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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Hackers can use generative AI and machine learning to make familiar attacks—such as phishing, impersonation and malware development—faster, more convincing or easier to tailor. That is different from attacking an AI system itself, for example by manipulating its inputs or training data. For people managing money online, the practical response is to verify unusual requests through a separate channel and protect accounts with strong authentication, while recognizing that current official sources do not establish a global statistic showing AI-enabled cyberattacks are rising overall.
How AI can make familiar cyberattacks more effective
In an AI-assisted attack, the target is still a person or an ordinary computer system; AI helps the attacker prepare or carry out part of the attack. Generative AI can produce text, images, audio or video, while machine-learning techniques can support other parts of an attacker’s workflow. The technology does not make an attempt automatically successful, but it can reduce the effort needed to create convincing material or adapt it to a target.
Phishing and social engineering
A scam message does not have to be generic or riddled with mistakes. Generative AI can help create fluent, personalized messages, including messages adapted to a recipient’s context. Europol’s 2025 Internet Organised Crime Threat Assessment announcement reports that criminals are using generative AI, including large language models, to tailor scam messages to victims’ cultural context and personal details. That is a reported trend in Europol’s assessment, not a measure of how common such messages are across all scams.
For someone handling personal finances, the danger is a message that appears to come from a bank, payment service, employer or family member and urges a transfer, a password reset or the disclosure of a security code. AI may help make the wording more plausible; it does not prove that the sender is genuine. Treat an unexpected request to move money or reveal a code as unverified, even if it sounds personal or urgent.
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Impersonation with synthetic media
Generative tools can produce lifelike audio, realistic images and deepfakes that may support impersonation or influence efforts. CISA’s guidance, “Risk in Focus: Generative AI and Elections,” dated January 18, 2024, discusses these potential malicious uses in an election context. It warns about possible uses; it does not establish that every technique is widespread or successful. In a financial scam, a convincing voice or video should be treated as a claim to verify, not as proof of identity.
Malware assistance
CISA also says malicious actors may use generative AI to help create malware strains that evade defenses. This is potential-use guidance, not evidence that AI-created malware is routinely successful or that AI is required to write malware. The broader point is that AI may assist an existing attack workflow; it does not turn every malware incident into a new kind of attack.
What “rising” does—and does not—mean
Officials have reported increased use of generative AI in particular contexts. Europol’s 2025 assessment announcement describes AI-enhanced social engineering, and CISA describes ways malicious actors may use generative tools. These sources support increased attention to the capability and reported use, but they do not provide a comparable global time series showing that AI-enabled cyberattacks as a whole are rising. Nor does a warning that a technique is possible establish its prevalence or success rate.
CISA emphasizes that phishing and social engineering are not new: generative AI may make them faster, more sophisticated or less costly. That distinction matters. The underlying risk is often a familiar attempt to trick a person or compromise a conventional system, with AI changing the attacker’s speed, scale or presentation.
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These are separate security problems. An attacker can use AI as a tool against a person or ordinary system, or target an AI system’s data, inputs, model or deployment. NIST’s March 2025 publication, AI 100-2 E2025, provides a taxonomy of concepts and terminology for adversarial machine learning. It covers both predictive and generative AI and organizes threats by system type, lifecycle stage, attacker objective, capability and knowledge.
| Threat category | What is targeted | General idea |
|---|---|---|
| AI-assisted conventional attack | A person or conventional system | AI helps with research, tailored messages, impersonation or parts of malware development. |
| Evasion | An AI model during use | An attacker crafts or changes inputs to influence the model’s output or avoid its detection. |
| Poisoning | Data used to build or adapt a model | Manipulated data can affect model behavior. |
| Privacy attack | Information associated with an AI system | An attacker seeks to infer or expose information through or about the system. |
| Misuse attack | An AI system’s capabilities or deployment | An attacker exploits a system’s capabilities or access in a way that causes harm. |
The last four labels are categories in NIST’s taxonomy, not a claim that every incident fits neatly into one box. The relevant question is what the attacker is trying to affect: a person, a conventional system, or an AI system at a particular point in its lifecycle. NIST’s 2025 taxonomy is the current reference here; its January 2024 AI 100-2 E2023 report is an earlier baseline.
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Because AI can make impersonation more convincing, defenses should rely on verification and layered account security rather than trying to spot one telltale writing style. CISA’s guidance and NIST’s December 2025 Cybersecurity Framework Profile for Artificial Intelligence support awareness and integrated defenses; NIST also highlights risks such as realistic spear-phishing messages, audio or video manipulation, and hyper-realistic malicious websites and links.
- Verify financial requests independently. If a message or call asks you to transfer money, share a one-time code, or change account details, contact the person or institution using a number or app you already trust—not contact information supplied in the request.
- Protect sign-in and recovery. Use unique passwords and multifactor authentication where available. Never disclose a one-time verification code to someone who contacts you. Secure the email account used for financial-account recovery as carefully as the financial account itself.
- Pause before following links. Open your bank or payment provider’s official app or type its known address yourself instead of using a link in an unexpected message. A polished message or realistic website is not proof of legitimacy.
- Use a second check for unusual payment instructions. Confirm changes to payment details or urgent transfer requests through a separate, previously established channel, especially when the request relies on urgency or secrecy.
- For organizations deploying AI, assess the system itself. Use NIST’s lifecycle and attack categories to consider what data, model inputs, outputs and surrounding services need protection. Controls should match the system and threat; no single measure addresses evasion, poisoning, privacy and misuse alike.
For staff-facing risks, NIST’s 2025 AI cybersecurity profile calls for updated personnel training and integrated defenses. Training is most useful when paired with practical ways to verify requests and technical protections for email, identity and account access; it cannot establish whether a particular message is authentic on its own.
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What to do if a request or account activity looks suspicious
- Do not reply, click, transfer money or share a code. Preserve the message or call details if you may need to report them.
- Contact the financial institution through a trusted route. Use the number on your card or statement, or the institution’s official app or website. Ask whether the request or transaction is genuine.
- If you disclosed credentials, act from a trusted device. Change the affected password, review recent account activity, and use the institution’s official support channel to secure the account.
- Report an unauthorized transaction promptly. Contact the provider using its official dispute or fraud-reporting process and follow its instructions. If the same password was reused elsewhere, change it on those accounts too.
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