Shadow AI: The AI Risk Already Inside Most Businesses

15 May

Artificial intelligence has arrived in the workplace in much the same way as, well, much any other business technology seems to: quietly, quickly, and, more often than not, before formal policy has caught up.

One person might use AI to summarise meeting notes. Someone else uses it to sharpen the wording in a proposal. A team member pastes rough ideas into an AI tool to speed up the drafting of a client email. (In one slightly unique example we had earlier this year, someone sent their AI agent in to join our webinar, rather than attending themselves.)

All of that means that, before long, AI is being used across a business, but not necessarily in tools the company has approved, reviewed, or even knows about.

That’s what shadow AI is, and though it’s not an especially dramatic term, it is a useful one.

Fifosys has already been writing about how AI is becoming embedded in everyday business systems and workflows, and the same theme keeps emerging: the real challenge is no longer access to AI, but whether businesses have the visibility, governance, and data controls to use it properly.

What Is Shadow AI?

Shadow AI is the use of AI tools or AI features for work without proper oversight, approval, or governance.

In practice, it is the latest version of a very familiar problem. The National Cyber Security Centre defines shadow IT as unknown assets being used within an organisation for business purposes, and warns that staff often turn to these workarounds simply to get the job done when official processes are not meeting their needs. Shadow AI follows exactly the same pattern, just with generative AI tools, assistants, plug-ins, those meeting bots we just spoke about, and all those shiny and new AI features built into software people you’ve already been using for years.

Here’s the thing: shadow AI doesn’t really start with bad intent. Most of the time, it simply starts with convenience. Someone wants to work faster, get through their admin more quickly, or just wants help turning notes into something presentable before their next meeting. None of that is unusual. In fact, it is probably already happening in more places than most organisations realise.

Why Shadow AI Is Growing So Quickly

AI is easy to access, often low-friction to try, and very good at helping with the kind of work people tend to delay: writing, summarising, organising, analysing, and tidying up.

That is one reason adoption has moved so quickly. Another is that employees are not waiting for a formal strategy to catch up. Back in 2024, Microsoft’s Work Trend Index said workers are bringing their own AI to work, and Microsoft’s SMB-focused follow-up said this behaviour is even more common in small and medium-sized businesses. Now, just over two years since it was published, AI is more prevalent than we could’ve maybe even imagined back then.

For UK SMEs and mid-market organisations, that creates a fairly obvious gap. The business may still think AI is still in the “we should probably discuss this soon” or “it would be nice to have one day” phase, while employees are already using it to support live work. That gap between official position and day-to-day behaviour is where risk usually starts to build.

Shadow AI Is a Business Risk, Not Just an IT Issue

It’s easy to treat this as an IT problem because the tools are technical. Only, the impact is much broader, making it everyone’s issue.

If your staff are entering business information into unapproved AI tools, or free versions of things like ChatGPT/Claude, etc., the issue quickly moves into data handling, confidentiality, compliance, process control, and commercial risk. A draft proposal might contain client-sensitive details. A spreadsheet might include financial information. A meeting summary might contain HR issues, strategic plans, or supplier discussions. None of those things looks especially dramatic in isolation, but together they create exposure the business may not even be aware of.

Why’s that? Well, once these files leave your network and get uploaded to, for the story’s sake, ChatGPT, these files are then read, used and stored by OpenAI. In the small print, they state that they use your information - and chats - to train their models. So, what might have saved you five minutes on that confidential document has turned into something your competitors can read, reference and learn from.

Running parallel to that is also a prominent quality issue. Shadow AI raises security concerns beyond mere introduction, creating messy, inconsistent workflows. Different teams use different tools, and nobody agrees on what is approved, so outputs vary. People start trusting polished language more than checked facts. Before long, the business has accidental process sprawl with an AI label attached.

That’s why this isn’t just about whether AI is useful. It is. But rather, it’s whether the organisation still understands how work is being done.

The Problem With Blanket Bans

The instinctive response is often to do what? The logical thing is to say, “Fine, then we will just ban it.”

That usually sounds stronger than it is.

