So you want to defend your enterprise against the liability of Shadow AI, but there’s a problem: you can’t manage what you can’t see. If you feel like you’re operating in the dark, you’re not alone. Many enterprise teams are unknowingly introducing unsanctioned AI tools into their workflows – with the worst part being that leaders are unaware that this is even happening (until it’s too late).
I’ve spent decades helping enterprises like yours navigate evolving digital threats. In every wave of change, one truth remains: early visibility separates the secure from the exposed. And when it comes to Shadow AI, what you don’t know can (and will) hurt you.
In this article, I’ll show you how to shine a light on Shadow AI in your organisation. You’ll learn how to spot the signs: where to look, what tools can help, and how to ask the right questions – because before governance comes discoverability.
The consequences of Shadow AI usually surface through things like internal data audits, which uncover unexplained tools or data flows; whistleblowers alerting management to unsafe AI practices; or even failures in operations that lead to a full-blown investigation. There are also cases where an AI vendor fails to disclose that they use their customers’ data to train their models – catching the security team by surprise.
Instead of waiting for disaster to strike and you’re left wondering, ‘How did this happen?’, the best approach is to be proactive. To get (and stay) ahead of the issue, I recommend starting with a structured discovery phase:
This is where getting to the source begins. Identify what AI tools and services employees have adopted (formally or informally). This can be done through surveys, interviews, or scanning enterprise networks for traffic to AI service domains. Modern security platforms can help too: for instance, some endpoint protection suites like CrowdStrike offer modules to detect unauthorised applications or unusual AI-related activities running on devices.
For each identified tool, investigate where it interacts with your data. What kind of data are employees inputting into these AI systems (i.e., customer data, code or strategic plans)? Understanding the nature of data exposure is key to gauging just how much risk you have. Also, find out where the AI tool stores data and if there’s any control or agreement in place (the answer will probably be no, given that it’s shadow usage – but it’s worth a triple check).
Determine which teams or individuals are using these tools and for what purposes. This helps in quantifying how widespread Shadow AI is in the organisation and also in assigning ownership of the risk. The latter point is not about placing blame, but as mentioned in my previous article, a dire consequence of Shadow AI is the lack of accountability it introduces, which makes it that much harder to identify.
Mapping usage also highlights any duplicated efforts. You might discover two departments unknowingly using similar AI tools in parallel, which is not only risky but inefficient. By documenting who is doing what with AI, you set the stage for accountable governance.
Beyond these discovery steps, there are practical signs and signals to look out for that could point out Shadow AI activity:
Monitor network logs for spikes of traffic to AI service endpoints (e.g., OpenAI, Anthropic, etc.), or repeated Application Programming Interfaces (API) calls to external AI platforms. In the same vein, if you see data being exported or copied out of internal systems at odd volumes or times, it might be feeding an AI tool.
Pay attention to work outputs that have unmistakably AI characteristics. For example, if you start seeing multiple employees use the same language when they write (which you can immediately tell is AI-generated text) – that’s a clue. Sudden jumps in productivity or quality of work are great, unless someone is using a tool that hasn’t been approved.
Sometimes, employees will mention in passing something like “I had help from a bot” or drop names like ChatGPT or Copilot in watercooler conversations. While sharing tips is an excellent way to create a learning culture, these are Shadow AI cues to listen out for. Previously, I mentioned how company culture plays a big part in the adoption of Shadow AI. If employees don’t feel safe enough to openly reflect on their AI journeys, they’ll simply use it in secret.
This isn’t about reprimanding anybody: it’s about asking direct questions that help you gain visibility across the inner workings of the organisation. Introduce regular security or IT check-ins where employees are asked questions like:
These discussions – to support and not punish – can reveal not just the existence of Shadow AI, but why it’s happening, which puts you firmly on the path of rectifying the issue.
By focusing on visibility, policy, and education (in that order), your enterprise can significantly mitigate the risks of Shadow AI while still harnessing the benefits of these tools. Without visibility, you can’t manage what you can’t see. Policy establishes guardrails and best practices – not just restrictions. Education is the best way to bring it all together by empowering users to use AI responsibly.But there are a few more proactive considerations worth noting:
Some organisations are implementing technical controls like AI gateways or sandboxes that route all AI queries through a controlled environment. These act as a broker between employees and external AI APIs, which allow the company to monitor content and strip out sensitive data. If resources allow, this can be a powerful way to let users access AI safely rather than blocking it outright.
Don’t forget that your software vendors and cloud providers have a role to play in managing AI risk. We are responsible for offering transparent data policies and admin controls, and supporting enterprise integration with compliance in mind.
When choosing AI tools or services to give the stamp of approval, take some time to vet your vendor’s posture:
Ultimately, forging a good partnership with your AI vendors means you can take full advantage of these solutions, knowing that they are both secure and compliant.
Finally, treat the mitigation of Shadow AI as an ongoing program, not a one-time project. The world of AI is evolving in front of our very eyes: what may be considered a “productivity powerhouse” today, might be a cyber security nightmare tomorrow – and new tools will continue to emerge.
Regularly revisit your AI usage policies, monitoring techniques, and training materials. Have open discussions where employees provide feedback on what’s working and what isn’t. By keeping your finger on the pulse, so to speak, you ensure that your organisation stays ahead of the curve, rather than constantly reacting to the next AI mishap.
With the right mix of technical tools, human insight, and open dialogue, you can uncover where Shadow AI lives in your organisation — and take back control before it takes you by surprise.
Shadow AI is a growing liability, not because employees have malicious intent, but because your business may not yet have the systems in place to guide safe and secure AI use. So, the first, and most critical, step is identifying the problem.
With extensive experience in navigating every major shift in the enterprise threat landscape over the past few decades, I can honestly say that Shadow AI is one of the most urgent challenges I’ve seen. I’m here to help you address it — starting with clarity, not complexity.
Start the discovery process today: audit your environment, speak to your teams, and review your software landscape with fresh eyes. If you're unsure where to begin, partner with a trusted advisor like Babble, who can help you assess your current exposure and build a roadmap for responsible AI governance.