Qevlar AI
Reducing false positives feels necessary, but aggressive alert tuning creates blind spots where low-and-slow attacks thrive. Learn why modern SOCs must rethink tuning, how weak signals get suppressed, and how AI can investigate every alert at scale to eliminate visibility gaps.
Every SOC analyst knows the feeling. You arrive Monday morning to find a ton of alerts waiting in the queue. Most are false positives: the same noisy authentication failures, benign process executions, vendor scanning activity you've seen a thousand times before, and so on.
So you do what any reasonable security team would do: you tune by adjusting rules and adding exceptions.
And in doing so, you may have just opened the door to your next breach.
The cybersecurity industry has a false positive problem, and we often talk about it. Studies confirm that up to 53% of alerts investigated by analysts turn out to be benign.
Alert fatigue is real, burnout is rampant, and the pressure to "clean up the dashboard" is intense.
But while we've been laser-focused on eliminating false positives, we've created an equally dangerous problem that gets far less attention: false negatives.
The problem with false negatives is that they are invisible until your incident response team is picking through the rubble of a breach that's been running for months.
Consider this scenario that plays out in SOCs every single day:
Your detection rule doesn't fire, and the attacker eventually succeeds. Six months later, you discover 400GB of customer data was stolen.
Advanced persistent threats don't announce themselves with obvious indicators. Modern attackers understand detection engineering better than most defenders.
According to Mandiant's M-Trends 2024 report, the global median dwell time for intrusions is 10 days, and that's only for breaches that get detected. The ones that leverage detection blind spots? They persist much longer.
Those weak signals your team suppressed often are:
When you tune away these signals, you're eliminating visibility.
Here's the dilemma facing every SOC leader: you need to maintain high-fidelity detection to catch sophisticated threats, but you don't have the analyst capacity to investigate every low-confidence alert.
Traditional approaches force you to choose between the two following options:
The fundamental problem is that human-scale investigation cannot keep pace with the scale of machine-scale attack surfaces. You can't investigate 10,000 weak signals manually, and that’s where AI comes in to help.
What if you didn't have to choose? What if you could maintain comprehensive visibility without overwhelming your analysts?
Instead of forcing humans to triage machine-generated alerts, Qevlar AI uses graph-based AI to investigate and enrich every alert, even the weakest signals, without LLM hallucinations.
When that slow password spray appears, AI doesn't just see 15 failed logins. It sees 15 failures across 8 accounts, from an IP address with no prior legitimate access, during off-hours, using authentication patterns inconsistent with your organization's standards.
That misconfigured automation script? AI recognizes the pattern, validates it against known legitimate activity, documents the finding, and closes the alert with a full audit trail.
And it works in any other scenario, as Qevlar AI applies the same level of contextual correlation to every alert type. Even in the noisiest log streams, it surfaces the one action that doesn’t belong.
As Daniel Aldstam, Chief Security Officer of GlobalConnect, using Qevlar AI, puts it:
"With Qevlar AI, we've achieved the depth and consistency of investigation we've always aimed for. Every alert, no matter how subtle, is analyzed and documented in minutes. It has given us confidence, clarity, and the ability to scale without compromising quality."
Leading enterprises and MSSPs using Qevlar AI are seeing:
Your detection rules should be optimized for comprehensive threat coverage. The investigation layer should scale to handle alert volume, so the team can focus on complex cases and proactive measures and develop the top SOC analyst skills needed to face the new era of GenAI threats.
Because somewhere in that noise you tuned away, there can be a barely detectable signal with the most danger.
The guidance is to optimize detection rules for coverage rather than for keeping the queue quiet. Once the investigation layer can absorb volume, the reason to raise thresholds or add suppression exceptions mostly disappears, since those exist to protect analyst time, not to improve security. You can lower thresholds back toward catching the slow password spray and similar low-and-slow activity, because the cost of a noisier rule is no longer paid in analyst hours.
By adding context the raw count never had. A rule sees 15 failed logins and, above a 100-attempt threshold, stays silent. Investigation sees those 15 failures spread across 8 accounts, from an IP with no legitimate history, during off-hours, with authentication patterns that don't match your environment. The verdict comes from correlating the signal against its surroundings, which is exactly what a volume-based threshold can't do.
SIEM enrichment annotates an alert with related data, but a human still has to read it, correlate it, and decide what it means, which is the bottleneck that caps how many weak signals you can realistically review. Qevlar reaches a documented verdict on every alert autonomously, using graph-based reasoning rather than threshold logic, and does it in minutes. Enrichment makes an alert easier to investigate; this completes the investigation.
