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AI is changing cybersecurity on three fronts: organizations are using it to assist defenders, attackers can use it to create or scale some attacks, and AI systems themselves introduce risks that need to be managed. Security tools help when matched to a particular threat—for example, authentication controls can reduce account-access risk, while agent authorization can limit what an AI agent is allowed to do. No source cited here establishes that one tool can neutralize AI-related cyber risk broadly.
How is AI changing cybersecurity?
AI is affecting both the work of cybersecurity teams and the systems they must protect. NIST’s preliminary Cybersecurity Framework Profile for Artificial Intelligence organizes the issue into three areas: securing AI systems, using AI to support cyber defense, and countering attacks enabled by AI. NIST’s December 2025 announcement describes those focus areas as reflecting the different ways AI is entering organizations. The profile remains a preliminary draft, not a final standard.
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This framing helps avoid a common mistake: treating “AI security” as a single product category. A phishing-resistant login control, a monitoring tool, an AI governance process and a system that restricts an agent’s access address different parts of the problem. One does not automatically replace the others.
NIST’s initial preliminary draft of NIST IR 8596 discusses AI-enabled defense and attacks as emerging capabilities and concerns. It does not establish that every attacker is using AI, that AI makes every attack more successful, or that AI-powered defense tools reliably outperform other approaches.
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Secure: protect the AI system
AI products can have weaknesses in their models, data, software, configuration and connections to other systems. An AI agent—a software system that can take actions using tools or applications—may also cause harm through an allowed but poorly controlled action, even if no attacker has tricked it.
Defend: use AI to support security work
AI may assist analysts and support detection, response and recovery. NIST’s draft presents these as potential benefits, while cautioning that organizations need to assess whether a capability is mature enough for their needs. That is not a basis for assuming AI can replace security staff or always improve outcomes.
Thwart: prepare for AI-enabled attacks
AI can help produce realistic messages or manipulate audio and video for impersonation. It may also affect the speed or scale of attack activity. Those are reasons to plan for resilient systems, monitoring and timely response—not evidence that every attack has become more effective or that one new tool will stop them all.
What cyber risks does AI create?
AI-related risk includes both familiar cybersecurity problems and attacks that exploit features of AI systems. The controls must match the weakness: conventional software security still matters, while systems that act on prompts or external data need additional attention to their inputs, permissions and behavior.
Phishing and synthetic impersonation
AI can assist with more realistic phishing communications and manipulated audio or video. A convincing message or voice can make a fraudulent request seem familiar, but the existence of synthetic media does not prove that every unusual call or message is fake. NIST’s preliminary profile points to updated personnel training and integrated defenses, including email and authentication security, as parts of a response.
Indirect prompt injection and harmful agent actions
An agent may encounter untrusted instructions embedded in data it reads, sometimes called indirect prompt injection. If the agent has access to tools, applications or sensitive information, a manipulated input or an unsafe action can have consequences beyond the text of its answer. NIST identifies these risks in its request for information about securing AI agent systems.
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Data poisoning and compromised AI components
Data poisoning and insecure models are distinct concerns for agent systems. NIST’s generative AI profile also treats the security of AI code, training data and model weights as part of information security. Protecting an endpoint alone does not establish that a model’s components or training data are trustworthy.
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Ordinary software weaknesses
AI-enabled products remain software. NIST notes that agent systems may have exploitable vulnerabilities such as authentication or memory-management problems. AI-specific safeguards therefore do not replace vulnerability management, secure development or access control.
Which security tools address which risks?
