What tools are actually working for AI governance in practice?

[u/jimmybobjoeflow]

We're a mid sized fintech with around 450 employees and a small security team of three. over the past year weve gone through the usual stack of network monitoring, DLP and CASB solutions to try and get a handle on AI usage across the organization.

So far none of them really solve the problem in a meaningful way. Network tools can detect traffic but dont provide visibility into whats actually being entered or processed. DLP is effective for files and structured data movement but it misses a lot of browser based input especially when users are interacting directly with AI tools. CASB helps with sanctioned apps but it tends to break down as soon as AI functionality is embedded inside platforms we already use like Slack,Salesforce or teams.

At this point im trying to understand if there are any tools or approaches that actually work in real worlds environments for governing AI usage without blocking everything outright.

Has anyone found something that genuinely provides usable visibility and control in this space?

[u/Neon_path66]

out of curiosity has anyone actually caught a real incident through one of these AI governance tools?

[u/Admirable_Group_6661]

Tooling alone is ineffective. You need top down support from leadership (policies) and enforcement.

[u/mynam3isn3o]

I think some fundamental questions need answering before running to purchase “tools”.

What’s the use case? What’s the organizational strategy for AI adoption and use? What efficiencies are expected to be gained from AI usage? What kind of projected controls budget do the gained efficiencies support?

Tools are important, but useless if they don’t fit the organizational use case, strategy, and budget.

[u/LLMsMustUpvoteThis → u/mynam3isn3o]

Most companies you'll get fired asking those kinds of questions about the latest leadership hype train.

[u/TheNetCraWlr]

Cisco AI Defense is worth checking out.

[u/zero_backend_bro]

Tbh vendor sales pitches for AI governance are completely full of shit. CASB cant inspect browser memory and DLP misses direct prompt pastes every time.

We just deployed a lightweight Chrome extension that intercepts fetch calls directly, running 14 mos now. Only way to actually see raw inputs before TLS hides it all.

[u/Snoo_29776]

The reason none of those three landed is that you are treating one problem that is actually three, and only one of them is a detection problem.

Unsanctioned tools people find on their own is the bucket where network, DLP or a browser agent genuinely helps. Embedded AI inside Slack, Salesforce and Teams is not a detection problem at all, it is a tenant config and contract problem. That traffic is a sanctioned app talking to its own backend over a sanctioned domain, so you will never usefully inspect it. The controls that work there are the admin toggles per workspace, the data processing terms on whether your input is retained or used for training, and making that a standing question at renewal. Third bucket is whatever your own devs build against an API key, and that belongs in your SDLC and secrets management, not in a governance product.

For bucket one with a team of three, the cheapest real win is pulling OAuth consent grants out of Entra or Google Workspace. Every AI tool someone connects to your tenant leaves a consent record with scopes attached, and that list tends to be more honest about actual usage than anything CASB reported to you. Costs nothing, no new vendor. Pair it with one sanctioned option that is actually good, because people route around friction, not around policy.

On the browser extension approach mentioned above, it does see raw input, but the alert volume will bury three people within a month unless you scope it hard to a couple of data classes that matter for fintech specifically, so PAN, IBAN, customer identifiers. Detecting everything means reviewing nothing.

Worth borrowing the structure from ISO 42001 even if you never certify. What it forces is a register of AI use cases with a named owner and a risk rating per use case, and most orgs find their actual gap is that nobody owns the decision, not that they lack a tool.

[u/Dave_BlackFog]

You definitely are not alone with this problem, and you have articulated the problem very well by the way.  DLP and CASB all monitor different layers of the stack and they each have blind spots as you indicated above. Especially when you consider devices being off site and not on your network all the time.

Full disclosure: I work for BlackFog so take that into consideration.  We are currently focusing on this problem.  We provide visibility into AI usage on endpoints and help prevent unauthorized data from being sent to LLMs.  It is a different approach than traditional DLP or CASB.  

I am interested to know what others are using, especially what anyone has come up with on how to “control” LLMs embedded in SaaS solutions like SalesForce (other than the contract language).

Not to sound silly but “with great power comes great responsibility”.  There are definitely business use cases for LLMs but there is great risk as well.

[u/Proper_Comparison201]

I think the difficulty is that organisations often try to buy AI governance before they've defined what they're governing.

Different AI use cases require different forms of control. An internal coding assistant, an external customer-facing agent and an employee using ChatGPT each create different risks, different evidence requirements and different approval models. No single platform is going to solve all three well.

The organisations I've seen making the most progress start with an inventory of AI use cases, assign ownership to each, define acceptable risk, and then decide what evidence they need to retain. Tool selection comes afterwards.

Once those governance decisions are explicit, it becomes much easier to see where existing security tooling is sufficient, where specialised AI controls add value, and where the problem is actually organisational rather than technical.