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AI is changing cybersecurity, but the evidence does not show that it is making security worse overall. It is a dual-use technology: attackers can use it to scale phishing and social engineering, AI systems themselves have security risks, and defenders may use AI to strengthen their work. The practical response is to secure AI deployments, keep testing and human review in the loop, and verify suspicious requests through trusted channels.
How is AI changing cybersecurity?
AI affects cybersecurity in three connected ways: it can help people carry out attacks, it creates new systems and data that need protection, and it may augment defensive capabilities. These effects can occur at the same time. Evidence that malicious groups use AI, for example, does not by itself show that AI has caused a net increase in cybercrime.
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The sources discussed here have different scopes. ENISA reports on threats and events in the European Union, while NIST publishes U.S.-context cybersecurity and risk-management guidance. Neither source calculates AI’s overall net effect on cybersecurity.
How can attackers use AI?
Phishing and social engineering
ENISA says large language models are being used to improve phishing and automate social-engineering activity. Such tools can help attackers produce or adapt messages at scale. ENISA’s 2025 threat-landscape press release also reported that, by early 2025, AI-supported phishing campaigns represented more than 80% of observed social-engineering activity worldwide. Treat that as a figure reported by ENISA, not as an independently verified global census: the release does not provide a primary measurement study for the claim.
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Synthetic audio, video, and text
ENISA’s 2026 threat-landscape release describes AI-generated audio, video, and text being used in information manipulation, and says AI can enable mass distribution and translation. These capabilities can make impersonation attempts more convincing or help them reach more people. They do not mean that every suspicious message, call, or video was made with AI.
For a consequential request—such as changing payment details or sharing sensitive information—verify it through a separate, trusted channel or an established approval process. A polished message or convincing voice is not proof of identity.
What security risks do AI systems introduce?
AI deployments rely on software, hardware, data, and connections to other services. NIST identifies confidentiality, integrity, and availability risks involving AI systems, their training data, and their outputs, as well as security concerns in the underlying software and hardware. The specific exposure depends on the system and how it is used; it is not accurate to assume every AI system has the same weaknesses.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteNIST also notes that existing cybersecurity frameworks and guidance do not comprehensively address some AI-related attacks, including evasion, model extraction, membership inference, and availability attacks. Its adversarial machine-learning taxonomy helps distinguish several kinds of threat:
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| Attack class | Plain-language meaning | Where NIST’s taxonomy applies |
|---|---|---|
| Evasion | An attacker manipulates inputs to change a model’s output. | Predictive and generative AI |
| Poisoning | An attacker corrupts data used in training or learning. | Predictive and generative AI |
| Privacy attack | An attacker attempts to infer or expose information. | Predictive and generative AI |
| Misuse | A generative system is used for harmful purposes. | Generative AI |
These are distinct categories, not a claim that every model is susceptible to every attack. NIST’s Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (AI 100-2e2025), published on 24 March 2025, discusses attack types and mitigation limits. It is voluntary guidance, not a guarantee that a system is secure; NIST says it plans to update the report annually as the field develops.
Can AI also help defenders?
Yes. NIST says AI may augment defensive cybersecurity capabilities. That is a potential use, not a promise that an AI detection tool will reliably improve security or replace expert judgment. Any security measure should be assessed against a defined threat and intended use, with attention to its limitations and the need for human review.
When comparing defensive approaches, first clarify whether the goal is to protect an AI system or use AI to defend other systems. Then identify which components and data are covered, which attack classes the measure addresses, what kind of evidence supports it—guidance, evaluation, or operational results—and what risks remain. The cited sources do not provide a product comparison or head-to-head performance test.
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- Inventory AI use. Record where AI tools and AI-enabled services are used, along with relevant data, components, and dependencies. Include services integrated into business workflows.
- Secure the surrounding systems. Apply established cybersecurity and secure-development practices to the software and infrastructure around AI, then analyze the AI system’s data, model, and use case for additional risks.
- Match safeguards to threats. Name the risk a safeguard is meant to address—such as evasion, poisoning, privacy attacks, misuse, or another defined threat—and check whether the available evidence demonstrates effectiveness for that use.
- Verify high-impact requests independently. Use established approval and contact procedures for requests involving money, access, or sensitive information, including when a message or voice sounds familiar.
- Keep measurements in context. Distinguish documented AI use from statistics about cybersecurity in general; attribute an incident or vulnerability to AI only when the source makes that connection.
What do recent threat figures show—and what do they not show?
ENISA’s 2025 Threat Landscape analyzed 4,875 incidents observed from 1 July 2024 through 30 June 2025. ENISA’s publication page records a version 1.3 revision dated 22 September 2026 that corrected figures and links; the incident count should be understood within that publication and observation period.
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ENISA’s 2026 release covers events observed from 1 January through 31 December 2025. Its collection included open-source reporting and anonymized information shared by EU Member States and through ENISA’s Cyber Partnership Programme. It describes AI use in information manipulation and business integration, but those observations do not quantify AI’s total causal contribution to cybercrime.
That release also reports more than 48,000 newly assigned CVE identifiers in 2025, a 22% increase over the previous year. This is a general vulnerability count—not a count of AI-specific vulnerabilities, AI-driven attacks, or vulnerabilities caused by AI.
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