the capabilities of those targeting information technology (IT) and operational technology (OT) systems. The page identifies risks including evasion, model extraction, membership inference, and availability attacks, alongside broader concerns about complex AI system attack surfaces.
That makes two questions inseparable: how an organization can use AI in cyber defense, and how it will secure the AI systems it uses or builds. A strategy that addresses only the first risks leaving data, models, software, hardware, and connected workflows exposed. A strategy that addresses only the second may miss opportunities to apply AI to defensive work.
Why AI systems need cybersecurity controls of their own
AI security overlaps with familiar goals—protecting confidentiality, integrity, and availability—but can involve additional components and attack paths. NIST’s overview points to underlying hardware and software, training and output data, and AI-specific attack surfaces as relevant considerations. These are not abstract concerns: a compromise or manipulation affecting a model, its inputs, or its outputs can affect the system that relies on it.
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- Evasion: an attacker tries to make a system misclassify or miss something by manipulating what it receives.
- Model extraction: an attacker attempts to reproduce or obtain information about a model through access to it.
- Membership inference: an attacker tries to determine whether particular information was included in a model’s training data.
- Availability: an attacker aims to disrupt access to an AI system or its supporting services.
The list is not a claim that every AI deployment faces every attack in the same way. The relevant exposure depends on what the system does, which data and services it can reach, how it is deployed, and who can interact with it. Security teams should map those dependencies rather than treating “AI” as one uniform asset class.
AI agents make adaptation more urgent
AI agents can take actions through connected tools or services, which raises security questions beyond those of a system that only returns a response. NIST’s May 18, 2026 analysis of responses to an AI-agent security request for information (RFI) says commenters widely regarded agent security threats as novel and said fundamental cybersecurity practices may need adaptation. That is a synthesis of stakeholder comments—not evidence that every practitioner agrees, that every agent is unsafe, or that one universal control set has been settled.
For an organization considering agents, the practical issue is the authority each one receives. Before enabling an agent to act, document what it can access, what it can change, which tools it can invoke, and where a person must review or approve consequential actions. Keep permissions limited to the task, and make actions traceable so security staff can investigate unexpected behavior. These are implementation choices for managing exposure, not controls prescribed by a finalized NIST AI profile.
Where AI may help security teams—and what remains unproven
NIST’s December 2025 initial preliminary draft of the Cybersecurity Framework (CSF) Cyber AI Profile, NIST IR 8596, describes AI as a potential aid to human analysts, detection, response, and recovery. It also says organizations should continually evaluate whether a capability is mature enough for their needs. The draft describes possible uses; it does not provide comparative field-test results proving that AI outperforms existing tools or teams.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThat distinction matters when evaluating claims about faster operations or machine-speed defense. A capability that can generate an alert, recommend a response, or execute a response is not automatically accurate, safe, or suitable for a particular environment. Set an explicit role for the system, define when human review is required, and measure whether it performs acceptably against the organization’s own needs before widening its authority.
| Strategic choice | Potential value | Key question for implementation |
|---|---|---|
| Secure AI systems versus use AI to defend other systems | Addressing both can reduce blind spots as AI becomes part of operations. | Which models, data, components, and connected services are in scope, and which defensive tasks are candidates for AI assistance? |
| Human-augmented work versus more automated or agentic workflows | Assistance can support analysis; more autonomy may allow actions to occur with less manual handling. | What decisions can the system make, what actions can it take, and where is review or approval required? |
| Potential speed or scale versus capability maturity and oversight | AI may help teams handle work more quickly or at greater scale. | Has the capability been evaluated for this use case, and can the team detect and recover from errors? |
| Governance guidance versus operational implementation | Shared guidance can help frame risk, but organizations still need to apply it to their own systems. | Who owns the risk decisions, and how will requirements translate into day-to-day controls and response procedures? |
A practical 2026 planning sequence
NIST’s current work emphasizes governance, taxonomy, risk-based guidance, usability, attack surfaces, and AI-enabled defense. Those are active topics, not a finished checklist. Organizations can still use them to structure near-term planning:
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- Inventory AI use. Identify internally developed and externally provided models, AI features in existing services, agents, data sources, and connected tools. Record their purpose, owner, deployment context, and access to sensitive information or systems.
- Map exposure and authority. For each use, trace inputs, outputs, dependencies, permissions, and consequential actions. Prioritize systems with sensitive data, broad access, or the ability to affect IT or OT operations.
- Adapt existing security practices. Apply the organization’s established risk-management and security processes to AI components, while checking whether those processes account for model behavior, training and output data, and agent actions. NIST’s agent-security analysis supports the need to consider adaptation; it does not specify a universal implementation.
- Test the intended use before expanding it. Evaluate the system in the context where it will operate, including how it handles errors and how staff can recognize and respond to them. Start with a bounded role and expand only when the capability is mature enough for that role.
- Assign decision rights. Set who approves deployment, who monitors results, who can change permissions, and who can suspend the system. Define when a human must review an output or action.
- Revisit the assessment as systems change. Models, data, integrations, and permissions can change over time. Review the risk assessment when those changes alter what the system can access or do.
What current guidance does—and does not—settle
NIST’s August 2026 report, NIST IR 8607, summarizes discussion at the January 2026 second Cyber AI Profile workshop. Participants raised governance challenges, profile stability, AI attack surfaces, consistent taxonomy, risk-based guidance, usable resources and use cases, and AI-enabled cyber defense. The report documents issues informing ongoing work; it is not a final control standard.
Other institutional perspectives have different scopes. The Center for Strategic and International Studies (CSIS), in a July 15, 2026 analysis of U.S. cyber defense strategy, argues for machine-speed defensive action; treat that as the authors’ policy thesis, not a measured outcome. The European Union Agency for Cybersecurity (ENISA) lists a frontier-AI cybersecurity view dated July 7, 2026. Together with NIST’s U.S. federal work, these sources show that AI security is receiving attention in multiple policy settings, not that governments have adopted one uniform global approach.
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For security leaders, the useful conclusion is not to automate everything or to avoid AI altogether. It is to make AI a governed part of cyber risk management: protect the systems and data that make it work, limit and monitor the authority it receives, and judge defensive uses by demonstrated suitability for the task. NIST’s profile work may help clarify shared guidance, but organizations should not wait for a final profile to identify their own AI exposure and responsibilities.
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