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Criminals can use generative AI to make phishing and financial-fraud messages more convincing, but that is different from an AI system independently deciding to steal credit-card numbers. The FBI reports AI-related complaints and credit-card/check fraud complaints as separate totals; it does not state how many card-fraud cases involved AI. For protection, AI can support cybersecurity defenses, but it does not replace core controls such as multifactor authentication, careful verification, and secure devices.

Is AI stealing credit-card numbers?

There is evidence that criminals use generative AI to help carry out fraud. In a December 3, 2024 public service announcement, the FBI Internet Crime Complaint Center (IC3) warned that “Criminals use AI-generated text to appear believable to a reader in furtherance of social engineering, spear phishing, and financial fraud schemes such as romance, investment, and other confidence schemes or to overcome common indicators of fraud schemes.” Read the FBI IC3 announcement.

That describes a person using AI as a tool—not proof that an autonomous “rogue AI” is independently targeting people’s cards. AI-generated text may help a scammer impersonate a bank or retailer, personalize a message, or make a fraudulent request sound more plausible. A recipient may then be persuaded to disclose a card number, login, or verification code, or to visit a deceptive site.

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What the FBI’s fraud numbers do—and don’t—show

The FBI IC3’s 2025 Annual Report lists 22,364 complaints with the “AI Related” descriptor and 18,774 complaints classified as Credit Card/Check Fraud. These are separate measures, not a count of AI-enabled credit-card theft. The report explains that descriptors identify a medium or tool used to facilitate a crime; the figures do not show how many card-fraud complaints also carried the AI-related descriptor. See the FBI IC3 2025 Annual Report.

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The Federal Trade Commission reported $12.5 billion in total fraud losses reported by consumers for 2024. That aggregate covers fraud broadly; it is neither an AI-specific figure nor a credit-card-only total. The FTC also said reported losses to scams that started online exceeded losses from more traditional contact methods. Read the FTC’s summary of 2024 scams.

How AI-supported and traditional cybersecurity differ

These approaches are not competing replacements. Traditional controls aim to prevent unauthorized access, block or detect malicious activity, and limit damage. AI can augment security work, for example by helping identify patterns or support analysis, but its usefulness depends on the system, threat, deployment, and oversight. NIST describes both the potential for AI to augment cybersecurity and the need to manage AI-related risks. See NIST’s AI and cybersecurity overview.

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Security approach What it can do What it cannot guarantee
Multifactor authentication (MFA), especially phishing-resistant FIDO authentication Make stolen passwords less useful for account access; phishing-resistant methods can better resist credential-phishing attacks. It does not prevent every kind of fraud or protect an account where it is not enabled or supported.
Email authentication and endpoint detection and response (EDR) Support checks on email legitimacy and detection of suspicious activity on devices. Effectiveness depends on configuration, coverage, and how alerts are handled.
Zero-trust access principles Reduce reliance on automatically trusting a user or device simply because it is inside a network. They are an access strategy, not a guarantee that every attack will be stopped.
AI-assisted defensive analysis May augment analysis and help defenders examine activity or prioritize investigation. It is not established as universally superior to traditional controls, and it can introduce risks that need management.

CISA lists MFA/FIDO authentication, EDR, email authentication, and zero-trust principles among layered measures for addressing sophisticated AI-enabled phishing. Its guidance is framed around election security, so it supports these controls as security practices—not a claim about the prevalence of consumer card fraud. Read CISA’s guidance on generative AI and elections. NIST’s identity guidance also addresses AI and machine learning in identity systems. See the NIST SP 800-63 FAQ and NIST SP 800-63-4.

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How to make your accounts harder to take over

  1. Turn on MFA. Enable it for email, banking, shopping, and social accounts. If an account supports phishing-resistant FIDO authentication, prefer that option. A FIDO2-compatible hardware security key is one way to use this method, but check that each account supports the key before buying one.
  2. Use unique passwords. A password manager can generate and store different passwords for each account. Protect its vault with a strong master passphrase and enable MFA on the manager when available; the vault itself contains sensitive information.
  3. Pause before acting on urgent messages. Be cautious with unexpected links, requests for payment, and messages asking for passwords or verification codes. Don’t use contact details supplied in a suspicious message to confirm it. Instead, reach the organization through a known, trusted channel, such as its official app or a number you already have.
  4. Keep devices and security controls maintained. Apply available updates and use the protections provided by your device and security software. For organizations, CISA’s layered guidance includes email authentication, EDR, MFA, and zero-trust access principles; deploy and review controls according to the systems they protect.

NIST recommends password managers as a way to support stronger, unique passwords while emphasizing that users must protect the manager’s vault. See the NIST SP 800-63 FAQ.

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Is AI cybersecurity better than traditional cybersecurity?

There is no universal winner established by these sources. They do not provide a controlled, head-to-head test or a general performance ranking. The useful question is whether the controls in place address the threat: phishing-resistant authentication can make stolen passwords less effective, while email and endpoint defenses may help detect or contain other activity. AI may augment some defensive work, but its presence alone does not establish that a system is safer. Coverage, configuration, and human review of suspicious activity still matter.

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