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AI is changing cybersecurity on three fronts: organizations use it to support defense, attackers can use it to create or scale convincing attacks, and AI systems introduce their own security risks. No single security tool covers all three. The practical question is which threat a control is designed to reduce, what access or data it needs, and what risk remains afterward.
How is AI changing cybersecurity?
AI affects security both as a capability and as a target. It can help people analyze information and support defensive work; it can also help produce persuasive phishing or impersonation attempts. Meanwhile, AI applications and agents can be attacked through familiar software weaknesses as well as risks tied to models, data, and instructions.
NIST’s initial preliminary draft of the Cybersecurity Framework Profile for Artificial Intelligence organizes the work into three focus areas: securing AI systems, using AI in cyber defense, and thwarting AI-enabled attacks. This is a preliminary draft, not a final standard. NIST’s December 16, 2025 announcement describes the framing as reflecting the different ways AI is entering organizations.
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What cyber risks does AI create?
AI can change the content, speed, or scale of some attacks, but it does not erase the basics of cybersecurity. Risks span social engineering, attacks on model inputs or data, excessive agent permissions, and conventional software flaws.
Phishing and synthetic impersonation
AI can help attackers produce realistic communications and manipulated audio or video. That can make an impersonation attempt more convincing, but it does not mean every phishing message is AI-generated or that every synthetic message succeeds. NIST’s preliminary profile recommends updated personnel training and integrated, automated defenses, including email and authentication security.
Prompt injection and unsafe agent actions
An AI agent may read untrusted content and then take actions through connected tools or applications. Indirect prompt injection attempts to influence the agent through content it encounters, rather than only through the user’s direct prompt. A harmful action can also result from an agent’s design, context, or permissions without a malicious input.
NIST identifies these concerns alongside other agent-system risks in its January 2026 request for information about securing AI agent systems. Because agents can reach data and services, access boundaries and accountability matter as much as the model’s ability to interpret instructions.
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Poisoned data and compromised components
AI systems depend on models, data, code, and other components that may be changed or compromised. Data poisoning can affect what a system learns or how it behaves. Security therefore includes protecting the integrity and provenance of relevant data and components, not just monitoring the machine that runs the AI.
Ordinary software vulnerabilities still apply
AI products can have familiar weaknesses, including authentication problems and memory-management vulnerabilities. An AI-specific safety layer does not replace secure software development, vulnerability management, or access control. The NIST overview of cybersecurity, privacy, and AI discusses the overlap between AI concerns and established security disciplines.
Can AI help defend against cyberattacks?
AI can augment analysts and may support detection, response, and recovery, but it should be treated as a capability to evaluate—not as a guaranteed replacement for security staff. NIST’s preliminary profile notes potential defensive uses while emphasizing that organizations need to assess whether the capabilities are mature enough for their needs.
For a security team, evaluation should focus on the work the system is expected to do and the consequences of error. For example, an assistant that summarizes an alert for an analyst has a different risk profile from an agent allowed to disable accounts or change production systems. Human review, escalation paths, and bounded permissions should reflect that difference.
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The cited NIST and CISA material does not provide comparative testing of commercial products or a universal performance guarantee. A product’s use of AI is not, by itself, evidence that it detects more attacks or reduces response time.
Which security tools address which AI-related risks?
Security controls work in specific areas. The table matches the risk to relevant control categories and shows what those controls do not establish.
| Risk or use | Relevant controls | What they can address | Boundary |
|---|---|---|---|
| AI-assisted phishing or synthetic impersonation | Email security, authentication, staff training, incident response | Reduce exposure to malicious messages, strengthen account access, and help people recognize or report suspicious requests. | They do not establish that every synthetic message will be identified or every social-engineering attempt stopped. NIST recommends updated training and integrated defenses in its preliminary profile. |
| Agent prompt injection or harmful tool use | Agent identity, authorization, least privilege, auditing, and controls for untrusted inputs | Limit what an agent can access or change, clarify which agent is acting, and create records for review. | NIST’s identity and authority concept paper discusses these ideas; it is not a completed product certification or proof that a particular implementation works. |
| Poisoned data or compromised AI components | Secure development, provenance and integrity controls, risk governance | Help protect code, training data, and model weights and surface risks in the development or deployment process. | A general endpoint security tool alone should not be treated as protection for the model or data supply chain. NIST addresses generative AI risks in its Generative AI Profile. |
| Vulnerabilities in AI applications | Secure software practices, vulnerability management, access control | Reduce exposure to exploitable application and infrastructure weaknesses, including weaknesses also found in non-AI software. | AI-specific safeguards do not replace routine software security practices. |
| AI used in security operations | Detection, response, recovery, analyst workflow tools | Support analysts’ work and potentially improve parts of defensive operations when the capability is suitable and evaluated. | The cited sources do not establish a universal performance benefit or compare named commercial products. |
| Potentially faster or more scalable attacker activity | Resilient systems, monitoring, response readiness, information sharing | Help teams prepare to identify and coordinate responses to incidents and vulnerabilities. | NIST describes speed and scale as planning concerns, not proof that every attack is more successful. CISA’s JCDC AI Cybersecurity Collaboration Playbook supports voluntary information sharing; it is not a technical product recommendation. |
When comparing options, assess the threat each one addresses, the data and permissions it requires, where it will be deployed, how effectiveness is evidenced, and what integration and operational work it adds. Also name the residual risk: a control that improves account authentication, for example, does not address poisoned training data or unsafe agent actions.
