Stop hunting for Shadow AI and start listening to it

For years, enterprises have used a tried-and-tested response to unsanctioned technology: find it, block it and remind employees of the policy.

 

The block button worked for the most part, but now that approach is now colliding head-on with generative AI.

 

Employees are experimenting with AI tools at a pace that most organisations cannot match through procurement and security reviews. A new model can become a preferred tool for coding and research before an IT team has even finished evaluating the last one.

 

The common response is to describe this process as “shadow AI” and treat it as a compliance problem. Yet this diagnosis hugely misses the mark.

 

As employees continue to circumvent approved technology, they are also providing valuable business intelligence. Shadow AI tells you where employees think the approved technology is too slow, restrictive, or simply not good enough.

 

If organisations see shadow AI purely as bad behaviour, the answer will always be another policy or another block. However, if they see it as evidence of how work is getting done, they can start addressing the cause, rather than chasing the symptom.

 

Friction will always find another bypass

Most employees who use unsanctioned AI are not trying to undermine corporate security. They’re just trying to complete a task.

 

A developer wants help debugging code. A salesperson would like a summary of a long document prior to a call. The marketing team needs to turn rough notes into a roadmap.

 

If the fastest tool for doing the job through the approved route isn’t up to par, employees will find another way.

 

This pattern is not new. Employees adopted consumer file-sharing services because emailing attachments was painful. They brought their own devices to work because corporate hardware couldn’t match their experience at home. Teams even bought SaaS products on company credit cards because the IT team couldn’t provide a solution fast enough.

 

Generative AI accelerates this dynamic dramatically. There is often nothing to install, no time-consuming onboarding process and no new software footprint. Often, a user can simply open a browser tab and create a personal account to commence work within minutes.

 

An organisation can technically still follow the old approach and block AI, but the result will likely be that AI usage just moves somewhere security teams have less visibility.

 

This is why adding friction can sometimes increase risk rather than reduce it.

 

When an approved route isn’t streamlined, employees do not necessarily abandon the outcome they are trying to achieve. They abandon the route.

 

Shadow AI is free internal market research

Another way to look at this behaviour is by imagining whether every unsanctioned AI interaction can be treated as employee feedback.

 

Which tools do people choose when they have a choice? Which functions are valuable enough that employees will access them externally to the corporate environment? Which departments seem to be adopting tools the fastest?

 

These patterns can tell CIOs the answers they need much more accurately than an annual technology survey employees are obliged to take, based on the standard approach to deploying software.

 

Traditional software procurement works top-down. A business selects a platform, deploys it and measures adoption. AI works in the opposite direction: employees experiment first, discover what’s improving their work and then create pressure for the organisation to close the gap.

 

This makes visibility a strategic asset, not just a security requirement.

 

If hundreds of employees are pulled towards one AI tool, the first question shouldn’t be, ‘How do we stop this?’ It should be, ‘What are our people getting from this that we have not provided?’

 

That question turns shadow AI from a stand-off between security teams and employees into a source of intelligence.

 

Committing to this process can reveal gaps in sanctioned technology, identify opportunities for automation and help organisations see where is generating the most value before committing to large-scale systems

 

Governing the interaction, not just the tool

Of course, recognising the productivity of unsanctioned AI tools doesn’t automatically remove the risk.

 

There are several potential issues, including the fact that employees can still copy and paste company data, financial information and intellectual property into AI systems without understanding how this information will be handled.

 

Yet controlling risk doesn’t mean denying access. The more useful information is where this control should sit.

 

For organisations, this means looking beyond whether an AI tool is sanctioned and considering how employees are actually interacting with it.

 

Does control apply before data leaves, or only after it’s already been sent? If so, does it cover every entry point, including the browser, extensions, the desktop, and the network, or just one of them?

 

These distinctions matter because an AI prompt is different from a conventional file transfer. Much of the interaction takes place within the browser or desktop app, while network and endpoint controls may have more visibility of the device or connection than of what the employee types.

 

The test is not simply how much an organisation can restrict. It’s whether those controls enable employees to use the AI they want while protecting the business at the same time.

 

Organisations therefore need controls which operate as closely to the point of interaction as possible. This may mean prohibiting certain data from being put into a prompt, restricting sensitive information and considering access case-by-case depending on whether someone is signed into an approved corporate account.

 

When carried out correctly, this enablement can sit inside the flow of work rather than acting as another barrier employees need to reroute around. The productive method becomes the governed method, as guardrails act before sensitive information leaves the organisation rather than relying on employees to remember yet another layer of policy.

 

Models that employees will favour today may not be the same as in six months’ time. However, governing the interaction between data, browsers and endpoints is much more sustainable than predicting which individual services will matter most next.

 

The best security control is the one employees choose

The challenge for CISOs is partly technical and partly behavioural.

 

Security teams have been measured in the past on how effectively they restrict risky behaviours. Yet in the era of AI, they may also need to ask whether their controls make the risk-free option attractive enough that employees would prefer to use it.

 

That changes the relationship between security and the workforce.

 

Instead of asking employees to choose between productivity and compliance, enterprises can create an environment in which the productive choice is also the secure one.

 

Shadow AI will not disappear because another paragraph is added to the acceptable-use policy. Nor should its existence automatically be interpreted as employees refusing to follow the rules.

 

Sometimes, shadow AI is the workforce telling the organisation something it needs to hear. Companies listening may find that their biggest AI governance problem is also one of their most valuable sources of insight.

 

---

 

Matt Smith is Field CTO at Island

 

Main image courtesy of iStockPhoto.com and fstop123