Five Cybersecurity Risks on Using AI

Small and mid-sized businesses account for over 70% of all data breaches. The tools attackers are using have gotten significantly more sophisticated in the past 12 months. AI has lowered the barrier to entry for cybercrime — making attacks faster, cheaper, and harder to detect than anything that came before.

However, this isn't a reason to avoid AI. It's a reason to understand what you're dealing with.

Five AI Cybersecurity Risks

1. AI-Powered Phishing

The usual advice was simple: look for bad grammar, suspicious formatting, and generic salutations. Unfortunately, that advice is now outdated.

Generative AI now lets attackers create highly convincing emails in seconds. Cybercriminals are using AI phishing tools to create messages that sound polished, personal, and completely believable. AI-generated phishing emails now achieve click-through rates more than four times higher than their human-crafted counterparts. These messages reference real transactions, mimic your vendors' writing styles, and simulate internal workflows your team uses every day.

Fact: 82.6% of phishing emails now contain AI-generated content.

What to do: Implement multi-factor authentication (MFA) on every account that matters: email, banking, your CRM, your hosting. For financial requests or sensitive data, build a verification protocol that requires a second channel of confirmation, opt for a phone call to a known number, not a reply to the same email thread.

2. Deepfake Fraud

Deepfakes are the threat that most business owners haven't fully absorbed yet and it's the one with the most dramatic consequences.

Criminals now generate real-time video and audio that perfectly impersonate executives, government officials, and business partners. The FBI's IC3 has flagged deepfake-assisted fraud as the fastest-growing category of AI cybersecurity threats in the United States.

The most cited example: a finance worker at a multinational corporation was tricked into authorizing a $25.6 million payment after a video conference call with what appeared to be the company's CFO and several colleagues — all of whom were deepfake-generated replicas.

This isn't only an enterprise problem. Deepfake fraud growth has skyrocketed by 3,000%, with average per-incident losses ranging from $150,000 to $250,000 for SMBs.

What to do: Establish a simple rule: any request involving money, credentials, or sensitive data requires out-of-band verification. A video call alone is no longer sufficient confirmation. Call back on a number you already have on file — not one provided in the request.

3. Fake AI Tools

Fake AI tools are particularly relevant given the current wave of AI tool adoption among small businesses.

From January to April 2026, Kaspersky solutions detected 33,352 attacks on SMB users in which malware or unwanted applications were disguised as five popular AI services. Attackers are aware of the demand for AI tools and they are exploiting it by creating convincing fake versions of popular software that install malware the moment you download them.

Google Cloud's Cybersecurity Forecast 2026 also flags "shadow AI" — unsanctioned AI tools used inside organizations — as a fast-rising threat. When employees adopt AI tools independently, outside of any organizational review, the business loses visibility into what data those tools are accessing and where it's going.

What to do: Only download software from official sources — the tool's own website or verified app stores. If you have a team, establish a simple approval process for new AI tools before they're used with business data. The goal is to know what's in your environment, not necessarily blocking your business from AI adoption.

4. Vendor and Third-Party Data Exposure

The AI tools and platforms your business relies on don't just process your data. The tools you use store your data, train on it, or pass it through third-party infrastructure. Business owners sign up for a tool without reading what happens to their data under the terms of service.

This risk is compounded when the tools are built on models subject to government access laws in other jurisdictions. As covered in our earlier piece on Z.ai and GLM-5.2, China's National Intelligence Law requires Chinese companies to cooperate with government intelligence requests, regardless of where their servers are physically located or what their privacy policy states. The U.S. House opened a formal inquiry in May 2026 into cybersecurity risks from Chinese AI models in critical infrastructure.

What to do: Before adopting any AI tool that touches client data, contracts, or financial information, spend ten minutes reviewing the privacy policy and terms of service.

What to look for when reviewing an AI tool's policies:

• where is your data stored,

• is it used to train the model, and

• what jurisdiction governs the company?

For higher-sensitivity data, prioritize tools that offer data processing agreements and clear data residency commitments.

5. Weak Access Controls

Attackers are using AI across the full attack lifecycle: reconnaissance, initial access, credential theft, evasion, and persistence. The entry point in the majority of cases isn't a sophisticated technical exploit. It's a weak password, a reused credential, or an account with more access than it needs.

Twenty-eight percent of SMB respondents admit the person managing their cybersecurity doesn't have sufficient training. More often than not, it's the business owner themselves. AI-assisted attackers can now test millions of credential combinations in minutes. An account protected by a weak password and no MFA is not a question of if it gets compromised but when.

What to do: Audit who has access to what in your business systems. Remove access for former team members immediately. Use a password manager so every account has a unique, strong password. Enable MFA on everything. For your most critical systems, treat access controls as infrastructure.

AI-powered attacks have increased 72% year-over-year globally and 87% of organizations reported experiencing an AI-driven cyberattack in the past year. These numbers aren't meant to cause panic. They're meant to recalibrate the assumption that cybersecurity is someone else's problem.

The good news: organizations using AI-driven security detect threats 60% faster and save an average of $1.9 million per breach. The same technology being used to attack businesses is available to defend them. Awareness is the first layer of that defense, and it costs nothing.

The five risks above aren't theoretical. They're the ones showing up in real incidents affecting businesses at the scale most of our clients operate at. Understanding them is the starting point. Acting on them is what makes the difference.

Virtual Central is a done-for-you digital systems team specializing in website design, GoHighLevel setup, and AI-powered automation for small business owners and agencies across the US and Canada.

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