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AI is making cyberattacks faster to prepare, easier to personalize and more convincing across text, voice, images and video. It is also helping defenders detect threats and analyze incidents. But AI has not removed the familiar conditions attackers need: a usable credential, an unpatched system, a vulnerable internet-facing device, a compromised supplier or a person who can be persuaded to act.
That distinction matters. The practical response is to account for AI-assisted deception and new AI-system risks while continuing to prioritize identity security, patching, access control, verification and recovery.
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How is AI changing cyberattacks?
AI primarily changes the speed, scale, personalization and realism of cyber operations. It can help attackers produce and adapt lures, translate messages, research targets, imitate voices and develop malicious tools. These capabilities can lower the effort needed to make an attack feel specific to its recipient, but they do not by themselves provide access to a victim’s systems.
ENISA’s 2025 Threat Landscape analyzed 4,875 incidents from 1 July 2024 through 30 June 2025. Its reporting describes threat groups using commercial or jailbroken large language models to automate social engineering and accelerate malicious-tool development. ENISA also reports AI-generated lures and targeting of AI supply chains. CERT-EU’s 2026 report on 2025 activity documents voice cloning, personalized phishing and deepfakes, as well as a reported intrusion campaign involving agentic AI. Microsoft’s Digital Defense Report 2024 describes AI-enabled spear phishing, résumé swarming and deepfakes.
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These examples show AI being used as an amplifier for operations, not a replacement for the whole attack chain. An attacker still needs a route to influence a person, access an account or exploit a system.
What changes most in practice?
- Campaign production: AI can help draft and adapt messages more quickly, making it easier to produce many variations.
- Personalization: Language, tone and contextual details can be tailored to a target, making generic warning signs less dependable.
- Impersonation: Generated or manipulated voice, image and video can make a fraudulent request appear to come from a trusted person.
- Operator effort: Some tasks that once required more writing, translation or technical work may require less manual effort. The available evidence does not establish a universal cost reduction or a particular skill threshold.
- Defender workload: More plausible messages and identity fraud can increase the importance of verification, monitoring and fast response.
How is AI-assisted activity different from a conventional attack?
The main difference is often the efficiency and believability of familiar methods, rather than a wholly new way into a network. The underlying attack path—identity abuse, vulnerability exploitation, misconfiguration or supplier compromise—still matters.
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| Dimension | AI-assisted operations | Conventional operations | What remains decisive |
|---|---|---|---|
| Scale and speed | AI can help produce, translate and vary campaign material quickly. | More production and adaptation may be done manually. | Whether the activity reaches a person or system that can grant access. |
| Personalization and language | Messages can be adapted to a target and written in more natural language. | Generic wording, awkward phrasing or repeated templates may be more common, but are not guaranteed. | Whether a request is verified through a trusted channel rather than judged only by its writing. |
| Voice, image and video | Cloning and deepfake techniques can make impersonation more realistic. | Impersonation may rely on ordinary email, calls or reused material. | Independent confirmation of sensitive requests. |
| Technical access | AI may assist reconnaissance or tool development, but it does not make every target vulnerable. | Attackers still use credentials, software flaws, exposed devices and other access routes. | Patch status, identity protections, configuration and least privilege. |
| Defender response | AI can assist detection, triage and incident analysis; outputs need human review. | Analysis may rely more heavily on manual review and established security tools. | Good telemetry, clear escalation procedures and timely decisions. |
The table describes tendencies, not a reliable way to classify an incident. A polished message is not proof of AI use, and an awkward one is not proof that it is safe. Security decisions should turn on the requested action, the account or system involved, and whether the request has been verified.
Are AI phishing emails actually more dangerous?
They can be more convincing and faster to personalize, but the presence of AI does not make a phishing message successful on its own. The risk is that recipients may have fewer obvious clues—such as poor grammar or generic wording—on which to rely.
