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Generative AI makes some cybercrime faster, cheaper, more scalable and more convincing. It can draft tailored phishing messages, translate them, create fake profiles and images, imitate voices and automate parts of social engineering. It does not create the underlying crimes: deception, impersonation, credential theft, fraud and the sale of stolen access all predate generative AI. The practical lesson is to treat AI as an amplifier inside an established criminal economy, not as a new explanation for every incident.
What changes when criminals use generative AI?
The FBI’s Internet Crime Complaint Center (IC3) described the operational advantage in a public warning dated December 3, 2024. Bryan Vorndran, then Assistant Director of the FBI’s Cyber Division, said: “The FBI is warning the public that criminals exploit generative artificial intelligence (AI) to commit fraud on a larger scale which increases the believability of their schemes.” That sentence captures two distinct effects: criminals can produce more material, and the material may look or sound more credible.
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AI can reduce the time and effort needed for work that previously required fluent writing, translation, image editing or repeated manual conversations. It can also help an operator tailor a message to a person, organization or cultural context. Those advantages matter because many scams succeed through volume and persistence rather than a single technical breakthrough.
| Dimension | AI-assisted operation | Conventional operation |
|---|---|---|
| Speed and effort | Text, translations, images and profile material can be produced quickly with less manual work. | Attackers create and adapt material manually or with older automation. |
| Scale | One operator can generate more messages, profiles or variations for a larger target set. | Output is constrained by the people and tools available to the criminal group. |
| Personalization | Messages can be adapted to a victim’s personal details and cultural context. | Personalization is possible but usually takes more research and labor. |
| Realism | Natural-sounding language, convincing images, voice imitation or deepfakes can reduce obvious warning signs. | Errors, stock images or a mismatch between claimed identity and communication may be easier to notice. |
| Goal and tactic | Still commonly involves persuading a target to disclose credentials, send money, reveal information or grant access. | The same objectives and many of the same social-engineering methods apply. |
The first four rows describe changes in production and presentation. The last row is the continuity that matters most: AI changes how an operation is delivered, not what the criminal wants.
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How criminals are using generative AI
Text for social engineering and spear phishing
According to the FBI warning, generated text is being used in social engineering, spear phishing and financial fraud. A criminal can ask a model for multiple versions of a message, adjust its tone for a particular job or relationship, and keep a conversation going after the first reply. This does not make the request legitimate; it can simply remove the awkward wording that once exposed a scam.
Europol’s June 11, 2025 announcement of its Internet Organised Crime Threat Assessment 2025 adds that large language models can tailor social-engineering messages to a victim’s cultural context and personal details. A message that references the recipient’s role, location or current concern may feel more plausible even when the sender is an impostor.
Translation and localization
The FBI lists translation as another use. Better translation can reduce spelling and grammar clues that previously alerted a recipient. It also lets a campaign address people in more languages without hiring a fluent speaker for every target group. A polished message remains untrusted if it asks for an unusual payment, password or urgent action.
Fake profiles and generated images
IC3 reports mass-created fictitious profiles and generated images used for profile photos, identification, impersonation and fraud. A profile can therefore have a plausible face, biography and conversation history without belonging to a real person. The existence of a photograph or a populated social account is not independent proof of identity.
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Fraudulent websites and chatbots
The FBI also warns about fraudulent website content and chatbots. These systems can answer routine questions, imitate customer support and keep a prospective victim engaged. A familiar-looking page or responsive chat window still needs to be checked through a trusted route rather than the link or contact details supplied in the message.
Voice imitation, deepfakes and possible malware assistance
CISA’s “Risk in Focus: Generative AI and Elections,” with information as of January 18, 2024, lists voice imitation and deepfakes among possible election-related uses, alongside phishing and social engineering. It also discusses possible assistance with malware. The document is election-focused, so its examples should not be read as a measurement of all cybercrime. They show how synthetic audio and video can support an existing impersonation or influence operation.
INTERPOL’s Asia and South Pacific Cyber Threat Assessment Report 2025/2026 likewise discusses AI-generated deepfakes and AI-assisted phishing and social engineering in that region. It notes that email remains a continuing attack vector. These are regional assessment findings, not a global count of incidents.
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The criminal objectives
Most operations still aim to deceive a person into doing something that benefits the attacker: transferring money, revealing a password or one-time code, handing over sensitive information, installing software or granting access to an account or system. The requested action, not the sophistication of the wording or media, is the core risk.
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The familiar attack sequence
A typical operation still needs a target, a pretext, a communication channel and a way to collect value. The channel may be email, messaging, a social platform, a phone call or a website. AI can supply more variations of the pretext, but it does not remove the need to reach a person and persuade that person to act.
CISA states the continuity plainly in its election-risk document: “These tactics and types of attacks are not new, but generative AI allows malicious actors to employ them with greater speed and sophistication and for a much lower cost.” Treat that as a warning against two opposite mistakes: assuming every scam is new, and assuming older defenses are automatically sufficient because the underlying tactic is familiar.
