Artificial intelligence has gone from a curiosity to a core business tool in a remarkably short time. Employees are using AI to draft emails, summarize documents, generate code, analyze data, and automate workflows. In many organizations, this is happening whether leadership has sanctioned it or not.

That gap between AI adoption and AI governance is where risk lives. And for small businesses—where one misstep can be disproportionately damaging—closing that gap should be a priority.

We explored the broader phenomenon of unauthorized AI adoption in our article on shadow AI. This piece is the practical follow-up: a concrete checklist for getting your AI security posture in order.

1. Know What AI Tools Your Team Is Actually Using

You can't secure what you can't see. Before setting any policies, you need an honest inventory of the AI tools in use across your organization. This includes the obvious ones—ChatGPT, Claude, Copilot—but also AI features embedded in existing software that employees may not even think of as "AI."

Many SaaS platforms have quietly added AI capabilities. Your CRM might be using AI to score leads. Your email platform might be offering AI-generated replies. Your design tools might be using AI for image generation. Each of these represents a potential data flow that needs to be understood.

Start by surveying your team. Ask directly what tools they're using and why. The goal isn't to punish anyone—it's to get visibility. If employees fear reprisal, they'll continue using tools in the shadows, which is worse.

2. Establish a Clear AI Usage Policy

Once you know what's being used, create a written policy that establishes boundaries. We covered the components of a solid policy in our article on what businesses need in an AI usage policy. At minimum, your policy should address:

  • Approved tools: Which AI services are sanctioned for business use and which are prohibited
  • Data classification: What types of data can and cannot be entered into AI tools (client data, financial records, proprietary information, and personal information should almost always be off-limits)
  • Output verification: A requirement to review and verify AI-generated content before using it in any business capacity
  • Account requirements: Whether employees should use business accounts versus personal accounts, and whether enterprise licensing is required

The policy doesn't need to be long. It needs to be clear, accessible, and actually communicated to every employee.

3. Lock Down Data Inputs

The most immediate AI risk for most businesses isn't a sophisticated attack—it's an employee pasting confidential data into a chatbot. Client records, financial projections, internal strategies, employee information, source code—all of it can end up in training data or stored on third-party servers if your team doesn't understand the implications.

Practical steps include:

  • Enabling enterprise versions of AI tools that offer data retention controls and opt-outs from training
  • Configuring DLP (data loss prevention) tools to flag sensitive data being sent to AI service domains
  • Training employees on what constitutes sensitive data—many don't realize that a client's email address combined with their project details is personally identifiable information

4. Understand How AI Changes Your Attack Surface

AI doesn't just create internal governance risks. It's actively changing the external threat landscape your business faces.

AI-powered phishing has made social engineering dramatically more effective. Self-rewriting AI malware can evade traditional detection. Deepfake voice scams can impersonate executives convincingly enough to authorize wire transfers.

Understanding these threats matters because they require updated defenses. If your security awareness training still focuses on spotting broken English in phishing emails, it's not preparing your team for what they'll actually encounter. And if your endpoint protection relies on signature-based detection, it may miss AI-generated malware that changes its code with each execution.

5. Update Your Security Awareness Training

Your employees are both the users of AI and the targets of AI-powered attacks. Training needs to address both sides.

On the usage side, employees should understand:

  • Why pasting sensitive data into AI tools is risky
  • How to use AI responsibly within the company's approved guidelines
  • That AI outputs can be wrong, biased, or fabricated—and why verification matters

On the defense side, training should cover:

  • How AI makes phishing emails harder to spot (no more relying on grammar mistakes)
  • The rise of voice and video deepfakes in business contexts
  • Verification procedures for any unusual request, regardless of how legitimate it appears

As we discussed in our piece on the human factor in security, awareness training works best when it builds a verification mindset rather than just teaching people to spot specific red flags.

6. Review Your Vendors' AI Practices

Your software vendors are integrating AI into their products whether you asked them to or not. This means your business data may be flowing through AI systems you didn't choose and didn't evaluate.

For each critical vendor, find out:

  • Are they using AI features that process your data? Can you opt out?
  • Where is your data being sent for AI processing? Is it staying within your geographic jurisdiction?
  • Is your data being used to train their AI models? What are their data retention policies?
  • What security certifications or compliance standards apply to their AI features?

Most vendors have updated their terms of service and privacy policies to address AI. Read them. If the answers aren't clear, ask directly. A vendor that can't clearly explain how your data interacts with their AI systems is a vendor worth questioning.

7. Plan for AI-Specific Incidents

Your incident response plan likely covers ransomware, data breaches, and business email compromise. Does it cover AI-related incidents?

Consider scenarios like:

  • An employee accidentally feeds a client's confidential legal documents into a public AI chatbot
  • Your company's proprietary data appears in an AI model's output, suggesting it was included in training data
  • A deepfake video of your CEO surfaces, making statements that affect your business
  • An AI tool your team relies on has a security breach, exposing the prompts and data your employees submitted

For each scenario, you should have a basic understanding of who to notify, what to contain, and what your obligations are—particularly around client data and regulatory requirements.

The Bottom Line

AI isn't going away, and banning it outright isn't practical for most businesses. The organizations that will navigate this well are the ones that take a clear-eyed approach: understand how AI is being used, set reasonable boundaries, update defenses for AI-powered threats, and plan for incidents before they happen.

None of these steps require a massive budget or a dedicated AI team. They require attention, clear communication, and a willingness to adapt security practices as the technology evolves.

If you're not sure where to start, our free cybersecurity assessment evaluates your overall security posture—including how prepared your organization is for AI-related risks. It's a practical way to identify gaps and prioritize what to address first.

This article is intended for informational purposes only and does not constitute professional security, legal, or compliance advice. Organizations should consult with qualified professionals to assess their specific circumstances and develop appropriate protective measures.