Phishing is also expanding beyond email. CERT-EU’s reporting describes voice cloning and deepfakes, while the broader threat picture includes OAuth abuse and device-code attacks. A message, call or video can be used to persuade someone to disclose a credential, approve an access request or perform a sensitive action. The same response works across channels: verify the request independently, especially when it involves money, credentials, access or an unexpected change in procedure.
What should staff do with a high-risk request?
- Do not use contact details or links supplied in the suspicious request to verify it.
- Contact the person or organization through a known, separately obtained channel.
- For payment changes, credential resets or access approvals, follow an established approval process rather than relying on voice, video or email alone.
- Report suspected impersonation promptly so security staff can check for related messages or account activity.
These checks are useful even when the message appears to come from a familiar colleague. A believable identity is not the same as a verified instruction.
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AI may assist parts of ransomware operations, such as social engineering, reconnaissance or malicious-tool development, but the evidence here does not show that AI is the cause of a general rise in ransomware or that it independently carries out most attacks.
Microsoft reported a 2.75× year-over-year increase in human-operated ransomware-linked encounters in 2024. That figure describes encounters linked to human-operated ransomware; it is not a measurement of AI-caused incidents. Treating it as proof that AI drove ransomware growth would go beyond what the metric establishes.
For ransomware resilience, the practical priorities remain limiting access, protecting credentials, patching exposed systems, monitoring suspicious activity and maintaining tested recovery. AI-assisted lures may make it more important to train staff to verify unusual requests, but they do not make recovery planning or technical controls less important.
Can AI hack systems by itself?
AI can be involved in parts of cyber operations, and CERT-EU reported an intrusion campaign involving agentic AI. That is evidence of a reported campaign, not evidence that AI autonomously conducts most real-world intrusions or can reliably compromise arbitrary systems without human direction or a viable weakness.
Intrusions still depend on concrete opportunities: a valid credential or token, an exploitable vulnerability, an exposed edge device, a misconfiguration, a persuadable user or a path through a supplier. AI may help an operator find or exploit an opportunity more efficiently, but the opportunity and resulting access are what matter for defense.
This is why security teams should evaluate AI-related claims by asking what the system actually did, what access it had and what human or technical controls were involved. The label “agentic” alone does not tell an organization whether an incident involved autonomous decision-making, scripted actions or human-supervised use.
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Why do vulnerabilities and identity still matter so much?
Because AI does not eliminate the need for an entry point. CERT-EU found vulnerability exploitation was the highest-impact initial-access vector in its significant 2025 incidents. Its 2025 threat-landscape reporting also highlights phishing, credential abuse, cloud-service targeting and edge-device exploitation. The 2025 report notes that edge devices—including firewalls, VPNs and network appliances—remained high-impact entry points and need to be prioritized for patching.
Other figures reinforce the continuing importance of basic exposure management, without proving that AI caused the underlying activity. Google Threat Intelligence Group reported 75 zero-day vulnerabilities exploited in the wild in 2024. CERT-EU’s 2026 report on 2025 activity counted 174 threat actors, compared with 110 in 2024, and said that 38% of its recorded activity was cyberespionage and prepositioning. These figures describe different aspects of the threat environment; they should not be combined into a single measure of AI-driven risk.
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- ENISA’s 4,875 figure is the number of incidents it analyzed for the stated period, not a count of every cyber incident worldwide.
- CERT-EU’s actor and activity figures refer to its 2025 activity as reported in 2026, not all threat actors or incidents globally.
- The zero-day figure concerns vulnerabilities Google Threat Intelligence Group said were exploited in the wild in 2024; it does not say that all were used against a given organization.
- The ransomware-linked encounter figure is a Microsoft measure for 2024 and does not establish an AI cause.
What new cybersecurity risks does AI create?
AI systems and their supporting software introduce another area defenders need to govern. ENISA reports targeting of AI supply chains, while the identified risk types include poisoned models, trojanized packages, malicious configuration instructions and vulnerable AI applications. A compromise in this area can affect the tools, components or information an organization relies on, rather than simply supplying a more convincing phishing message.
