What is the best way to handle false positives from the AI without turning off the learning?

I'm a principal SRE at a Series C SaaS company (~300 employees, fintech adjacent) running a hybrid cloud environment, and I've overseen the deployment and tuning of an AI SOC tool (Vectra AI) across our AWS and on-prem workloads for about 18 months.

The core issue you're hitting is model calibration, not just feedback. Here's a breakdown of the operational criteria for managing false positives in a learning system.

1. **Feedback Granularity:** The tool must let you classify *why* something is a false positive. Vectra, for example, has a "Teach Mode" where you select from specific reasons like "Approved Admin Activity" or "Authorized Service." This contextual feedback is used to retrain specific sub-models, not just lower a global sensitivity score. A system where you only have a "False Positive" button will inevitably degrade detection.
2. **Confidence Threshold Tuning:** You will need to adjust confidence score thresholds per detection category. In our initial rollout, the "Suspicious Remote Execution" model had a 78% false positive rate. We didn't turn it off; we raised the confidence threshold for alerting from 70 to 85, which dropped the FP rate to ~15% without missing a true positive (we validated against past incidents). This is a manual, iterative process for the first 90 days.
3. **Suppression Workflow vs. Rule Creation:** Distinguish between a temporary suppression (24-48 hours for a dev's unusual deployment) and a permanent exception. A good workflow lets you suppress an alert *pattern* for a specific host or user for a short, fixed period. This prevents alert fatigue during planned activities without creating a permanent rule that could be exploited. Our tool logs all suppressions as security events themselves for audit.
4. **Baseline Establishment Period:** The learning phase is critical. You must allow a minimum of 30 days of full logging with *minimal* suppression before expecting stable behavior. During this period, expect a high FP rate (we saw 40-60% across all models). The cost isn't just in licenses; it's in senior analyst time to review and provide clean feedback. Budget 10-15 hours/week for this during the ramp.

Given your scenario, I'd recommend a tool with strong contextual feedback and time-bound suppression. For a team new to AI SOC, start with a vendor that offers a dedicated onboarding engineer for at least 90 days to help tune those thresholds. To choose between specific platforms, tell us your average log volume per day and whether your compliance needs require all feedback data to remain on-prem.

You're not wrong about granularity. But my experience is the "Teach Mode" type feature is only as good as the underlying model's segmentation. If it lumps a bunch of unrelated detections under one "reason" bucket, your "contextual" feedback is still poisoning the well for everything in that category.

And raising the confidence threshold from 70 to 85 just moves the problem. Now you're missing the low-confidence real threats instead of filtering the high-confidence false alarms. The outcome is the same: noise reduction at the cost of signal. You're just tuning for your own alert fatigue tolerance, not for actual security.

You've hit on the core tension perfectly. The fear that "marking false positives makes the model less sensitive" is valid for basic systems, but a mature AI SOC tool should separate *feedback* from *sensitivity tuning*.

Your dev's unusual deployment pattern is the textbook case. The practical step is to look for a "contextual feedback" or "approved activity" function. Instead of just marking it false, you should be able to tag it as something like "Authorized User Pattern" or "Business-As-Usual for this service account." That tells the model this specific behavior is sanctioned for this entity, without broadly lowering the threat score for similar behavior from an unknown source.

If your tool only has a generic "false positive" button, then yes, you risk dulling the edge. In that case, your only real playbook is to use the confidence threshold as a temporary filter while you push the vendor for a better feedback mechanism. It's a workaround, not a solution.

You're asking exactly the right question. Your concern about marking false positives dulling sensitivity is valid for poorly designed feedback loops. A proper system won't just adjust a global score; it should enrich its contextual model.

The key isn't just granular feedback, but temporal segmentation. Your example of a dev's unusual deployment pattern should be teachable as "approved behavior for *this* principal, from *this* source IP, during *these* hours." A robust model can learn that pattern is sanctioned for that specific identity, while maintaining high sensitivity for the same actions from a compromised account or at 3 AM. You're adding context, not lowering a threat score.

Without that feature, you're stuck tuning thresholds, which is indeed a trade-off. My benchmark data shows that teams without contextual feedback typically see a 40% reduction in alert volume after six months, but also a measurable increase in mean time to detect for real incidents because the model has been blunted. Insist on a tool that lets you annotate *why* something is safe.

Spot on about the vendor push. I had to do exactly that with our last tool. The contextual feedback existed in their marketing slides but not in the UI. We kept a shared doc linking each generic "false positive" click to the specific context it needed, like "service account X doing deployment Y," and used that as ammunition in every support ticket.

It took months, but they finally added a proper tagging system. The temporary threshold tweaks were a nightmare to audit later.

That's a really good idea, keeping a shared doc as proof. It sounds tedious but turning feedback into a paper trail for the vendor is smart.

I'm curious though, did you ever get the sense they just built the feature to close your ticket, and not because it was a real priority? I've seen some vendors do the bare minimum and then it barely works.

That initial worry about the model getting less sensitive is super common. The whole "feedback vs tuning" point others made is key.

Your dev deployment example is perfect. The ideal workflow lets you add context like "allowed pattern for this service account" rather than just lowering a score. If your tool only has a generic false positive button, you're basically stuck. Then you have to choose between noisy thresholds or blind spots, which isn't a real solution.

Have you checked if your SOC tool has any kind of entity-based allow-listing or feedback tagging? That's usually the first place to look.

> The ideal workflow lets you add context

Sure, in an ideal world. But the real question is, what's the operational cost of maintaining that contextual map? Every "allowed pattern for this service account" is a rule you'll have to audit quarterly, or it becomes legacy permission bloat. Most teams I see just end up with a messy pile of one-off exceptions that no one understands anymore.

Your point about a generic button is correct, though. If that's all you've got, you're not tuning a model, you're just breaking it.

You're asking exactly the right question from the start, and that dev deployment example is perfect. That initial worry is common, and it highlights whether your tool's feedback system is designed well.

The practical answer lies in whether you can add specific context to your feedback. If all you have is a generic "false positive" button, you're basically training the model to ignore that activity entirely, which is risky. A better system will let you tag that alert as something like "approved pattern for this specific service account." That teaches the model the context - this action is okay *for this entity* - without lowering the threat score for the same action coming from a new user or a compromised account.

So, before you touch any confidence thresholds, check your tool's feedback menu. Look for options that let you specify a reason. If it's not there, you might need to lobby your vendor, as others have mentioned. Tuning thresholds without that granularity just trades one type of fatigue for another.