AI is making familiar cyberattacks faster, more personalized and more convincing—not replacing the underlying playbook. Attackers still want the same outcomes: a password, an authentication code, access to an account, a malware infection, confidential information or a payment. Generative tools reduce the effort needed to write a credible lure, impersonate a trusted person and run many conversations at once.
That means spelling mistakes, awkward wording and obvious visual glitches are no longer dependable warning signs. The safest response is procedural: pause, inspect the sender and link, and verify an unusual request through a contact route you already know is genuine.
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What AI changes—and what it does not
The FBI’s San Francisco field office says AI tools can produce targeted phishing messages with correct grammar and spelling while increasing the speed, scale and automation of existing schemes. Europol’s IOCTA 2025 summary similarly describes criminals using generative AI, including large language models, to tailor scam messages to a victim’s cultural context and personal details.
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- Presentation improves: a lure can sound like a colleague, supplier or family member instead of a mass-mail template.
- Personalization expands: public posts, job information and previous messages can be used to make a request seem relevant.
- Operations accelerate: one operator can generate, translate and adapt messages at a scale that previously required a larger team.
- Impersonation crosses channels: text, email, telephone voice and video can reinforce one another.
Not every phishing email, malware incident or fraud is AI-enabled. The available official statistics are reports of complaints in categories that mention AI, not a census proving that AI caused every incident or that it increased the success rate of all attacks.
How an AI-assisted attack typically unfolds
1. The attacker chooses a familiar pretext
The starting point is usually ordinary social engineering: an urgent invoice, an account warning, a package problem, a job-related request, a family emergency or a demand from an executive. AI helps generate alternatives quickly, but the trust trigger remains familiar.
2. Public and stolen information supplies context
A message can mention a real project, a recent trip, a supplier name or a family relationship. Europol’s 2025 assessment specifically notes tailoring to cultural context and personal details. A personalized sentence is evidence that someone gathered information—not proof that the sender is genuine.
3. The lure moves the target toward an action
The requested action may be opening a link, signing in, downloading an attachment, buying gift cards, changing bank details, sending a payment or revealing an authentication code. FBI campaign descriptions identify account takeover, credential theft, malware delivery and requests for information or funds as recurring outcomes.
4. Follow-up messages maintain pressure
Generative systems can answer questions, translate replies and keep a target engaged. An attacker may move the conversation from email to text or another messaging platform, where the target feels less able to verify the request with colleagues. A polished exchange can still be a trap.
Impersonation now spans email, text, voice and video
Email and messaging
Correct grammar no longer separates a legitimate message from a fake one. AI can produce a convincing tone, imitate common business phrasing and create several versions for different victims. Text-message scams (smishing) may use a short link and an urgent warning; a later message can appear to come from a manager or service provider.
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Voice cloning
The FBI describes criminals using cloned voices to imitate family members, co-workers and business partners. In a May 2025 alert, the bureau warned about texts and AI-generated voice messages impersonating senior officials. The messages built rapport and sometimes shifted the target to another platform containing a malicious link.
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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 minuteA familiar voice is now only one signal. A short recording can be generated from publicly available audio, and a scammer can combine a cloned voice with real personal details. Before sending money, credentials or sensitive information, end the call and ring a number already stored in your contacts or published on an official account.
Video and live-looking meetings
Video manipulation can make a request appear to come from a real executive or relative. A video image, caller ID or profile photograph can be copied or altered. Treat the communication channel as untrusted until you verify the request separately, especially when it changes payment instructions or asks for secrecy.
“As technology continues to evolve, so do cybercriminals’ tactics. Attackers are leveraging AI to craft highly convincing voice or video messages and emails to enable fraud schemes against individuals and businesses alike,” said FBI Special Agent in Charge Robert Tripp.
Can you tell whether a phishing message was written by AI?
No single clue can reliably identify AI-generated content. Awkward writing, strange images, an unnatural voice or a perfect-looking message can all occur in legitimate or fraudulent communications. The FBI warns that AI-generated material can be difficult to identify.
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Use content clues as prompts to verify, not as a verdict:
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- Inspect the complete sender address, telephone number and account name. Look for a substituted domain character or an address that does not match the claimed organization.
- Hover over a link on a computer, or press and hold it on a phone, to reveal the destination before opening it. A familiar display name does not make the URL safe.
- Check whether the request is unusual for that person: a new bank account, secrecy, urgency, a gift-card purchase or an authentication code.
- Compare the request with an independent record, such as a prior invoice, a known company directory or a phone number you saved earlier.
- Do not open an attachment or sign in through a message until the sender has been verified through another route.
Do not reply to the suspicious message to ask whether it is real. A compromised account—or the same attacker—can answer convincingly.
What to do with a suspicious email, text or voice message
- Pause. Urgency is a manipulation technique. Do not click, call a number in the message or move to a new chat at the sender’s request.
- Record the details. Note the sender address or number, displayed name, link destination and the exact request. Preserve the message if your organization needs it for investigation.
- Verify independently. Use a telephone number, website bookmark, company directory or family contact method that existed before the message. For a payment request, speak to the person using that known route.
- Refuse to share authentication codes. A legitimate support worker, bank employee or relative should not need a one-time MFA code sent to your device.
- Report and remove it. Use your email or messaging service’s report function and follow your employer’s incident process. Deleting the message after reporting reduces the chance of an accidental click.
