The Hidden Risks of Shadow AI in SMBs

In our last blog, we briefly discussed the impact of "Shadow AI," a modern extension of the challenges around the use of unapproved apps in a business network. But this trend deserves a deeper dive because AI is providing the newest opportunity for shadow IT—except it can leak sensitive data in one inadvertent copy and paste.

Key Takeaways

• Shadow AI significantly increases data exposure, compliance gaps, and operational risk for SMBs.
• Employees are already using AI tools — governance and approved alternatives are essential.
• The most effective response to Shadow AI is structured adoption, clear policy, and human validation — not prohibition.

Let's start with a refresher: Shadow IT is a common practice fueled by innocent-enough intentions: Employees go looking for, download, and then use apps, often free, for business purposes without the approval of IT administrators. These team members aren't trying to circumvent or undermine cybersecurity measures; most often, they are trying to get work done faster or avoid a cumbersome internal solution. Shadow IT often involves cloud-based apps, mobile apps, or even undisclosed personal devices. Even if users have good intentions, using apps without authorization or the proper security protocols in place brings great risk to your IT environment.

Why is Shadow AI a Risky Practice?

With Shadow AI, the risks of Shadow IT are exacerbated. Not only does Shadow AI carry the usual opportunities for security breaches, but the way AI is used often involves sharing private information, intellectual property, or exposing other sensitive data. That puts businesses unknowingly at risk for data exposure and violations of compliance standards, alongside the common issues of inaccurate or misleading information generated by AI tools when not wrapped in proper training and policy.

How often is Shadow AI happening? More than you might guess. According to the 2024 Work Trend Index Annual Report from Microsoft and LinkedIn, 52% of AI users at work say they're reluctant to admit they use AI on their most important tasks, reflecting an AI governance, accountability, and visibility gap for enterprise leaders trying to oversee AI adoption. Adding to that, the study also revealed that 78% use their own tools, especially at SMBs, increasing the odds that sensitive data enters uncontrolled systems.

The Risks and Costs of Shadow AI

Not all of the risks associated with Shadow AI are security-related. Unauthorized AI apps can also mean that employees are paying for duplicate tools or licenses, or are making decisions with inaccurate or misinterpreted data from unvetted AI apps. But AI security risk remains the top concern, as free AI apps, or those not evaluated for security and other enterprise functionality, can lack encryption, secure data storage, siloed data usage, and other security tools.

What does that mean? Those unverified apps can open your organization up to:

• Data leakage and confidentiality
  Risk: Employees copy customer data, contracts, or employee information into unapproved tools
• Accuracy and hallucinations in operational decisions
  Risk: Confident-sounding but incorrect instructions can lead your team down the path to bad decisions with inaccurate information, incorrect assumptions, and ill-informed advice.
• Compliance and audit gaps
  Risk: With no records of what was entered or produced, it is impossible to defend decisions or provide audit logs required by most compliance standards, leading to significant violations and fees.
• Vendor and cost surprises
  Risk: Without guidance and alignment across your business on AI tools, you will likely face product sprawl, duplicate licensing, and no way to track usage and return on investment.
• Transparency into opportunities
  Risk: When employees hide usage, it is nearly impossible to enforce (or coach on) best practices, which can lead to AI failures before the positive impact of artificial intelligence can be gauged.

It can be easy to say, much like many businesses did about social media usage back in the day: No one on our team is doing that. They are too smart! But we'd be willing to bet that at least one of these real-life scenarios has happened within your organization:

• HR drafts a termination letter using a public tool and pastes private performance details
• Finance asks a chatbot to "summarize our top customers by revenue" and uploads a spreadsheet
• Operations uses artificial intelligence to write vendor risk language, but can't defend it during an audit
• A manager uses artificial intelligence to produce performance feedback that's tone-deaf and escalates conflict

The question is: How do businesses staunch that flow of Shadow AI and prevent these risky behaviors?

Best Practices for Battling Shadow AI

Sure, your IT team or MSP can go all security audit secret police on AI in your environments, but the more productive approach enables your team to be part of the solution and positions AI as a positive element within your business for those who are willing to explore its potential.

Yes, there are security tools that audit for the use of apps on your network, but a more effective way is to ask your team. Explain that the organization is hoping to find the right AI tools, and is looking for current users, preferred tools, and examples of use cases to guide the process.

By informing your team about the risks while also encouraging participation in the design and implementation of AI, you will likely sniff out those users who are open to innovation as well as the tools hiding in the shadows. To start your official AI rollout, there are a few simple steps:

• Normalize "approved use" and gather input on tools in use now
• Identify super users and turn them into champions for approved tools
• Publish playbooks with clear use cases and train your team on the proper use of AI tools
• Draft approved tool, data classification, technical controls, and other related AI policies for clear guidance on its secure use
• Address AI compliance and audit needs by crafting detailed guidance on areas governed by those standards
• Remind employees about the risk of hallucinations and inaccurate data by stressing the need for human verification, trusted citations, and restricted use cases that are simply too risky.

Shadow AI is likely the outcome of creative employees looking to improve productivity, not an underhanded attempt to derail business operations. As a leader, one of your jobs is to provide the right tools for your team (even if you, personally, aren't convinced of the value). The bottom line is, if you don't offer the right tools for people to do their jobs, they will seek alternatives, and you end up with a risk management challenge. Instead, research and select AI tools properly to mitigate risk while also involving your team to make sure they offer the right functionality, and wrap them with the correct AI governance. Then, you have an effective AL risk management strategy, and your team gets powerful, emerging tech, and everyone sleeps better at night.

Have questions about properly vetting technology tools? Our team can help guide you through the process. Let's talk.