Evaluate a tool by the specific threat it is meant to reduce, what data or privileges it needs, and what remains exposed after deployment. The sources cited here support control categories, not rankings or efficacy claims for named commercial products.
| Risk or use | Relevant controls | What the controls can address | What they do not establish |
|---|---|---|---|
| AI-assisted phishing or synthetic impersonation | Email security, authentication security, staff training and incident response | These measures can reduce exposure, make account access harder to abuse and help people recognize or respond to suspicious requests. | They do not guarantee detection of every synthetic message, video or voice, or prevent every social-engineering attempt. |
| Agent prompt injection or unsafe tool use | Agent identity, authorization, least privilege, auditing and controls for adversarial data | These measures can help identify an agent, constrain its permitted actions and make its activity reviewable. | A concept paper does not prove a particular implementation works; normal software controls are still needed. |
| Poisoned data or compromised AI components | Secure development, provenance and integrity controls, and risk governance | These practices address the trustworthiness and protection of code, training data and model components. | A general endpoint or antivirus tool alone does not secure a model supply chain or training dataset. |
| Conventional vulnerabilities in an AI product | Secure software practices, vulnerability management and access control | These controls address weaknesses such as faulty authentication or memory handling. | Calling a product “AI-secure” does not show that its underlying software is free of exploitable weaknesses. |
| AI used to assist security operations | Detection, response, recovery and analyst workflow tools | AI may augment analysts and support security work, subject to evaluation for the organization’s needs. | The cited official material supplies no independent product comparisons or universal performance guarantee. |
| Potentially faster or larger-scale attacks | Resilient systems, monitoring, response readiness and information-sharing processes | These measures support preparation and coordination when incidents or vulnerabilities emerge. | They do not prove that AI increases the success rate of every attack or provide a universal countermeasure. |
This is a control-matching framework, not a certification checklist. NIST’s AI Risk Management Framework is voluntary; NIST says AI RMF 1.0 was released on January 26, 2023, and is being revised. Its AI Risk Management Framework page also identifies the Generative AI Profile, NIST AI 600-1, as released on July 26, 2024. The profile is available at NIST’s Generative AI Profile PDF.
How should an organization secure an AI agent?
Start by treating an agent as software with an identity and permissions, not as a harmless chat interface. Map what it can access and do, limit authority to the task, and make its actions reviewable. These steps reduce avoidable exposure but do not prove that an agent is immune to prompt injection or other failures.
- Inventory the agent and its connections. Record which model or service it uses, what applications and data it can reach, and which tools can trigger actions. Include indirect access through other systems where relevant.
- Assign a distinct identity. Make it possible to distinguish an agent’s activity from a person’s or another service’s activity. NIST’s concept paper on agent identity and authority discusses identification, authorization and auditing as related design concerns.
- Limit authorization to the task. Give the agent only the access and action authority it needs. Avoid broad permissions that would let a compromised or misdirected agent reach unrelated data or systems.
- Plan for untrusted inputs. Identify what external content the agent reads and how it may affect its behavior. Consider indirect prompt injection and other adversarial data, rather than assuming that instructions embedded in retrieved material are trustworthy.
- Audit actions and review outcomes. Keep enough information to understand what the agent accessed and did, and define how people can intervene when an action is unexpected. Auditing can support investigation; it does not itself prevent a harmful action.
- Apply ordinary software security. Include the agent and its integrations in secure development, vulnerability management and access-control practices. NIST’s February 2026 concept paper on identity and authority of software agents discusses these ideas, but it is a concept effort, not a completed product certification.
NIST’s May 2026 summary analysis of responses to its AI-agent security request for information reports broad agreement among commenters that agent security presents novel threats and that fundamental cybersecurity practices need adaptation. This is a summary of submitted responses, not a quantified survey of attack prevalence or proof that any particular control is effective.
How should teams respond to phishing and AI-assisted impersonation?
Use overlapping defenses rather than relying on a person to spot every synthetic message or on a filter to catch every suspicious communication. NIST’s preliminary profile specifically points to updated training and integrated defenses that include email and authentication security.
- Refresh training around realistic requests. Make sure employees know how to verify unexpected requests for sensitive information or action through a separate, trusted channel. Training is a risk-reduction measure, not a guarantee that people will identify every deception.
- Use email and authentication protections together. Email controls address one route by which fraudulent requests arrive; authentication security addresses account access. One should not be treated as a substitute for the other.
