AI risk management is now a constant consideration for businesses of every size. They are already using tools like ChatGPT and Microsoft Copilot in everyday work. Many staff use these tools without formal approval, and most organisations don’t know how widespread that usage really is.
The result is quiet, untracked adoption. AI shows up in browser extensions and everyday workflows, without IT or leadership ever being notified.
Imagine an office administrator pasting a confidential client project brief into a public AI chat window to reword an email. In doing so, sensitive company data may be exposed outside the organisation’s control.
Generative AI has enormous potential, including writing assistance and analysis workflows, but if it is not governed, it can quickly become a source of data leakage, bias, prompt manipulation, and regulatory compliance breaches.
Want a broader look at how small businesses can make smart, safe moves into AI? Don’t miss our guide: AI for Small Business: Where to Start, What to Avoid, and What to Expect.
Step 1: Spot the AI You Didn’t Approve
Gartner research indicates a large share of organisations suspect or have evidence employees use unauthorised AI tools, creating security issues.
This happens because staff find AI in everything: browser extensions, embedded features in software, public chat tools. Employees often paste internal data, including client information and financials, into these systems without understanding the ramifications. These tools are outside your control and outside your logging and monitoring.
A light discovery audit can give you immediate insight. Start by logging:
- All AI‑enabled tools users have installed on workstations or cloud services.
- Copilot, Bard or Teams embedded AI features.
- Browser extensions that include generative AI capabilities.
Ask:
- CFO: Have confidential documents or financial data ever been run through an unsanctioned AI tool?
- IT Manager: Which endpoints have AI tools installed, and where are they communicating?
You need to surface unauthorised AI tools during routine visits or audits, identifying shadow AI before it exposes critical data.
Step 2: Define Acceptable AI Use
Once you know what’s in use, the next step is to set clear expectations. A formal AI risk management framework usage policy does not need to be a lengthy compliance tome. The goal is practical clarity for your staff.
Ground your policy in three categories:
- What’s allowed: Internal brainstorming, editing non‑sensitive content, general research tasks.
- What’s restricted: Client data, internal financials, HR records, or proprietary information.
- What’s prohibited: Any upload of regulated data (personal health, financial identifiers, legal contracts etc.) into public or unapproved systems.
You can integrate this into your broader acceptable use policy, and include it in onboarding and annual reviews. There are ready‑made templates and governance best practices available that describe how to control AI risks in policies that remain updated as technologies evolve.
A short, enforceable policy helps set expectations without bottlenecking innovation.
- CFO: “Clarity now avoids reputational risk later.”
- IT Manager: “Ensure teams aren’t bypassing controls you’ve invested in.”
For help turning these principles into a real policy, check out our full walkthrough on Building an AI Compliance Framework for Australian SMBs That Use Microsoft Copilot and ChatGPT.
Step 3: Put Technical Guardrails in Place
Awareness and policy are not enough without technical controls. These controls should align with your size and threat profile.
Endpoint Management
Use device management solutions to restrict or monitor installation of unsanctioned AI tools. In small businesses, this might be as straightforward as group policy settings or limited administrative rights.
Network and Web Controls
DNS or web filtering services can block known public AI tool traffic from business networks unless explicitly authorised. This reduces the risk of accidental or intentional data uploads to unsanctioned services.
Data Loss Prevention (DLP)
A core risk with generative AI is data leakage. Data leakage happens when sensitive information ends up processed or stored by an external model. Generative AI systems can inadvertently expose proprietary or regulated data if not properly constrained.
Prompt Injection Awareness
Prompt injection attacks are a unique risk for generative AI. Malicious actors craft inputs that manipulate model behaviour or extract confidential information. Providers like AWS and OWASP describe prompt injection as one of the critical AI risk management framework applications.
Train staff on basic red flags, such as unusual prompts or unexpected information requests. Even simple controls like input sanitisation and whitelisting content sources help. When supported by reliable IT Security Services, these measures become much easier to maintain and scale.
Step 4: Monitor, Log, and Learn Constantly
AI risk oversight is ongoing. Systems can change quickly; a tool you approved today may introduce new endpoints tomorrow. Monitoring and logging help you surface anomalies early.
Ensure you have telemetry available for AI tools you sanction. For example, Microsoft 365’s admin and security logs capture Copilot usage, giving you insight into queries and data flows through that service.
When you build continuous monitoring into your regular IT strategy review, you make risk visible instead of hidden. Regular review also maps usage patterns over time so you can align future decisions with real behaviour.
- CFO: “You can’t measure risk or ROI if you cannot see how AI is used.”
- IT Manager: “Continuous monitoring means less firefighting down the road.”
If you’re exploring Microsoft 365’s built-in AI, this breakdown can help you prepare: Microsoft Copilot Readiness Assessment: Is Your Business Ready for AI?.
Step 5: Prepare for the Worst
Even with the best controls, incidents can occur. That is just reality with any technology in production today. The question is whether you have a response plan.
Small AI misuse events can escalate quickly into legal exposure or loss of stakeholder trust. Incident planning components include:
- Immediate containment: Who gets alerted first if something goes wrong? Internal IT? External support partners? Legal counsel?
- Communication plans: Define who tells staff and stakeholders and how you document regulatory requirements or notifications.
- Remediation: How will systems be restored? What are the timelines?
Creating an AI incident response plan before an event gives you confidence and clarity. Too often small businesses react ad hoc, leading to confusion or delay when every minute counts.
Deployus can help small businesses build lightweight incident response playbooks and test them annually as part of Business Continuity Planning services.
Risk‑Ready Means Business‑Ready
Preparing for AI risk management doesn’t mean building a whole new department. With a few focused steps, you can start managing AI in a way that protects data and supports your team’s productivity.
Deployus helps SMBs put real-world controls in place, before they’re needed. We support CFOs, practice managers, and internal IT with clear advice, flexible support, and a team that already knows your systems.
Unchecked AI use can expose sensitive information and introduce risks associated with AI. But with the right guardrails, like continuous monitoring, clear policies, and a simple response plan, you’ll meet regulatory requirements without overengineering the solution.
Need help managing AI risk without overcomplicating things? Our team can help you set up smart, right-sized controls, tailored to your systems and budget. Explore our AI Consulting services and get started.
Frequently Asked Questions
What is AI risk management, and why does it matter for small businesses?
AI risk management is the process of identifying and controlling potential risks associated with using AI tools in your business. For small and mid-sized organisations, unmanaged AI use can lead to data leaks, compliance breaches, or poor decision-making.
Does Deployus use the NIST AI RMF or other frameworks?
Yes. We align our approach to industry-recognised frameworks like the NIST AI RMF (National Institute of Standards and Technology AI Risk Management Framework). But rather than hand over a document, we translate it into practical actions that suit your business, systems, and budget.
What kind of AI tools should be covered in our policy?
Any AI tools your staff use, whether approved or not, should be assessed. This includes ChatGPT, Copilot, browser extensions, and software with embedded AI features. A strong AI risk management framework accounts for both known and unknown usage, and sets clear expectations around sensitive information and acceptable use.
How do we start managing AI risk without overcomplicating things?
Start with the basics: audit your current tools, define clear usage policies, and implement a few key technical controls. Then build in continuous monitoring and plan for incidents.