From 10,000 to 10: How AI Identifies Which Alerts Actually Matter

Security teams are overwhelmed.

In today’s threat landscape, the average Security Operations Center (SOC) faces thousands—sometimes tens of thousands—of alerts every day. Most of them are noise. A few point to real threats. And the scariest part? They often look the same.

SOCs have reached a breaking point. It’s no longer just about detecting threats—it’s about knowing which ones to prioritize. That’s where AI changes everything.

The Alert Overload Problem

SOCs were never designed to handle this level of volume. Tools like SIEMs, EDRs, and email security platforms fire off alerts in silos. Each alert might only represent a small part of the picture—an anomalous sign-in, a flagged URL, an unfamiliar process.

But with traditional triage, these alerts are handled individually. Analysts manually pivot between consoles, check logs, and try to piece together a coherent story. It’s slow. It’s reactive. And it creates burnout.

In many organizations, Tier 1 analysts spend most of their time on routine enrichment: checking IP reputation, user behavior, geolocation, and known device lists. But with thousands of alerts, manual triage is simply unsustainable. The result? Missed threats, alert fatigue, and high turnover.

Why More Tools = More Noise

Ironically, the more security tools a company adds, the worse alert fatigue gets. Every vendor produces alerts based on its own logic, often unaware of the broader context. This leads to duplicated signals, false positives, and blind spots in detection.

Take this common scenario:

• An identity provider logs a suspicious sign-in.
• Your EDR detects a suspicious PowerShell execution.
• Your email filter flags a message with an obfuscated URL.

Individually, none of these might escalate. But together, they could indicate credential theft, initial access, and command execution—the start of a breach.

The problem? These alerts don’t “talk” to each other. Your analysts are left to connect the dots manually—if they have time.

Enter AI: The SOC Force Multiplier

AI doesn’t look at alerts in isolation. It ingests signals across your stack—identity, EDR, email, cloud, network, and more—and automatically correlates them to build context.

Think of AI as a virtual analyst that:

• Pulls in data from tools like Okta, Microsoft Defender, Proofpoint, and Sentinel.
• Understands user behavior, device history, geo patterns, and past alert patterns.
• Chains together related signals to build a narrative.
• Scores risk based on the entire event sequence, not just one alert.

This isn’t just enrichment—it’s storytelling. AI can build an attack timeline in seconds, whereas a human might spend hours correlating logs.

AI also enables horizontal correlation: recognizing when multiple low-severity alerts across different users or endpoints share a common tactic, technique, or indicator. This level of insight is nearly impossible to achieve at scale without automation.

Context Is King—And AI Builds It Faster

The real power of AI isn’t just speed—it’s consistency. While human analysts vary in skill, fatigue, and familiarity, AI applies the same rigorous logic every time. It doesn’t skip steps. It doesn’t miss signals buried three hops deep.

‍

For example:

• A user logs in from a suspicious IP.
• Minutes later, a script runs on their device.
• Moments after, the same user sends an unusual email to finance.

AI correlates those events—across identity, endpoint, and email—and tags it as a coordinated incident. No swivel-chair analysis needed.

AI also leverages statistical models and behavioral baselines. It knows what “normal” looks like for each user, device, and geo pattern—and flags deviations with supporting evidence. This eliminates the guesswork of human intuition.

From Volume to Verdict

Once AI has context, it can move from detection to decision. It can:

• Escalate high-confidence threats to analysts with full supporting evidence.
• Suppress low-confidence noise without dropping true positives.
• Trigger playbooks for containment—like quarantining emails, disabling sessions, or alerting users.

This means your SOC isn’t buried in alerts. Instead, it’s focused on verdicts—incidents that matter, backed by data, ready for action.

AI also improves the feedback loop. As analysts review and disposition incidents, the AI learns from outcomes—refining its confidence thresholds and prioritization logic over time.

Telemetry Fusion: Seeing the Full Picture

At Culminate, our AI SOC analyst integrates data from across the enterprise:

• Identity Providers (Okta, Entra ID): login anomalies, geo risk, MFA abuse
• Endpoint Detection & Response (Microsoft Defender, CrowdStrike): malware, lateral movement, persistence
• Email Security (Proofpoint, Defender for Office 365): phishing links, spoofing, payload delivery
• Cloud Logs & SIEMs (Sentinel, Splunk): session hijacking, privilege escalation, DLP

Each signal is valuable, but only in context. AI fuses them to detect patterns that humans miss—and filter out the noise that humans waste time chasing.

The Results: Real Impact in Real Time

Organizations using Culminate’s AI-driven SOC assistant have seen:

• 90% reduction in alert triage workload
• 60% faster Mean Time to Detect (MTTD)
• 70% faster Mean Time to Respond (MTTR)
• Better analyst retention due to reduced burnout and more meaningful work

One client saw a drop from 15,000 raw alerts per day to just 18 meaningful escalations—with 100% coverage and faster containment.

Final Thoughts: The Future of the SOC

You can’t solve alert fatigue by hiring more people. You solve it by giving your people superpowers. That’s what AI does—it extends your SOC’s reach, context, and speed without sacrificing precision.

In the age of constant threats, fast-moving attackers, and hybrid infrastructure, SOCs need more than visibility—they need clarity.

From 10,000 to 10 isn’t a marketing line. It’s a survival strategy.

Ready to see what Culminate’s AI Analyst can do for your team?

Let’s talk.