- Prepare an incident path. Make it clear how a suspected impersonation or compromised account is reported and handled. Fast reporting and response matter even when the origin or authenticity of a message is uncertain.
For account protection, a security key using FIDO2 is one possible authentication component where the account and device support it. It can help with the authentication part of the risk, but it does not identify deepfakes, prevent prompt injection or provide comprehensive AI protection.
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What guidance is available, and what is still in draft?
NIST and CISA materials provide frameworks, concept work and collaboration guidance, but their status matters. A preliminary profile or proposed control overlay should not be described as a finalized standard or a product endorsement.
- NIST Cyber AI Profile: The initial preliminary draft of NIST IR 8596, published in December 2025, uses the focus areas “Secure,” “Defend” and “Thwart.” NIST’s August 2026 IR 8607 workshop summary report continues discussion of the profile and related control work. The material is work in progress, not a final standard.
- AI Risk Management Framework: NIST describes the AI RMF as voluntary and says version 1.0 was released January 26, 2023 and is being revised. NIST also identifies an April 2026 concept note for a critical-infrastructure profile on its framework page.
- AI control overlays: NIST’s August 2025 control-overlays announcement describes a concept paper and proposed action plan for overlays that cover generative and predictive AI, single- and multi-agent systems, and developer use cases. It is not a finalized control set.
- Incident and vulnerability sharing: CISA’s January 2025 JCDC AI Cybersecurity Collaboration Playbook provides voluntary information-sharing processes for incidents and vulnerabilities associated with AI systems. It supports collaboration and preparation, not a technical product recommendation.
When comparing any security options, ask what specific risk each addresses, what information and permissions it needs, where it is deployed, what evidence supports its effectiveness, what operational integrations it requires and what residual risks remain. The cited sources do not compare named commercial products, and they provide no directly attributable overall statistic for AI’s effect on cyber risk or for comparative tool effectiveness. A percentage claiming otherwise would need its own relevant, attributable evidence.
How can a team choose controls without overclaiming?
Use a threat-to-control map and record both the intended benefit and the boundary. This makes it easier to avoid buying or deploying a tool on the assumption that an AI label means broad protection.
- Name the asset and harm. Specify whether the concern is account takeover, fraudulent requests, agent access to sensitive data, model-component integrity, software vulnerabilities or another concrete risk.
- Choose a control for that mechanism. Authentication addresses account access; email protection and training address phishing exposure; authorization constrains agent actions; development and integrity practices address software and component risks.
- Check dependencies and operational fit. Identify what systems, data and privileges the control needs, and who will operate, monitor and maintain it.
- Define how you will evaluate it. Set a relevant test or review method for the deployment instead of assuming results from a different context transfer. NIST’s draft explicitly calls for organizations to assess whether AI-enabled capabilities are mature enough for their needs.
- Document what remains uncovered. Record residual risks and the response path for failures. This keeps a narrow control from being presented as comprehensive protection.
The practical takeaway is to secure AI systems as software and data assets, govern the authority of agents, and apply established controls to phishing, accounts and incident response. AI can assist defenders and attackers, but which control helps depends on the threat being addressed.
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Is NIST’s Cyber AI Profile mandatory?
The cited profile is a preliminary draft, not a final standard or a certification requirement. NIST separately describes its AI Risk Management Framework as voluntary; organizations should confirm the applicable obligations for their own sector and jurisdiction.
Can an organization say its AI system is secure because it follows the AI RMF?
Following a risk framework can structure decisions, but it is not proof that a particular system has no vulnerabilities or that all risks have been eliminated. Any public security claim should be specific about the system, controls and evidence behind it.
Does AI-generated phishing mean email filters are obsolete?
No. NIST’s preliminary profile includes email security as part of integrated defenses, alongside authentication security and updated training. The sources do not claim filters identify every synthetic or deceptive message.
Does NIST publish a statistic showing how often AI agents are attacked?
The cited NIST summary of responses about agent security does not provide a directly usable numerical estimate of attack prevalence. It summarizes comments and should not be presented as a quantified prevalence survey.
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