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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAI is raising business cybersecurity risk in two directions at once: criminals can use it to make impersonation and fraud more convincing and to speed up parts of cyber operations, while companies that deploy AI systems and agents create additional data, software, and access paths to secure. It amplifies familiar threats; it has not invented cybercrime, and the available evidence does not show that every business attack now uses AI.
What the numbers say—and what they do not
The FBI’s Internet Crime Complaint Center (IC3) reported the following figures for 2025:
| Measure | Reported figure | How to read it |
|---|---|---|
| Complaints reporting AI-related information | 22,364 | IC3 complaint count; not a census of all AI-enabled crime or business exposure. |
| Adjusted losses associated with those complaints | $893,346,472 | IC3’s adjusted-loss figure for the AI-related complaints; it is not a complete accounting of AI-caused losses. |
| Business losses reported for business-email-compromise (BEC) scams involving AI | Over $30 million | Reported BEC losses involving AI. IC3 notes that not all BEC tactics are AI-enabled. |
These are reported complaints and losses, not a measure of every incident or proof that AI caused every loss in the totals. The primary sources reviewed do not establish a reliable share of all business cyberattacks caused by AI or a comparable year-over-year AI-specific business breach rate. Treating either as known would overstate what the figures show.
How AI can make familiar attacks more convincing
Impersonation and targeted messages
Generative AI can help produce text, profile images, audio, and video that support phishing, social engineering, financial fraud, and impersonation of executives or other trusted figures. The FBI’s December 3, 2024 public service announcement describes these as observed uses of synthetic content—not evidence that every unusual message or call is AI-generated. As the FBI puts it, “The creation or distribution of synthetic content is not inherently illegal; however, synthetic content can be used to facilitate crimes, such as fraud and extortion.”
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For a business, the danger is not limited to a generic phishing email. An attacker may use public information to tailor a plausible story, imitate a familiar voice, or create a realistic-looking page or link. NIST’s Cybersecurity Framework Profile for Artificial Intelligence, an initial preliminary draft dated December 2025, discusses these possibilities as risk scenarios, not measured attack rates.
Why visual or grammatical “AI tells” are a weak test
Generated content can be polished and personalized, so awkward wording or obvious image glitches are not dependable ways to decide whether a request is genuine. Verify the identity and the requested action instead. A sudden payment instruction, bank-detail change, request for credentials, or demand for sensitive data should be checked through a separate, trusted channel—even if it appears to come from a known colleague.
How AI may speed up cyber operations
NIST’s December 2025 preliminary draft describes ways AI could help adversaries identify exploitable weaknesses, move through attack paths faster, exfiltrate or tamper with data, and scale activity. It also identifies realistic spear phishing, manipulated audio or video, malicious sites, and malware designed to evade signature-based detection.
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Those are potential capabilities and risk areas, not a claim that every attacker has advanced AI tools, that attacks are autonomous by default, or that AI reliably defeats modern defenses. The practical concern is that some parts of an attack may become faster or easier to adapt, leaving less room for a business to rely on slow verification or delayed response.
AI systems and agents can create new exposure
Protect the system, not just the people using it
AI is also a target. NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, July 26, 2024) identifies prompt injection and data poisoning among security risks. It also calls attention to the code, training data, model weights, availability, and deployment environment associated with AI systems. A business should consider what information can be submitted, where it is processed, who can access related assets, and how the system is maintained.
NIST summarizes the dual risk this way: “GAI-based systems present two primary information security risks: GAI could potentially discover or enable new cybersecurity risks by lowering the barriers for or easing automated exercise of offensive capabilities; simultaneously, it expands the available attack surface, as GAI itself is vulnerable to attacks like prompt injection or data poisoning.”
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Agents need boundaries around actions and access
An AI agent may be connected to business applications, files, or other tools and may take actions rather than only generate text. For each agent, map the information it can reach, the tools it can call, the actions it is permitted to perform, and the point at which a person must approve a consequential action.
NIST’s May 18, 2026 summary of public comments on AI-agent security reports broad agreement that agents present novel security threats and that baseline practices may need adaptation. It is a summary of stakeholder responses, not a finalized universal technical standard. Use access control, oversight, and risk management as practical starting points while agent-specific guidance continues to evolve.
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AI does not make established security controls obsolete. The Federal Trade Commission’s Cybersecurity for Small Business guidance points organizations to the voluntary NIST Cybersecurity Framework 2.0 structure: Govern, Identify, Protect, Detect, Respond, and Recover. Adapt the measures below to the organization’s size, sector, systems, and obligations.
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- Dual USB-A and USB-C Security Key – Features both USB-A and USB-C connectors for seamless compatibility across desktops, laptops, and tablets. Supports plug-and-stay use or keychain carry.
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1. Govern and inventory
- Keep an inventory of AI tools and services in use, including who owns each one, what data it handles, and which business systems it connects to.
- Review contractual and legal requirements that apply to the data and services involved.
- Apply the same inventory discipline to integrations and agents: document permissions and permitted actions, not just the product name.
2. Protect identities, accounts, and money movement
- Require multifactor authentication (MFA) for business accounts and restrict privileged access and access to sensitive data to people who need it.
- Verify unusual payment, credential, and bank-detail-change requests through a known, separate channel. Do not use contact details supplied in the suspicious message itself.
- Set clear approval requirements for consequential transactions and for AI agents that can initiate or alter them.
3. Strengthen email and domain defenses
Configure and monitor SPF, DKIM, and DMARC for company domains. The FTC explains that these complementary email-authentication protocols help receiving servers check whether mail is authorized and how to handle suspicious messages. They can reduce domain spoofing, but they do not prevent every lookalike domain, compromised account, or voice-based scam.
4. Protect systems and data
- Patch software, encrypt sensitive data in transit and at rest, and maintain regular backups.
- For AI services, assess what data staff may enter and how inputs, outputs, integrations, and deployment environments are protected.
- Limit an agent’s permissions to the data and actions needed for its assigned task. Keep human review for actions whose impact warrants it, and retain enough logging to investigate activity.
5. Train staff to verify and report
Use role-specific scenarios involving payment requests, executive impersonation, links, and sensitive-data requests. Teach employees how to report suspicious contact and how to verify an unusual request independently. Measure whether people follow verification and reporting procedures, not only whether they complete a course.
6. Prepare to detect, respond, and recover
Monitor for unauthorized activity and maintain incident-response, disaster-recovery, and business-continuity plans. Test the plans, including how the business will contain compromised accounts or connected AI tools, preserve essential operations, and restore data from backups.
How to choose safeguards without chasing an “AI detector”
No single detector is a substitute for controls around identity, access, data, and approvals. When comparing security measures, use criteria that fit the risk rather than assuming one tool solves the problem.
- Authentication: assess resistance to phishing, rollout and recovery, support across business accounts, management of lost devices, and total cost.
- AI systems and agents: examine data sensitivity, permission breadth, external connectivity, logging and auditability, human approval for consequential actions, and the ability to contain an incident.
- Email protection: compare domain-spoofing coverage, monitoring and reporting, handling of legitimate senders, and ongoing maintenance.
- Training: look for realistic role-based scenarios, regular practice, a clear reporting path, and ways to assess follow-up behavior.
These are decision criteria, not results from a tested vendor comparison. The core business problem is manageable: AI can make some attacks more persuasive or efficient, and AI deployments can add exposure, but disciplined verification, limited access, monitoring, and recovery planning still address the underlying risks.
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