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How should an organization secure AI agents?
Start by treating an agent as software with an identity, permissions, inputs, and an audit trail—not simply as a chatbot. A sensible security review follows the agent’s path from the information it reads to the actions it can take.
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- Inventory the agent and its connections. Record its purpose, owner, data sources, tools, applications, and the environments in which it operates. Include agents built into third-party services where the organization enables them.
- Define identity and authorization. Establish how the agent is identified and which user or service authority it acts under. Grant only the permissions needed for its task; avoid broad access to data or functions merely because the integration makes it convenient.
- Limit consequential actions. Separate read access from write access where possible. Require a human or a separate approval step for actions with significant business or security consequences, and decide in advance how the agent can be stopped or access revoked.
- Account for untrusted content. Identify whether the agent can encounter email, web pages, documents, or other input that may contain adversarial instructions. Apply safeguards to those inputs and test what the agent does when they conflict with the intended task.
- Log and review activity. Keep records that let the organization determine which agent acted, what it accessed, and what action it took. Use those records in monitoring and incident response, while managing sensitive data in logs appropriately.
- Apply normal software security. Include the agent and its integrations in secure development, vulnerability handling, authentication review, and change management. Assess the integrity of models, data, and code relevant to the deployment.
NIST’s February 2026 paper on software-agent identity and authority is a concept effort, not a finished certification scheme. NIST’s August 2025 control-overlays concept paper likewise describes proposed overlays for generative and predictive AI, single- and multi-agent systems, and developer use cases; it should not be represented as a finalized control set.
Which guidance can organizations use?
NIST and CISA guidance can help structure risk management and coordination, but the documents have different purposes and statuses. Use each for what it actually provides.
- NIST AI Risk Management Framework: NIST describes the AI RMF as voluntary. AI RMF 1.0 was released January 26, 2023, and NIST says the framework is being revised. The Generative AI Profile, NIST AI 600-1, was released July 26, 2024. NIST also notes an April 2026 concept note for a critical-infrastructure profile.
- Cyber AI Profile: NIST’s initial preliminary draft groups activity under “Secure,” “Defend,” and “Thwart.” Workshop reports in 2026 continue to discuss the profile and related controls; it remains work in progress rather than a final standard.
- AI-agent security responses: NIST’s May 18, 2026 summary of responses to its request for information reports broad agreement among commenters that agent security presents novel threats and that fundamental practices need adaptation. It summarizes comments; it is not a quantified survey of attack prevalence or product effectiveness.
- Incident and vulnerability collaboration: CISA’s January 14, 2025 playbook describes voluntary information-sharing processes for AI-related incidents and vulnerabilities. It can inform preparation and coordination, not replace technical controls.
How can a team decide whether an AI security tool is worth deploying?
Evaluate a tool against a concrete security task and a defined operating environment, rather than buying it because it is described as “AI-powered.” The assessment should distinguish claimed capability from evidence and account for the new access the tool itself may receive.
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- Specify the outcome: Identify the threat or workflow the tool is meant to address and what a successful result looks like.
- Check its inputs and privileges: Determine which messages, logs, files, accounts, models, and systems it can inspect or modify. Consider how sensitive information is handled.
- Test realistic failure cases: Include false alerts, missed threats, ambiguous instructions, untrusted inputs, and unavailable integrations. Decide who reviews high-impact outputs.
- Plan operations: Account for integration, tuning, monitoring, incident handling, access reviews, and the process for disabling or replacing the tool.
- Document what remains uncovered: A tool may reduce a specific exposure while leaving other risks untouched. Keep those gaps visible in the organization’s risk and response planning.
As of October 2026, the cited official materials offer risk frameworks, draft guidance, concept papers, and collaboration processes—not a product ranking or a single control that neutralizes AI-related cyber risk. NIST’s May 2026 agent report summarizes submitted comments but provides no numeric estimate of attack prevalence or comparative tool efficacy.
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Frequently Asked Questions
Does a FIDO2 security key protect against AI deepfakes?
No. A FIDO2 key can strengthen authentication for compatible accounts and devices, but it does not verify whether a voice, video, or message is genuine. Organizations still need procedures for verifying high-risk requests through a separate trusted channel.
Is NIST’s Cyber AI Profile a mandatory cybersecurity standard?
No. The cited Cyber AI Profile is a preliminary draft, and the AI Risk Management Framework is described by NIST as voluntary. Organizations should check the latest NIST publication status and any separate regulatory or contractual obligations that apply to them.
What should I do if an AI agent takes an unauthorized action?
Use the organization’s incident-response process: contain the agent by revoking or narrowing its access, preserve relevant activity records, and assess what data or systems it reached. Notify the appropriate security and system owners, then address the cause before restoring the agent.
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The cited official materials do not establish that. They identify potential changes in attacker speed and scale and discuss realistic impersonation, but do not provide a universal success-rate estimate.
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