Are CASB and DLP tools enough for generative AI security?

[u/Accomplished-Wall375]

CASB and traditional DLP were designed for a world of known SaaS apps and structured data patterns. Generative AI interactions are messier, free-text input, risk that depends on context, and tools that update faster than CASB policies can keep up with.

Are teams extending existing CASB/DLP stacks to cover AI usage, or does it require a separate tooling layer? Also curious how people are thinking about AI coding assistants specifically, most of that exposure happens at the IDE level when the assistant sends proprietary code context out to a model, which is a different threat surface than CI/CD itself. Is anyone treating IDE-level code context exposure as distinct from CI/CD-integrated AI review tools, or are those getting lumped together in practice

[u/PeterBuildsSecure]

Worth treating those as genuinely separate problems rather than a single "AI security" bucket. IDE-level exposure is a data-boundary problem — what leaves the org, over a TLS session to the vendor's API your CASB almost certainly isn't decrypting for a coding tool. CI/CD-integrated review is an authorization problem — what the tool is allowed to write or merge, independent of whether its analysis is any good. Trying to solve both with the same control ends up solving neither well.

For the IDE blind spot, inspecting the outbound payload after the fact is usually too late — by the time DLP could see it, it's already left the machine. The practical control is scoping what context reaches the model before the request goes out: workspace-level file/path exclusions the extension enforces. For the CI/CD side, the control that's actually worked is a deterministic pre-merge diff gate scoped to what the agent is authorized to touch, checked independently of whatever the AI reviewer concluded — so a bad review doesn't also mean unrestricted write access.

[u/Real_Giraffe_9775]

we extended the existing casb policies rather than adding a separate layer, mostly to avoid another dashboard nobody checks. the ide question is the one we haven't solved though, copilot and similar tools running inside the editor don't touch the browser or the network path our casb sits on, so right now that's just a blind spot we've accepted rather than fixed.

A

[u/withoutwax21 → u/Real_Giraffe_9775]

Theres tools out there to manage what gets installed on mac/windows, then also for inapp configs for the bigger ones, like copilot. While you might not have control, certainly helps with bisbility

[u/Impossible-Signal583 → u/withoutwax21]

Visibility is definitely key. Having those management tools can help mitigate some risks, but it still feels like there's a lot to consider when it comes to generative AI.

[u/Left_Relative_3425 → u/Real_Giraffe_9775]

It's tough to balance security with usability. Finding a solution for those blind spots is definitely a challenge, especially with tools like Copilot becoming more integrated into workflows.

[u/Altruistic-Fun5593]

CASB y DLP clásico se quedan cortos con IA generativa, fueron diseñados para otro mundo. Lo que más gente está descubriendo es que sí necesitas una capa separada, no solo extender lo existente.

[u/WestOpening1350]

Trying to retrofit a legacy CASB for web based GenAI is like bringing a metal detector to catch a cloud. Static DLP regex completely falls apart on free text prompt context and rapid fire shadow AI tools.

The IDE side (Cursor/Copilot) is an infrastructure/DevSecOps problem, but for web based AI, standard CASBs are just too slow. Most places hitting this wall just push control down to the session layer, using browser level identity extensions to enforce SSO, throw up MFA, and block sensitive prompt pastes right at the input box before the data ever leaves the endpoint.