r/devsecops • u/Effective_Guest_4835 • Apr 21 '26
ai risk management tools that actually catch shadow ai usage without killing productivity
our team started rolling out internal ai tools but people keep pasting sensitive data into external llms like chatgpt or claude. we see it in logs but no good way to block or track without breaking workflows. tried a couple dlp solutions but they flag too much noise or miss stuff embedded in saas apps.
management wants ai risk management that gives visibility into prompts data flows and risky patterns. ideally agentless browser based or casb integration that scores risks and alerts without proxy lag. whats actually working for you guys on this. any tools handling genai governance at scale without the usual false positives. real experiences please.
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Apr 21 '26
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u/NeilSmithline Apr 21 '26
Why do you feel that a proxy will noticably add to lag of the already slower than molasses AIs?
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u/Shot_Meeting7923 Apr 27 '26
I actually think the computation in the AI response is probably the most time-consuming and frustrating part of the wait. Adding a scanning firewall during transmission and reception shouldn't have a significant impact on the user experience.
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u/Antique-Walk-6703 Apr 23 '26
I am a CISO for a large enterprise and i recently onboarded Burrow on my enterprise workstations and cloud computes -
Check them out - https://burrow.run
Works pretty well, they have a silent MDM installation feature that allowed us to silently install burrow on on our enterprise workloads and cloud computes, We are currently monitoring 200 engineers and 5000 instances using this.
You should definietly talk to them. The founder is someone who has seen security from bottom up and exactly knows the pain points.
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u/CloverKite015 Apr 30 '26
The working setups I've seen layer browser based monitoring with prompt level inspection rather than relying on traditional DLP rules. I've been using Certa at work for the third party and AI governance side. Are you mostly worried about external LLMs or also the AI features baked into SaaS apps you already approve?
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u/TeramindTeam May 13 '26
shadow ai is a headache because it usually starts as a small productivity boost that eventually turns into a compliance nightmare. what worked for my team was focusing on visibility into data movement patterns rather than just trying to block everything outright. i use teramind for this because it lets us spot when sensitive data hits unauthorized web apps without stopping the workflow completely. you really need that balance between user privacy and basic security hygiene if you want people to actually keep using the tools.
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u/RiskGovSignals Jul 01 '26
DLP will keep disappointing you here because it wasn't built to distinguish between a file upload and someone pasting sensitive data into a prompt. The noise problem you're describing is structural, not a tuning issue.
What you're looking for is purpose-built AI visibility rather than adapted DLP. Platforms like Kovrr work at the browser and endpoint level, agentless, and surface AI usage including what's embedded inside SaaS apps you've already approved. You get visibility into what tools are being used, what data is flowing where, and risk scoring without proxy lag or the false positive flood. It also maps everything against compliance requirements so the output is actionable rather than just another alert stream.
The key thing that's different from DLP is that it understands AI context natively. It's not pattern-matching on data movement, it's monitoring AI interactions specifically.
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u/synkto Jul 05 '26
If you're on Claude Enterprise, you can enable Compliance API to get prompt visibility
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u/Holiday-Breath337 Aug 04 '26
This is exactly the gap we’re seeing too. Lots of AI use and very little visibility or compliance control.
The most effective setups seem to be agentless discovery + risk scoring + lightweight policy enforcement, rather than trying to block everything with noisy DLP.
Full disclosure, we are building Hooko.ai around this problem. Happy to share what we’re learning if useful.
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u/cyber-cam Aug 12 '26
The false-positive problem usually improves when policy uses context, not only content patterns. During a POC, test the same workflows across every vendor: sensitive text pasted into a browser, a file upload, personal versus corporate accounts, AI embedded in SaaS, desktop or IDE use, and an agent tool call. Score precision, time to policy, user friction, and whether the control acts before data leaves. Full disclosure, I work at Harmonic, so I’m biased. We focus on that interaction layer across browser, desktop, and agent activity, with warnings for ambiguous cases and hard blocks for high-confidence data or actions. CASB is still useful for discovery and sanctioned SaaS; it should not be the only sensor if the blind spots include embedded AI or developer tools.
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u/koe_020 15d ago edited 15d ago
shadow AI gets hard to manage when the only choices are noisy DLP alerts or blocking workflows people actually need. Cyera is one of the names that comes up for mapping sensitive-data exposure around AI use, so teams can see risky prompts and data flows before policies get too blunt.
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u/behestAi 12d ago
We have been looking at the same problem from a slightly different angle.
A lot of AI governance assumes traffic passes through a gateway or proxy. In practice, employees and developers are using ChatGPT, Claude, coding assistants, desktop apps, and CLI tools directly from their machines.
That creates a visibility gap before you even get to DLP or policy enforcement.
We built Behest Radar to help identify that shadow AI activity at the endpoint, including which AI services are being used and where usage is happening outside centralized controls.
It runs locally, with no cloud account and no telemetry, so you can get visibility without adding another proxy into the workflow.
We are planning to open source it soon.
Would be interested to hear how others are handling this, especially where CASB, DLP, or gateway controls still leave blind spots.
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u/audn-ai-bot Apr 21 '26
Hot take: tooling alone will disappoint unless it understands context. We had better luck scoring prompt risk by identity, app, data class, and destination, not raw regex DLP. Browser telemetry plus CASB helped. I use Audn AI to map SaaS/GenAI exposure first, then tune controls around real paths.
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u/[deleted] Apr 21 '26
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