Balance AI security and productivity without pushing risk into the shadows

Most AI security conversations start with attackers. That is only half the picture.

In SoSafe Live | In Action – Securing The AI Workplace webinar, the audience poll pointed to a more immediate concern for many teams: enablement and shadow AI. That says a lot about where security leaders are feeling pressure. Employees are already using AI to move faster. The question is whether the organisation can make that use safe before it disappears into private accounts and unmanaged tools.

SoSafe’s Adaptive Defence Playbook found that 97% of security leaders see AI as the main driver behind the recent shift in threat sophistication. Attackers are scaling from the outside. At the same time, employees are adopting AI from the inside to summarise notes, clean up documents, draft messages, debug code and make routine work easier. 

In the same survey, 40% of security professionals said they had identified shadow or unsafe AI use in their organisations, including unapproved tools or sensitive data being entered into them.

That is the dual frontline: one side is hostile, the other is well-intentioned. Both can create risk.

Shadow AI usually starts with a normal task

Shadow AI often starts when employees use public tools to make everyday work easier, such as summarising internal notes, improving a draft or making sense of a long document. The risk grows when those small shortcuts involve sensitive context that should stay inside approved systems.

In the webinar, Harry Jeyarajah, Head of Solution Engineering at SoSafe, highlighted a common visibility gap: much of this activity can happen through personal or unmanaged accounts. Security teams may never see the prompt, the uploaded file or how the output is reused later.

Blocking AI entirely may feel safer, but it can push the behaviour further out of sight. For competitive teams, the tools are useful. People will keep looking for faster ways to work. A hard “no” can turn a visible adoption problem into an invisible data problem.

The risk is bigger than confidential data

Sensitive data exposure is the obvious concern. It is not the only one.

Harry also called out risks that traditional security awareness training was not designed to cover: hallucinations, prompt injection and public models being trained on proprietary intellectual property.

That changes what employees need from security awareness training. They do not only need to know which tools are approved. They need to know how AI can be wrong, how outputs should be checked and where company context should never be entered.

A policy can tell someone not to paste confidential information into an unapproved tool. It may not help them decide whether meeting notes, source code, internal strategy or customer context count as sensitive in the moment. Those are workflow questions, and they need practical examples.

Fear makes shadow AI harder to see

AI governance also needs to account for employee confidence. Some people worry about using the wrong tool, exposing data by accident or falling behind colleagues who already use AI regularly. Others may avoid AI completely, while some use it quietly because it helps them work faster.

Fear-led communication does not solve that. If all employees hear is risk and restriction, they may stop asking questions. That leaves security teams with less visibility, not more.

A better approach gives people safe ways to experiment. Share examples of approved use. Show what a good prompt looks like. Explain why certain data must stay out. Give employees a place to ask when the answer is not obvious.

That is how shadow AI starts coming into the open. Not because people are forced to comply, but because the safer path is easier to follow.

Safe AI enablement needs to fit the workforce

AI guidance also needs to fit the way people actually work. A long, technical session will not help a mixed workforce build safer habits, especially when AI use now appears across everyday tasks, not only technical roles.

Start with the behaviours that create the most exposure, then build guidance around the moments where employees are most likely to use AI without thinking twice.

Balance AI security with everyday productivity

Security leaders need to make safe use easier than hidden use.

That means giving employees approved tools, practical rules and short learning moments they can actually use during work. It also means treating shadow AI as a signal. When people reach for unapproved tools, they are usually showing where the organisation has not yet provided a workable route.

The dual frontline is easier to manage when security and productivity are designed together. Employees get the confidence to use AI responsibly. Security teams get fewer blind spots. The organisation keeps the value of AI without leaving people to guess where the boundaries are.

Help employees use AI confidently and safely across everyday work.