- If you acted already, contain the damage. Change the affected password from a clean device, revoke active sessions where the service permits, contact the bank or payment provider immediately, and tell your organization’s security team.
A family secret phrase
The FBI suggests agreeing on a secret word or phrase with family members. It should not be posted publicly or reused as a password. If a supposed relative calls with an emergency, ask for the phrase and independently call the relative or another trusted person.
How to verify a payment request from a boss, supplier or relative
Payment fraud succeeds when a target treats a familiar voice or polished message as authorization. Use a two-person or two-channel check for any unusual transfer.
- Compare the new request with the last approved invoice or bank details; do not rely on a reply in the same email thread.
- Call the requester using a known number, not the number in the message or caller ID.
- Ask a second authorized person to review the change, particularly a new beneficiary or urgent international transfer.
- Confirm the amount, destination, deadline and reason verbally, then document who approved it.
- If the requester insists on secrecy or refuses a verification step, stop the transaction.
These controls address the underlying fraud opportunity even when the attacker uses no AI at all.
Does multi-factor authentication stop AI-assisted phishing?
MFA substantially reduces the value of a stolen password, but it does not make phishing harmless. Attackers can direct a victim to a fake sign-in page and then ask for the one-time code, or pressure the victim to approve an unexpected login. Never read an MFA code to a caller or type it into a page reached from an unsolicited message.
Use the strongest MFA method your account supports and enable login alerts. A hardware security key is one possible physical option, but compatibility varies by account and service; confirm that an account supports the key before buying one. MFA cannot prevent a victim from authorizing a fraudulent payment, so payment verification remains necessary.
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Organizational defenses that match the attack path
Organizations should combine controls rather than search for an AI detector. NIST’s December 2025 AI Cybersecurity Framework Profile initial preliminary draft calls for updated personnel training and integrated automated email and authentication defenses.
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Reduce exposure
- Use email authentication and filtering to reduce spoofed and malicious messages.
- Block known malicious links and attachments, and quarantine look-alike domains.
- Require phishing-resistant or hardware-backed MFA where the workflow and service support it.
- Limit mailbox forwarding, privileged access and payment-change permissions.
Train for scenarios, not grammar tests
Recurring training should include polished writing, smishing, voice cloning, fake video meetings, QR-code links and requests for MFA codes. Employees need a simple reporting path and permission to pause a transaction without being penalized for delay.
Build verification into payments
Use out-of-band confirmation for changes to bank details, dual approval for high-value transfers and a known-contact directory. A security tool can reduce delivery, but only a human or workflow check can determine whether a legitimate-looking request is authorized.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the reported numbers show
The FBI Internet Crime Complaint Center’s 2025 annual report gives useful scale indicators, with important limits: the figures cover complaints that reported AI-related information and are not a verified estimate of all AI-caused cybercrime.
| Measure | Reported figure | How to interpret it |
|---|---|---|
| Complaints reporting AI-related information | 22,364 | Complaints received by IC3 in 2025; not a count of confirmed AI-enabled attacks. |
| Adjusted losses in those complaints | $893,346,472 | Reported adjusted loss; the report’s prose says losses exceeded $893 million. |
| Business email compromise involving AI | More than $30 million | Business losses reported to IC3 in that category. |
| Confidence or romance scams with a likely AI nexus | More than $19 million | Victim losses in the report’s category and attribution language. |
| Distress scams | More than $5 million | Victim losses in the report’s category; not a complete total for AI-related crime. |
Complaint totals depend on reporting behavior, category definitions and the information victims provide. They should not be converted into an attack-success rate or used to claim that AI caused every loss.
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NIST IR 8596, dated December 2025 and identified as an initial preliminary draft, discusses broader possibilities including realistic malicious websites and links, AI-assisted malware and potential autonomous orchestration of attack phases. Those are evolving risk assessments, not a universal or measured trend across all incidents. NIST also cautions that AI-enabled attacks may go undetected while defenders develop their understanding and countermeasures.
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Separately, NIST AI 100-2 covers threats to AI systems themselves, including evasion, poisoning, privacy attacks and misuse of generative AI. This is related but distinct: an organization can be attacked because it deploys an AI model, while criminals can also use AI to improve an ordinary phishing campaign. The defensive controls and owners may differ.
CISA’s January 2024 document discusses generative AI in the specific context of election-related risks. It is useful context for amplification of existing threats, not a general measurement of cybercrime rates.
A practical decision framework
When choosing a control or procedure, match it to the path the attacker is using:
| Attack path | Priority control | Verification question |
|---|---|---|
| Credential theft | MFA, password manager and link inspection | Am I signing in through a bookmark or a known application? |
| Account takeover | Phishing-resistant MFA, session alerts and recovery protection | Did I initiate this login or approve this device? |
| Payment fraud | Known-number callback and dual approval | Did the requester confirm the destination through an independent channel? |
| Malicious links or attachments | Filtering, sandboxing and cautious opening | Was this file or URL expected and independently verified? |
Then check audience and workflow: an individual may need a family phrase and account alerts, while a company needs documented approvals, training and technical filtering. The best control is one people can use consistently and that adds a genuinely separate verification step.
Bottom line
AI changes the quality, speed and reach of familiar attacks. It can make a phishing email sound professional, turn a text into a convincing conversation and make a cloned voice seem personal. The reliable defense is not guessing whether content was machine-generated. Pause, verify through a previously trusted channel, protect authentication codes, and require independent approval before money, credentials or sensitive data move.
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