AI assistants can provide next-level value when it comes to productivity, time-savings, and enabling better decision making – there’s no doubt about that. But always-on and on-by-default AI tools have a dark side, too. The fact is, most companies’ data systems aren’t fully secured. So when they add AI assistants into the mix, it’s a recipe for potential disaster.
The danger isn’t the AI itself – it’s what happens when these tools operate on top of weak access controls. Give an AI assistant access to misconfigured systems, and it doesn’t take much – just one casual query – to surface data that was never meant to be seen. If left unchecked, AI assistants can accidentally leak anything from confidential employee information, to customer records, product roadmaps, and sensitive legal documents.
These aren’t edge cases – they’re inevitabilities. Nearly every organization is sitting on troves of sensitive data that are wide open to these tools. It’s not if something is going to leak. It’s when.
When implemented with intention and control, AI assistants can be an invaluable tool – but without the right guardrails in place, they quickly become liability magnets. Companies need to stop assuming AI is safe by default and start governing it with the full knowledge of relevant risks and controls.
Fix Your Permissions and Clean Your Data Before AI Finds the Flaws
AI assistants don’t create their own security problems – they inherit yours and put them on steroids. If your permissioning is a mess, AI will act accordingly. Most AI assistants operate within existing permission structures, and most of those structures are overly broad. One study found up to 92 percent of identities with access to sensitive permissions didn’t use them over a 90-day period. That’s not just inefficiency – it’s exposure waiting to happen.
Before rolling out AI assistants, companies need to do the foundational but often ignored work of auditing access. You can’t govern what you can’t see, so start with data discovery and classification. From there, map what matters to the teams and individuals who actually need it.
That access map becomes your gold standard for who needs what and who doesn’t. But don’t stop there. Automate the audit process. Manual reviews are a breeding ground for blind spots. Smart tooling can flag overprivileged access, surface dormant or orphaned accounts, and catch access creep early – before it becomes front-page fallout.
Data hygiene is also crucial. So in addition to reducing over-permissioned data, enterprises also need to get rid of the redundant and/or obsolete data lurking in their systems. Having high-quality, AI-ready data improves AI assistants’ performance andenhances security. Simply put, good data hygiene is a prerequisite for safe and effective generative AI use.
Notice and Consent as a Control Layer
Most AI assistants are embedded into the platforms employees use every day. Again, that means they will inherit the same data access that their users have. But here’s the problem – just because you can access something doesn’t mean you want your AI assistant to process it, summarize it, or pull it into a response.
Right now, most users lack clear options to consent to or restrict AI assistants’ access to specific data types. There’s no interface to review what the assistant can access. No toggle to restrict it from pulling data from your inbox, your word processing software, or that private folder in your cloud storage drive where Legal stores draft M&A docs. AI assistants operate silently, relying on permissions you may not even realize you’ve been granted.
If companies want to build trust and avoid unintended data leakage, they need to move beyond passive policies and start designing interactive consent. That means building a real-time disclosure layer directly into the UX. When someone first launches an AI assistant, they should see a clear message: “This assistant can access your files, email, customer data, internal documents, and chat history to generate responses. Would you like to restrict access to any of these data types?”
Give users the ability to opt out – by data type, by source, by sensitivity. Better yet, disable AI assistants by default in high-risk environments like Legal, HR, and Finance unless granular controls are in place.
Don’t Deploy the Tech if You Haven’t Trained the Team – AI Literacy Is Not Optional
When it comes to AI in the workplace, most companies have FOMO and are racing to deploy – but forgetting to train. And it’s a dangerous gap. Today’s AI assistants are powerful enough to expose sensitive data in seconds, and most employees have never been properly taught how to use them responsibly.
Regulators are catching on. Article 4 of the EU AI Act requires both providers and deployers of AI systems to take measures to ensure “a sufficient level of AI literacy” among staff. And it’s not just a European thing. It’s common sense. You wouldn’t hand someone a power tool without instructions. Why do it with generative AI?
AI education can’t be generic. It needs to be tailored by role, informed by risk tolerance, and reinforced over time. A salesperson using AI to summarize client calls needs different training than finance teams using it to parse confidential budgets. Legal teams need to know and decide when to step in – not after something has gone live, but before an assistant makes a bad call irreversible.
And let’s be clear – AI is not magic. It’s not perfectly neutral. And it’s not always right. Companies that overhype what it can do end up creating overreliance, and that’s when real mistakes happen. The only way to get value out of these tools without walking into a compliance nightmare is to ground every rollout in reality, clarity, and training. Want safe use of enterprise AI? Train the humans. Don’t launch the tool until you’ve prepared the people.
As AI assistants increasingly become a workplace staple, security and privacy need to be a top priority from the jump. Responsible AI isn’t just what you promise your customers. It’s what you promise your own company. This isn’t about saying no to AI, it’s about saying yes only when the right foundations are in place. The companies that get this right won’t just stay out of trouble – they’ll define what responsible AI looks like for everyone else.