In practice, blanket bans tend to push usage out of sight rather than bring it under control. Staff still have those same deadlines and still want to take the same shortcuts - especially if it’s something mundane, repetitive or very manual - why would you want to go back to making tasks take hours rather than minutes?

But if there’s no approved route, many will simply look elsewhere.The NCSC makes a similar point in its shadow IT guidance: if people are resorting to insecure workarounds to do their jobs, it suggests that existing policy or tooling needs refinement.

That doesn’t mean businesses should take a relaxed approach, just a realistic one.

What UK SMEs Should Do About Shadow AI

The first step is not to panic. It’s purely around visibility.

Find out what is already happening. What tools are staff using? What types of tasks are they using them for? Is company, client, or personal data being entered? Are they using AI features within approved platforms, or are they using separate public tools on the side?

That gives you a starting point grounded in reality rather than assumption.

The second step is to clearly define the red lines. Most businesses do not need a 40-page AI policy to make progress. They need plain-English answers to a small number of practical questions:

What tools are approved?
What information must never be entered into public or unapproved AI tools?
What outputs need human review?
Who signs off on new tools or features?
What should someone do if they are unsure?

If people cannot understand the rules quickly, they are unlikely to follow them under pressure.

The third step is to give employees a workable alternative. If the only official guidance is “don’t use AI,” that, simply, isn’t much of an operating model. A better approach is to approve safer tools, set up an enterprise account to have everyone under the same umbrella, explain where they can and cannot be used, and show staff what sensible usage actually looks like in their role.

Good AI Governance Should Be Practical

This, really, is where a lot of organisations start to overcomplicate things.

Good governance doesn’t mean turning AI into a legal essay. Users just won’t read it, so it’s a waste of everyone’s time. Instead, it means making safe behaviour easier than unsafe behaviour. Approved tools. Clear guardrails. Human review where needed. A bit of training built around real scenarios rather than vague warnings.

That last part in particular should matter to you a lot. As we were saying, most people just don’t need a lecture on machine learning - some of them have been using these tools for years already. They do, however, need help spotting the moment when convenience starts to turn into exposure. They need to know what should never be pasted into a prompt, when an AI-generated answer should not be trusted on its own, and when a quick shortcut creates a larger problem later.

That’s a much more useful conversation to have than either blind enthusiasm or theatrical panic.

Shadow AI Is Really a Visibility Problem

Underneath all the noise, Shadow AI isn’t really a story about whether AI is good or bad. We all have firsthand experiences of how (and where) AI has helped us in our day-to-day lives (spoiler alert: it isn’t in silly caricatures or art), but in saving time and driving efficiencies.

Instead, it’s a story about governance lagging behind behaviour.

Businesses have dealt with this pattern before - although arguably the only time we faced something this intense was when the internet became mainstream. As new tools appear, staff find ways to work around friction, and leadership assumes things are happening one way. The reality turns out to be something else. AI hasn’t invented that problem, and it won’t be the last time we have this conversation. But it has simply made it faster, easier, and more conversational.

The organisations that handle this well won’t be the ones that pretend nobody is using AI, but they will be the ones that make it easier to use AI safely than to use it badly.

That is usually what good IT and security leadership looks like in practice. Not denying reality, but properly catching up with it.

What is shadow AI?

Shadow AI is when employees use AI tools or AI features for work without formal approval, oversight, or governance from the business. In simple terms, it is the AI version of shadow IT.

Why is shadow AI a risk for businesses?

The main risk is loss of visibility and control. Staff may enter sensitive business, client, or personal data into tools the organisation has not assessed, or rely on AI-generated outputs that still need proper human review.

Is shadow AI only a problem for large enterprises?

No. It is just as relevant for SMEs and mid-market organisations, especially where teams are under pressure to work quickly and there is no clear approved route for using AI tools safely.

Should businesses ban AI tools outright?

Usually not. Blanket bans often push AI use further out of sight rather than bringing it under control. A more practical approach is to create visibility, approve safer tools, define clear rules, and give staff workable guidance.

AIBusiness Processes

Jordan Stewart