Four actions to regain control without slowing down the machine

Shadow AI is not resolved by prohibition. 

It is resolved by substitution: give your teams a secure path that is just as simple as the tool they are currently using.

1. Map out before deciding:

You probably do not know which AI tools are being used in your organization. If you have a proxy or a DLP solution, a two-week outbound traffic audit will give you a real picture. If this is not yet in place, a quick alternative exists: an anonymous survey of department managers, with three simple questions (which tools, for what uses, how often), takes 48 hours and covers 80% of the ground. 

It is not perfect, but it is enough to prioritize. Without this starting point, any policy is blind.

2. Establish a whitelist, not a blacklist:

Banning ChatGPT is useless if you do not offer an alternative. Define the authorized tools, document them, and communicate the reasons. A usage charter without a replacement tool will not last six months.

3. Address the problem at the source, in real time:

The real question is not "which tool is my employee using?" but "what data should they be allowed to send?". For this, there is a specific category of tools, anonymization proxies for LLMs, which sit between the user and the AI model. 

Before the prompt goes to ChatGPT or another service, the tool automatically detects and masks sensitive elements: names, contractual references, financial data, HR IDs. The AI receives stripped-down text. The employee gets their response. You keep control of what goes out, without having to block usage.

4. Do not tackle everything at once:

Identify the two or three services that concentrate the most sensitive data (HR, finance, R&D depending on your sector) and start there. A targeted six-week pilot is more effective and more acceptable than a poorly prepared global rollout. And it will give you the arguments to convince reluctant teams.

The organizational dimension matters as much as the technical one. IT and business managers must build this governance together. A policy imposed from above without dialogue with end users is bypassed in forty-eight hours.

What Bosl.ai concretely brings to this context

This is exactly the type of problem that akawan has addressed with Bosl.ai. 

The principle: an anonymization layer that sits between your employees and the LLMs they use, in real time, without changing their working habits.

Concretely, before the prompt goes to ChatGPT, Gemini or another model, Bosl.ai detects and masks sensitive data: names, financial data, HR information, project references. The AI receives a prompt stripped of identifying elements. 

The employee gets their response. You keep control of what goes out.

The solution is available in SaaS for rapid deployment, On-Premise for environments that require nothing to leave the perimeter, and as a Chrome plugin for teams working directly on ChatGPT. For organizations that want zero contact with external AIs, the Bosl.ai ECO option is based on a fully in-house sovereign model.

At akawan, we do not deliver the tool alone. We start by analyzing your actual traffic, identify the most exposed data, and co-build the deployment scope with your IT and business teams. This is what makes the difference between a tool that is installed and a system that lasts over time.

Growth will not slow down. Governance, however, can move quickly.

Shadow AI is not tomorrow's problem. It is active today, in your teams, on tools you have not yet mapped. The larger your organization grows, the wider the problem spreads.

The good news: you do not need to solve everything at once. A targeted scope, an effective layer of protection, and governance built with users rather than against them are enough to significantly reduce the risk. 

Your teams want to go fast. Give them a secure path to do so.