u/WolfShoddy7443

so ibeen digging into our identity inventory and i found that the fastest growing category by far isn't employees or even traditional service accounts... wow it's ai agents. we've got agents pulling data from internal apis, kicking off workflows, and writing to systems, and each one needs its own credentials to do that. some of them got provisioned quickly to hit a deadline and ended up with broader access than anyone intended.

also meanwhile every identity conversation still starts and ends with locking down employee logins. then the agentic identity barely comes up in the same breath even though these agents can take actions at machine speed with permissions nobody ever reviewed.

just wanna know cuz im not sure that...is this the bigger exposure right now, or am i overindexing because it's the thing i'm staring at

how other security teams are prioritizing agentic identity relative to the usual employee-focused identity work.

We scan images in CI and gate on criticals like everyone. An image passed clean, we shipped it. About 3 weeks later that same image, not rebuilt, not touched by anyone, gets flagged with a critical in a library buried in the base. First thought was scanner glitch. It wasnt.

The vulnerable code had been in there the whole time. It just was not in the CVE feed on the day we scanned, the disclosure landed later. Which means the clean scan was never telling me the image was safe. It was telling me what was publicly known that afternoon. I had been treating those as the same thing for years.

And version tag also just tells you the app version and nothing about when the image was last rebuilt. A tag that hasn't moved in eight months can be sitting on top of packages with a dozen upstream fixes and it looks identical to a fresh one in your registry.

Besides faster rebuild cadence and pulling bases that track upstream closely which seems to be the direction, am not sure of whats the proper fix here. Mostly I want to know whether the people who live in this treat a clean scan as anything more than a snapshot, because I gave mine way more authority than it deserved.

We've been tracking the AppGen/"vibe coding" explosion for a while now. Our teams have found Replit, Lovable, and Vercel apps all over our cloud estate that nobody in security knew existed. The scary part? Most of them are wired directly to live databases with no auth, no RLS, and exposed API keys sitting in client-side code.

We looked at Wiz's recent Lovable integration (GA May 2026) and it scans apps you deliberately connect. But that's the problem: the apps that actually worry me are the ones nobody connected to Wiz because nobody even knew they existed.

The research bears this out: RedAccess found ~380,000 publicly accessible vibe-coded assets, with ~5,000 leaking sensitive corporate data. Those are exactly the apps Wiz's opt-in model would miss.

Orca seems to approach this differently, agentless discovery across the whole cloud estate, finding apps regardless of platform or whether anyone connected them. But what others are actually seeing in practice.

Has anyone run both side-by-side? or one

Getting the same request from three clients now: "we want to let people use AI, make it safe." Sizes range from 40 seats to about 600.

Trying to standardize on one platform rather than doing something different per client, so my criteria are probably a bit different from an in-house team's:

Real multi-tenancy, not one portal per client that I have to log into separately

Sane licensing at 40 seats: a lot of the enterprise SASE players get unaffordable fast at the low end

Policy templates I can build once and push across the whole book

Reporting a non-technical client contact can actually read, because they will ask

API/PSA integration so alerts do not just live in another dashboard nobody checks

The AI-specific piece I care about most is discovery. Clients genuinely do not know what their staff are using, and a discovery report is the easiest way to turn a vague "make AI safe" ask into a scoped project.

What is everyone standardized on? And more importantly, anyone regret their choice after onboarding client 10 or 15? The pain seems to show up at scale, not during the first deployment.

Traditional DLP was built around known data patterns (SSNs, credit cards, etc.) moving through known channels (email, USB, cloud storage). Generative AI breaks that model a bit, people paste source code, customer data, or strategy docs into a chat window, and it's not always caught by pattern-matching.

Has anyone actually tested DLP tools built specifically for AI interactions? Wondering whether they're catching real incidents or just generating noise, and whether this is more of a policy/training problem than a technology problem at this point.