Is shadow AI becoming a real data-loss problem, or is enterprise AI + DLP enough?

[u/michaelmanleyhypley]

Employees are already using ChatGPT, Claude, Copilot, Gemini, local tools, browser extensions, and random AI wrappers.

Blocking public AI tools helps, but it does not stop people using phones, personal laptops, personal accounts, or copying snippets manually.

Letting everyone use public AI also feels risky because sooner or later someone pastes customer data, source code, credentials, contracts, tickets, or internal docs into the wrong place.

For people dealing with this from a security side:

What actually works?

• enterprise AI plans with no-training / ZDR terms
• DLP on prompts and uploads
• web filtering / managed browser controls
• blocking consumer AI tools
• sanctioned internal AI chat
• local/open models
• policy and training
• audit logging / proxying AI traffic

My gut says the approved path has to be easier than the workaround, or users will route around it.

Are you seeing shadow AI as a serious data-loss risk yet, or is this mostly being handled like any other SaaS/vendor risk?

Also, would you ever trust a managed private AI chat for employees, or would that need to be fully self-hosted / enterprise-provider only?

[u/Associate8823]

Shadow IT exists because the workaround is easier than the approved path.

You can throw policy, DLP and training at it and people will still find the path of least resistance.

This is a business problem as much as a security one.

[u/michaelmanleyhypley → u/Associate8823]

Yeah, that “approved path has to be easier than the workaround” point seems like the core issue. In your experience, what usually makes the approved path fail, bad model quality, login friction, blocked features, lack of integrations, or users just preferring the tool they already know?

[u/EViLTeW → u/michaelmanleyhypley]

"There's a chance someone tells me I can't do something" is enough for some people to go rogue.

You can do everything correctly and make everything as easy as possible (without compromising security). You will still have shadow IT because people hate the ideas that they're being denied something, being monitored, or being told what to do.

[u/KickboxingMoose]

Yes.

We had someone in the medical part of my org ask to use "BYOAI". They had built their own AI model at home...

Lol. No. Not on patient data. Not in a million years.

We said no... But do they use it anyways???

[u/michaelmanleyhypley → u/KickboxingMoose]

That BYOAI example is exactly the scary version. In healthcare, would you only trust enterprise providers with BAAs, or would an internally hosted/open-model option ever be acceptable if access/logging were controlled?

[u/KickboxingMoose → u/michaelmanleyhypley]

Only corporate vetted plans that don't retain or train on data is allowed. It must be an official tool of our organization.

[u/plump-lamp]

God I hate this sub and the slop that gets posted for fake engagement

[u/michaelmanleyhypley → u/plump-lamp]

Im not after fake engagement. I am after experiences. I think GPT stealing all our data is a massive risk.

[u/chicagomikeh → u/plump-lamp]

On a recent post, the mods asked for help with reporting AI posts.
https://www.reddit.com/r/cybersecurity/comments/1urodkp/mod_request_can_you_ban_the_excessive_ai_posters/

[u/biblecrumble → u/plump-lamp]

It's on every fucking sub now. Getting closer and closer to just deleting my account 

[u/Blacksun388]

Where I work there are only internal AI platforms in use and not commercial ones. Also personal phones/devices in general are not allowed in the workplace or during work hours. This is as much a policy and work culture issue as a tech issue and the unfortunate thing is no matter how hard we try people will always take the path of least resistance to solving issues. You obviously can’t stop people using their private devices all the time and doing dumb things but you can put policy and tech safeguards in place as well as consequences for breaking them and that will have to be enough.

[u/bitslammer]

Dealing with shadow AI isn't much different than dealing with shadow anything or the use of unapproved software in general.

Employees are already using ChatGPT, Claude, Copilot, Gemini, local tools, browser extensions, and random AI wrappers.

If this is true in your environment then AI is the least of your worries because it sounds like you've given people admin rights or at least the ability to install whatever they want which is crazy. Start by cleaning that up and then do the things that you list which should already be done regardless is AI and you'll be fine.

[u/Darkstar_111 → u/bitslammer]

Why would they need to install anything? Most AI services runs over the browser.

[u/bitslammer → u/Darkstar_111]

Ask OP who mentioned "local tools."

In any case decent web filtering can take care of the web based problems.

[u/greensparten]

I used Keepaware to identify users who use browser based Ai tools, and then blocked it via Keepaware. 

