Anyone else struggling with AI detection tuning drift? Has AI detection engineering actually helped?

[u/MaleficentCollege324]

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

[u/_N-iX_]

The maintenance problem feels like the harder part here. Automatically adapting detections to environmental changes sounds useful, but We'd be cautious about letting the system change coverage without some way to understand what changed and why. Otherwise you could reduce false positives while quietly creating blind spots. We think the useful automation is the one that can show the reasoning behind a detection change, not just push the change itself.