Inventory is the first useful step: organizations need to know which AI tools, models, coding assistants and related dependencies are in use, who can access them and what information they can reach. Without that visibility, security teams may not know where to apply access restrictions, logging or supply-chain checks.
Questions to ask when reviewing an AI tool
- What data can users enter, and who can access or retain it?
- Which model, package or external service does the tool depend on, and how are those sources approved?
- Can the tool take actions, call other services or access code and credentials? If so, are those permissions limited to what it needs?
- Are use and administrative activity logged in a way that supports investigation?
- How can the organization disable the tool or revoke its access if a compromise is suspected?
These are governance and security questions, not a claim that every AI tool is unsafe. The right controls depend on the tool’s function, the data it handles and the access it has.
What should companies do about AI-powered scams?
Start with the controls that reduce the chance of account takeover and limit what an attacker can do after access. Add verification rules for high-impact requests and governance for AI systems and dependencies. AI can also assist defenders with detection, triage and incident analysis, but it should support—not replace—review and accountable decisions.
1. Protect high-value identities
Prioritize phishing-resistant multi-factor authentication for administrators and other high-value accounts. Use least privilege so that ordinary accounts and tools cannot reach more systems or data than their role requires. Review credentials, tokens and OAuth grants, and remove access that is no longer needed; an attacker who obtains a token may not need to steal a password in the usual way.
2. Reduce exposed vulnerabilities
Maintain an inventory of internet-facing software and edge devices, including firewalls, VPNs and network appliances. Patch those systems quickly, with particular attention to vulnerabilities known to be exploited and to devices that can provide a route into internal networks. A patch process should include identifying affected assets, applying the fix and confirming that the exposure is gone.
3. Make verification part of the workflow
Require out-of-band confirmation for payment changes, credential requests and access approvals. Define which channel and approver staff should use before a crisis occurs, and make the procedure practical enough that employees can follow it under time pressure. A familiar voice or realistic video should not override the verification step.
4. Govern AI tools and dependencies
Inventory approved AI tools, models and coding assistants. Scan software dependencies and model sources, restrict permissions, and log AI-system use so that unusual activity can be investigated. Establish who can approve new tools and how access is revoked if a tool or component is suspected of compromise.
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5. Prepare to detect and recover
Monitor identity activity, access grants and internet-facing systems, and define how suspicious behavior moves from detection to investigation. Exercise incident recovery, including restoring from backups and coordinating decisions across security, IT and business teams. Microsoft’s Digital Defense Report 2024 notes that AI can improve threat detection, response speed and incident analysis; organizations still need human review and governance over how those capabilities are used.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does AI make the cybersecurity fundamentals obsolete?
No. It changes the pressure on some controls, especially verification and monitoring, and adds security questions around AI tools and their supply chains. It does not make phishing-resistant MFA, rapid patching, least privilege, credential protection, independent verification or tested recovery obsolete.
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In the practical balance, AI gives attackers more ways to scale and disguise familiar methods while giving defenders tools that may help process alerts and analyze incidents. Security depends on whether an organization can prevent easy access, notice suspicious activity and respond before that access becomes a larger compromise.
Frequently Asked Questions
How should a small business start preparing for AI-powered scams?
Assign an owner for handling suspicious requests and make the verification route easy to find, including for finance and account administrators. A short, rehearsed process for confirming payment or access changes is more useful than relying on employees to identify AI-generated content by appearance.
Should employees be allowed to paste company information into AI tools?
Set a clear policy based on data sensitivity and the tool’s approved use, rather than leaving the decision to individual guesswork. Specify what information is prohibited, which tools are authorized, and where employees can ask for an exception.
Can an AI detector reliably tell whether a message or video is fake?
The evidence described here does not establish a detector that can reliably authenticate all text, voice or video. Treat detection tools as signals for investigation, not as a substitute for confirming a high-impact request through a separate trusted channel.
What should an organization do if someone already approved a suspicious access request?
Report it to the security or IT team immediately and preserve the message and relevant details. The team can assess the account, revoke suspicious grants or tokens where appropriate, and check for related activity; the response should follow the organization’s incident procedure.
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