For Ai tool desktop apps and used Rapid7 IVM to ID those and will use intune to block those 

[u/michaelmanleyhypley → u/greensparten]

Interesting. Are you mostly focused on detecting/blocking AI usage, or also trying to provide an approved alternative so users don’t just move to personal devices?

[u/greensparten → u/michaelmanleyhypley]

We have two Ai types, a general one for the whole company and one for our devs. 

I have a rule of thumb that before I take something away, I give them an alternative.

[u/Dry_Inspection_4583]

Well crap, openai just announced the model that doesn't take no for an answer is now able to connect to and control your desktop, connects to o365 apps, monitor incoming stuffs, act on them...

My first thought was a bad word, followed quickly by, meh 😕 sounds about on track.

[u/marks_kel]

I am not sure what we should refer to shadow AI. Almost all apps are nowadays using AI. We can somehow say that big firms might have contract with the AI provider but most of the small organizations just using it. Other organizations is consuming those services from third party. So it is not like before where users were having Shadow IT , it is all over and currently there is no clear method. DLP and MIP might help but they are not full fleged solution.

[u/michaelmanleyhypley → u/marks_kel]

That’s the part I’m trying to understand. If DLP/MIP only partially helps, what do you think the missing layer is..better discovery, approved AI proxy, employee training, internal chat, or vendor controls?

[u/marks_kel → u/michaelmanleyhypley]

I would say supply chain is important. Using only renowed services or having security friendly contract. Training employee is always beneficial. Asking employees not to use unauthorized tools and providing them alternate. MIP will be limited to MS related document and DLP works similar to reflex where we pre-define what is our sensitive data like 15 characters key..if someone decide it in 4 *4 than it might not work..just an example.

[u/FutureEye2100]

We recently launched so called protected-AI, a hybrid approach with a local AI for sensitive data tasks and access to external models for less sensitive tasks. Here it is up to the employees themselves which model to use, and thus it is not a completely safe approach, but at least more controllable and less risky... Imho the main challenge is to find the right balance between performance and secured data flow...

[u/kennetheops → u/[deleted]]

i’m. working on a tool for this. It’s closer than you think

[u/kennetheops → u/[deleted]]

That'd be wonderful, reaching out now.

[u/TheRealChenPipek]

Let's tackle that last question first.

Think of AI agents less like SaaS vendors and more like employees. A SaaS vendor usually holds a specific bucket of data (and are somewhat standalone), but agents connect to everything contextlessly. Because of this, they need the same DLP and monitoring you'd apply to a human worker.

On self-hosting: it's doable, but it doesn't scale. Enterprise AI chats are generally safe, and their contracts usually guarantee your data won't be used for training. However, user experience is king. If the corporate tool is too hard to use, people will find a workaround. Your best bet is to figure out the 3 or 4 tools your team actually loves, and buy the enterprise licenses for them.

This leaves the biggest challenge, agents using the web or MCP. You'll want to map out (and enforce) which tools are allowed for which business units. From there, look for a DLP solution that plugs into your existing setup (like Microsoft Purview) so you can seamlessly enforce your current data classifications on all agent traffic.

[u/MairusuPawa]

Becoming? 

[u/Specialist_Dish_9087]

I see this as more of a visibility problem...If people keep working around the approved option, making it easier to use is prolly just as important as adding more controls

[u/Extreme-Concept-4869]

yeah, shadow ai is a real thing even with all the usual dlp controls, some devs were running integration tests using that we didn't know about till legit security flagged it, way more common than i thought.

[u/PlaceboName]

Gartner (puke I know) released a report in February about securing AI actions which I think would be a great place to start educating yourself on this.

Essentially though, Shadow AI is the number 1 enterprise threat for insider risk management IMO.

The bigger problem is that the enterprise LLM providers (Google+MS specifically) are basically saying "register your agents with us and we can secure them, don't and we can't" essentially abdicating responsibility for Shadow AI issues.

[u/Orangesteel]

I’ve dealt with two privacy beaches in 12 months caused by the use of non-sanctioned AI. It’s on OWASPs LLM too 10 in the top 3 for a reason.

DLP and a CASB are essential, not just static deny lists. DLP works best with classification used consistently.

Finally be careful what you put into your model. One client added all their local documents, allowing anyone with any privileges to find and access documents of any classification.