AI is making some cyberattacks more convincing and easier to scale, while giving businesses new systems, data flows, and automated tools to secure. It is an amplifier of existing threats—not the origin of cybercrime, and not a feature of every incident. The practical response is to strengthen identity checks and access controls while treating AI tools and agents as part of the company’s attack surface.
How AI changes the threat to businesses
Businesses face two connected risks. Criminals can use generative AI to create persuasive messages, images, audio, and video; organizations that use AI add software and integrations that may expose sensitive data or enable actions across business systems. Both risks matter, but they call for different controls.
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| Risk | What AI can change | What businesses should focus on |
|---|---|---|
| Social engineering | Messages and impersonations can be personalized or made to look and sound more convincing. | Verify unusual requests through a separate, trusted channel rather than relying on clues such as awkward wording. |
| Attack activity | AI may help identify weaknesses, move through attack paths, or adapt malicious content more quickly. | Keep systems patched, restrict access, monitor activity, and maintain tested response plans. |
| AI deployment | AI systems can be attacked, and their data, integrations, and permissions can create additional exposure. | Limit what each system can access and do; review consequential actions and protect data inputs and deployments. |
Why AI-assisted impersonation is hard to spot
Generative models can help draft credible messages quickly, translate them, create profile imagery, and produce synthetic voice or video. The FBI says such material has been used in social engineering, spear phishing, financial fraud, fake identities, and attempts to impersonate executives or other authority figures. NIST’s December 2025 initial preliminary draft also describes realistic spear-phishing messages, manipulated audio or video, and convincing malicious pages and links. NIST notes that publicly available personal information can help attackers tailor their stories; that assessment is preliminary, not a measured attack-rate study.
A polished message, familiar voice, or plausible video is not proof that a request is genuine. The FBI cautions that synthetic content is not inherently illegal, but can be used to facilitate crimes. For payment changes, credential requests, or requests for sensitive information, use a known phone number or another independent contact method—not the contact details in the message or an incoming call. A voice or video confirmation should not replace an established approval process.
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What the reported AI-related losses show—and do not show
The FBI Internet Crime Complaint Center’s 2025 Internet Crime Report records 22,364 complaints reporting AI-related information and adjusted losses of $893,346,472. The report also says businesses reported more than $30 million in losses to business-email-compromise scams involving AI. These are complaint and adjusted-loss figures, not a census of actual losses or a complete measure of business exposure. The FBI specifically notes that not all BEC tactics are AI-enabled.
The figures establish that reported AI-related fraud is a real concern, but they do not tell a business what share of all cyberattacks used AI. The reviewed official sources do not establish a reliable overall percentage of business attacks caused by AI or a directly comparable year-over-year AI-specific business breach rate. It would be misleading to treat the reported BEC losses as the total cost of AI to businesses.
AI may speed attacks, but it does not make every attack autonomous
NIST’s initial preliminary draft Cybersecurity Framework Profile for Artificial Intelligence, dated December 2025, describes ways AI-enabled activity could help adversaries identify exploitable weaknesses, advance along attack paths faster, exfiltrate or tamper with data, and scale attacks. It also discusses malware designed to evade signature-based detection. These are potential capabilities and risk scenarios—not evidence that every attacker has them, that AI reliably defeats modern defenses, or that attacks now run without human involvement.
That distinction matters for planning. Businesses do not need to assume every incident is AI-driven to justify patching, limiting privileges, monitoring for unauthorized activity, and preparing to restore operations. Those controls address familiar weaknesses that AI might help an attacker exploit more efficiently.
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AI systems and agents create exposure on the defender’s side
An AI tool can process information, connect to services, and—in some deployments—take actions. That makes its permissions and data paths security questions, not just product features. NIST’s 2024 Generative AI Profile identifies prompt injection and data poisoning among the risks to AI systems. It also highlights concerns involving code, training data, model weights, availability, and deployment environments.
Agents deserve particular scrutiny when they can act across systems or use tools. NIST’s May 18, 2026 summary of public comments reports broad agreement that agents present novel security threats and that baseline practices may need adaptation. It summarizes stakeholder responses; it is not a finalized, universal technical standard. In practice, establish what an agent can read, which services it can reach, what actions it can take, and which actions require human approval. Keep access no broader than the job requires, and log activity so unexpected behavior can be investigated.
Practical controls businesses can put in place
The FTC’s small-business cybersecurity guidance points organizations to the voluntary NIST Cybersecurity Framework 2.0: Govern, Identify, Protect, Detect, Respond, and Recover. The framework is a useful way to organize work; the right implementation depends on a business’s size, sector, systems, and obligations.
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- Govern and inventory: Assign an owner to each AI system or service in use. Record what data it handles, which systems it connects to, who can use it, and any relevant contractual or legal requirements. Include employee use of external AI services in the inventory.
- Protect identities and approvals: Require multifactor authentication (MFA), limit privileged access, and restrict access to sensitive data. Verify payment instructions and credential changes using a trusted, separate channel, with approval steps appropriate to the risk.
- Authenticate company email: Configure and monitor SPF, DKIM, and DMARC for company domains. The FTC explains that these protocols help receiving servers authenticate authorized mail and handle suspicious messages. They can reduce domain spoofing, but do not prevent every lookalike domain, compromised account, or voice scam.
- Protect systems and data: Patch software, encrypt sensitive information in transit and at rest, and keep regular backups. Review whether AI services retain or expose information submitted to them before staff enter confidential material.
- Train staff to verify and report: Teach employees to pause on unusual requests, check identity using independently obtained contact details, and report suspicious activity through a clear internal route. Grammar errors and visual glitches are weak primary tests when generated content can look convincing.
- Prepare to detect and recover: Monitor for unauthorized activity and maintain incident-response, disaster-recovery, and business-continuity plans. Test that backups and recovery procedures work before an incident occurs.
- Constrain AI tools and agents: Apply ordinary access-control and risk-management principles to AI deployments. Limit accessible data, connected services, and permitted actions; require review for consequential actions; and make sure activity can be audited and access can be withdrawn.
How to assess an AI deployment before expanding it
Before connecting an AI system to business data or workflows, assess its exposure rather than relying on a general claim that it is secure. The answers help determine whether a pilot should remain isolated, needs tighter permissions, or is ready for broader use.
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- Data: What information can users submit, and what information can the system retrieve? Could it expose customer, employee, financial, or other confidential data?
- Permissions: Which accounts, files, and services can it access? Are those permissions limited to the task?
- Actions: Can it send messages, change records, move money, or alter access? Which actions need human approval?
- Visibility: Are inputs, outputs, connected tools, and actions logged well enough to investigate misuse or errors?
- Containment: Can administrators disable integrations, revoke credentials, or isolate the system quickly if something goes wrong?
These questions are especially important for agents with cross-system access. Start with the smallest useful set of permissions, then expand only when the added access has a clear business purpose and suitable oversight.
Why a single AI detector is not a security plan
Trying to identify AI-generated content from style alone is unreliable as a business defense: authentic-looking text, images, or audio can be used in fraud, while not every unusual message is AI-generated. Authentication, independent verification, restricted access, and layered monitoring address the underlying risk whether a criminal used AI or not.
For the same reason, email authentication is one layer rather than a complete phishing solution. SPF, DKIM, and DMARC help receiving systems assess whether mail is authorized for a domain; they cannot validate every request made through a compromised mailbox, a lookalike domain, or a synthetic voice call.
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