u/MaleficentCollege324

We're evaluating AI SOC platforms and one thing keeps coming up in our legal reviews: data privacy and compliance. These tools want to ingest massive amounts of telemetry, logs, and alerts, some of which contain sensitive data.
Our legal team is asking hard questions I don't have great answers for yet:
How do these platforms handle data residency requirements?
What happens to our data if we stop using the tool?
I've asked a few vendors directly and the answers have been vague. Lots of "we take security seriously" but not much substance on specific compliance frameworks like GDPR or SOC 2.
For those already running AI SOC tools: how did you get past legal and compliance reviews? What questions should I be asking that I'm not

We have been reviewing the tools our company uses and one goal is improving our internal documentation. We looked at some of them, but for our small team it feels like more than we need. Many of our workflows are straightforward and we would like something that is easy to set up and maintain.

Feels like the second i finish tuning an AI detection rule, the environment shifts again and I’m back to square one. new assets get spun up and cloud configs shift constantly. on top of that, whitelist changes rarely get looped through AI detection engineering before they land. by the time I’ve noticed it, i'm chasing drifts instead of building new coverage.
the worst part is that this is invisible work. Management sees "detections deployed" as a one-time task, not something that needs constant rework just to stay accurate.
This is driving me crazy as I'm spending more hours reacting to organizational changes than improving our AI detection and response surface, and it's starting to eat into the roadmap items I got hired to build in the first place. To make things worse, it’s hard to make the case for more headcounts when the work looks like maintenance from the outside.
i keep hearing about tools that supposedly update AI detections automatically based on what's happening in the environment, but I want to see them hold up in a real situation before i trust them. Have you ever used them? And if you did, what worked for you? I’m interested in partial fixes as well

Every time documentation comes up in a meeting, someone says they don't have time and I get it. Everyone's focused on shipping work, serving customers or hitting deadlines.

But then we spend hours every week answering the exact same questions because nothing's written down. Feels like we're saving time today just to lose it again next week.

How have you convinced your team that documentation is actually worth doing?

we are saas company in the low‑seven‑figure arr range with a mix of US and international customers on terms and ar now sits in the high six / low seven figures most months. I’m seeing more tools pitch “ai for arcollections” and I’m trying to figure out whether anyone here is running this in production not just testing it on a small sample.
by that I mean accounts receivable software that says it can rank which customers to contact first, draft collection emails, and predict payment dates for overdue invoices. If you ve rolled out anything like that, i’m interested in what changed in the numbers or in your team’s day to day work: did DSO or recovery rates move in a way you’d link to the tool, did collectors get back meaningful time or did it mostly add another thing you had to watch and correct? Stories where you tried…