u/Any_Side_4037

testing ZTNA for SaaS access and running into limits with encrypted traffic. once sessions are proxied over TLS, visibility drops to metadata. hard to tell what users are actually doing inside approved apps.

security wants auditability and control. privacy pushes back on full TLS inspection. enabling decryption adds latency and creates other concerns.

without decryption, most controls seem coarse. you see domains, sessions, maybe some risk signals. not much at the action level.

example problem is data leaving through approved apps. if someone pastes sensitive data into tools like ChatGPT, it’s hard to detect without inspecting content.

testing so far shows similar tradeoffs. policy enforcement works at a high level, but detailed visibility requires decryption.

for teams running this in production, what level of visibility do you actually rely on.. are you using full TLS inspection, partial, or none. how are you handling data exfiltration through approved SaaS??

looking for approaches that work without relying entirely on decrypting traffic

testing ZTNA for SaaS access and running into limits with encrypted traffic. once sessions are proxied over TLS, visibility drops to metadata. hard to tell what users are actually doing inside approved apps.

security wants auditability and control. privacy pushes back on full TLS inspection. enabling decryption adds latency and creates other concerns.

without decryption, most controls seem coarse. you see domains, sessions, maybe some risk signals. not much at the action level.

example problem is data leaving through approved apps. if someone pastes sensitive data into tools like ChatGPT, it’s hard to detect without inspecting content.

testing so far shows similar tradeoffs. policy enforcement works at a high level, but detailed visibility requires decryption.

for teams running this in production, what level of visibility do you actually rely on.. are you using full TLS inspection, partial, or none. how are you handling data exfiltration through approved SaaS??

looking for approaches that work without relying entirely on decrypting traffic

Running Spark jobs on Databricks with 50+ stages per pipeline. Debugging is still almost entirely manual. Spark UI and event logs help but when something breaks it means checking driver and executor logs to find what  happened.

Tried verbose logging, explained plans, Ganglia. Once jobs are chained it turns into moving between UIs and logs just to trace one issue. Around 10TB+ daily, mostly PySpark with Delta and a few custom UDFs.

Been looking at whether an agentic Spark copilot would change this. The pitch makes sense, something that reasons across stages and jobs instead of just surfacing metrics. But not sure if an agentic Spark copilot delivers on that in practice or if it's still mostly demos.

need opinions from people who've  used one, is it worth it or is manual debugging still faster?

Running Spark jobs on Databricks with 50+ stages per pipeline. Debugging is still almost entirely manual. Spark UI and event logs help but when something breaks it means checking driver and executor logs to find what  happened.

Tried verbose logging, explained plans, Ganglia. Once jobs are chained it turns into moving between UIs and logs just to trace one issue. Around 10TB+ daily, mostly PySpark with Delta and a few custom UDFs.

Been looking at whether an agentic Spark copilot would change this. The pitch makes sense, something that reasons across stages and jobs instead of just surfacing metrics. But not sure if an agentic Spark copilot delivers on that in practice or if it's still mostly demos.

need opinions from people who've  used one, is it worth it or is manual debugging still faster?

We blocked chatgpt and all the obvious ai domains at the proxy level months ago. logs look clean. except now im seeing our dlp alerts light up because finance dumped customer sheets into notion ai and sales is asking copilot in teams to summarize deal pipelines with pii.

These are approved saas apps. the traffic never hits our ai blocklist because its all notion.com and microsoft.com. completely invisible at network layer. tried casb rules but they only catch api calls not what happens inside the browser session when someone types sensitive stuff into an ai prompt box. dlp on file uploads doesnt help when its just pasted text.

Now compliance is asking why we have zero visibility into ai usage and i got nothing. anyone actually solved embedded ai in approved tools?