The market for stolen data and access
Europol places AI-assisted social engineering inside a wider criminal economy. Its June 2025 assessment announcement describes markets for stolen data and access, including brokers that sell credentials and access to compromised systems. AI is one tool used by participants in that economy; it is not a replacement for the infrastructure, accounts, data and buyers that make criminal operations profitable.
How to judge claims about “AI cybercrime”
Keep three types of statement separate:
- Capability: an AI system can generate text, images, translations or synthetic audio that could support a scam.
- Reported use: an official agency has described criminals using one of those capabilities, as the FBI, Europol, CISA and INTERPOL have done.
- Prevalence or causation: a measured share of all cybercrime uses AI, or AI caused a particular incident.
The cited official materials support the first two categories. They do not establish a global percentage of cybercrime that uses AI, and they do not show that every incident described as AI-related was caused by AI. A synthetic image may be present in a fraud case without being the reason the victim lost money. An operation may also use a language model for drafting while relying on entirely conventional theft and money-movement techniques.
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When reading a headline, ask what is actually evidenced: a demonstrated capability, a reported case, an agency warning or a measured sample. Do not turn a warning about possible malware assistance into proof that a particular attack used AI.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to spot an AI-assisted phishing or impersonation attempt
There is no reliable single “AI tell.” Generated language can be fluent, and a real criminal can copy errors or use a human-written script. Use the request and the verification path as your primary evidence.
Check the action being demanded
- Pause when a message demands secrecy, urgency or an unusual payment route.
- Never provide a password, authentication code, recovery phrase or sensitive document because a message claims to be from support, a colleague or a government office.
- Open the organization’s known website or use a phone number already stored in your records; do not rely on the link or number in the unexpected message.
Verify identity on a separate channel
If a colleague or relative requests money or confidential information, contact them through a channel you initiated. For a voice or video request, ask a question that an impostor would not expect, then confirm with another trusted person. A familiar voice, face or profile image is now weaker evidence than an independently verified conversation.
Inspect the surrounding details
- Check the actual sender address, domain and reply-to field rather than the display name.
- Look for a request that conflicts with normal process, such as bypassing approval or changing payment details at the last minute.
- Treat a polished website, chatbot or profile as presentation, not proof of ownership.
Do not let perfect grammar reassure you
Translation and text generation can remove spelling and grammar mistakes. Their absence is not evidence that a message is genuine. Conversely, an awkward phrase does not prove AI involvement; many conventional scams contain errors.
What individuals and organizations can do
For individuals
- Stop the requested transaction or login while you verify the sender.
- Use a known-good route to contact the person or organization independently.
- Inspect the destination address before entering credentials, and navigate directly if anything is unexpected.
- Use unique passwords and multi-factor authentication so one disclosed password is less useful.
- If you already sent money or credentials, contact the payment provider or service immediately, change affected passwords from a clean device and report the incident to the relevant platform and authorities.
For organizations
- Train staff to verify payment changes, credential requests and urgent executive messages through a second channel.
- Make approval workflows resilient to persuasive text, synthetic audio and convincing video; no single message should authorize a high-impact action.
- Use phishing-resistant authentication where available and limit account privileges so a compromised identity cannot reach every system.
- Monitor unusual sign-ins, mailbox rules, payment destinations and data access, then preserve logs for investigation.
- Test awareness with scenarios that include polished language, translated messages and impersonated voices, not only obvious misspellings.
These controls address the unchanged objective—unauthorized access or transfer—while accounting for AI’s ability to increase speed, volume and realism.
Why geography and date matter
The evidence comes from different kinds of official publication. The FBI item is a US public fraud warning published December 3, 2024. Europol’s statement, published June 11, 2025, summarizes its European cybercrime assessment and the surrounding market for stolen data and access. CISA’s source is an election-specific document whose information was current on January 18, 2024. INTERPOL’s 2025/2026 assessment covers Asia and the South Pacific.
Together, these sources show recurring uses and a coherent risk pattern. They should not be combined into a global prevalence estimate. Threat techniques, criminal services and defensive guidance can change, so current assessments should be checked when making a time-sensitive operational decision.
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AI changes the economics and appearance of cybercrime: an operator can create more convincing material, tailor it more closely to a target and run more interactions at lower effort. It does not make deception, phishing, impersonation, fraud or stolen-access markets new, and it is not required for any of them to work. Defenses therefore need both continuity and adjustment: keep verifying requests, credentials and payment changes as before, while assuming that text, images, voices and profiles can now be produced at scale.
As Edvardas Šileris, Head of Europol’s European Cybercrime Centre, put it: “You can’t defend what you don’t understand.” Understanding the distinction between an AI amplifier and the underlying criminal method is the starting point for proportionate defenses.
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