AI Risks for Businesses: 5 Security Threats Small Businesses Can’t Ignore

Artificial intelligence (AI) is already part of how businesses operate. It’s being used to draft emails, summarize meetings, analyze data and automate tasks. And in many cases, it’s happening faster than leadership teams can put structure around it. That’s where the risk begins.

In our recent Fairdinkum webinar, we explored a simple reality: the biggest AI risks for businesses aren’t theoretical — they’re already showing up in day-to-day use.

Our goal isn’t to slow adoption of AI technologies. We just want make sure it’s happening with the right guardrails in place.

What Are the Real AI Security Risks for Small Businesses?

The use of AI models introduces a new layer to your environment.

It’s not just another tool — it’s a system that processes information, makes decisions and, in some cases, takes action on your behalf. And while it’s powerful, it’s not perfect.

As Fairdinkum’s James Jakubowski, Sr. Systems Engineer, explained during the session, “It’s not a definitive source of truth… AI can hallucinate and provide incorrect responses.”

That combination — speed, access and unpredictability — is what makes AI security vulnerabilities different from traditional IT risks. The good news is that most are manageable once you know where to look.

Based on what we’re seeing across SMB environments, these are the five areas of AI adoption that deserve the most immediate attention.

Risk #1: Employee AI Usage and Data Exposure

For most small businesses, the biggest risk isn’t external. It’s internal. Employees are using AI tools to move faster—drafting emails, reviewing documents, organizing information. None of that is inherently risky. The issue is what gets shared in the process.

It’s easy to paste in an email thread, a client summary or internal notes without thinking twice. But once that information is entered into an AI system, control becomes less clear.

Alan Zimbalist, Sr. Managing Engineer at Fairdinkum, noted, “Employees often upload seemingly mundane content like emails and client data… and you don’t know what’s going to happen with that data afterwards.”

That’s where exposure happens — not through bad intent, but through everyday behavior.

The fix starts with clarity. Define what should never be entered into AI tools, especially anything tied to client or sensitive data, financials or personally identifiable information. From there, reinforce it through training and by standardizing which tools are approved for use.

Risk #2: AI-Powered Cyberattacks

AI is also changing how attacks are carried out.

Phishing emails are more convincing. Messages are more personalized. And bad actors can now scale their efforts in ways that weren’t possible before.

What used to require time and research can now be automated. Public information — websites, social profiles, company data — can be pulled together and used to create highly targeted AI-generated content that looks legitimate.

There’s also a growing concern around deepfakes. “It doesn’t need a lot of data,” explains Jakubowski. “About 20 seconds of video is enough to create something convincing.”

That changes the nature of impersonation risk, especially for leadership teams and client-facing roles.

The response here isn’t just technical. It’s behavioral. Employees need to be trained to question what they’re seeing and hearing, even when it looks familiar. And foundational controls — like multi-factor authentication — become even more important.

Risk #3: Shadow AI Inside the Business

Another challenge we’re seeing more often is the rise of “shadow AI.” This happens when employees adopt AI tools on their own — testing platforms, connecting workflows, solving problems quickly without going through IT.

On one hand, it shows initiative. On the other, it creates a blind spot.

As John Iacono, Fairdinkum CIO, emphasized, allowing unrestricted AI usage across multiple platforms makes it difficult to track data, standardize workflows and properly vet security risks. Different tools handle data differently. Without standardization, there’s no clear understanding of where information is going or how it’s being used.

The best way to address this is to narrow the field. Identify one or two approved platforms that meet your needs, vet them properly and make them the standard across the organization. That balance of enablement with structure keeps things manageable.

Risk #4: Too Much Access, Too Quickly

Not all AI systems operate the same way. Some are limited to prompts and responses. Others — often referred to as agentic AI — can interact more directly with systems, files and applications. That’s where data security risk can increase quickly.

If these tools are given broad permissions, they can act beyond what the user intended. In some cases, they can access or modify data at a level that exceeds normal user controls. Iacono explained, “Once that access is granted, it can have more control over the system than the user themselves.”

That doesn’t mean these tools shouldn’t be used, but they do need to be introduced carefully.

Limiting permissions, using dedicated accounts and avoiding admin-level access are simple ways to reduce exposure to potential risks. For many businesses, it also makes sense to test these tools in controlled AI project environments before rolling them out more broadly.

Risk #5: No Defined AI Policy

Most small businesses are still figuring out how AI output fits into their operations. As a result, many don’t have a formal policy in place yet. That leaves employees to make decisions on their own: what tools to use, what data is acceptable, how outputs should be handled. Over time, that lack of consistency creates risk.

As Zimbalist pointed out, policies don’t need to be perfect from the start, they just need to establish a baseline and evolve over time.

At a minimum, an AI policy should define:

• which tools are approved
• what data can and cannot be used
• where human review is required

From there, it can grow alongside your usage.

How Small Businesses Move Forward with Confidence

Despite the potential risks, AI isn’t something to avoid. Instead, approach it with intention.

The businesses seeing the most value right now aren’t the ones moving the fastest. They’re the ones putting structure in place early. They understand where AI fits, where it doesn’t and how to use it responsibly. That starts with a few practical shifts:

• setting clear expectations for how AI is used
• limiting where sensitive data can go
• and making sure there’s always a layer of human oversight

From there, AI becomes easier to manage and far more valuable. Because at this point, it’s already part of how work gets done. The real question is whether it’s being used with clarity and control — or without it.

Want the Full Breakdown?

This overview highlights the most immediate AI security risks for small businesses, but there’s more behind each of these areas.