The New Front Line: How Agentic AI Is Revolutionizing SOC Efficiency

In the Security Operations Center (SOC), time isn’t just valuable, it’s everything. The difference between a few minutes and a few hours can mean stopping an attacker mid-reconnaissance or cleaning up after a full-scale breach. That’s why Agentic AI isn’t just a checkbox feature, it’s a frontline capability that delivers true value when it’s actively used by an MXDR partner in real-world operations.

While many MDR providers are now starting to talk about Agentic AI, Ontinue has been operationalizing it since December, running it in production across our global customer environments. We’ve spent months refining it, teaching it, and validating its outputs in live incidents. It’s not theory. It’s tested, tuned, and trusted by our SOC analysts, every day.

What We Used to Do (and Why It Was a Problem)

Before Agentic AI, investigating a single alert often required a sequence of manual queries: Who is the user? What is their role? Have they been targeted before? Is this device normally used by them? Have we seen this behavior in the environment recently?

Even with automation, this was time-consuming and inconsistent. When analysts are triaging hundreds of alerts a day, context often gets skipped, leading to slower escalations or, worse, missed threats.

For organizations trying to run a SOC in-house, this creates a major resource and staffing challenge. Standing up and operating a 24/7 detection and response function requires not just tools, but skilled analysts, mature processes, and constant tuning. Many security teams simply don’t have the budget, scale, or bandwidth to manage that level of complexity on their own.

That’s why more companies are turning to MDR and MXDR partners to relieve the burden—and why the right partner matters. A mature MXDR provider that deeply integrates Agentic AI into its operations doesn’t just extend your team, they help you move faster, respond smarter, and gain an edge in a threat landscape where speed is everything.

A SANS 2024 SOC Survey found that 66% of SOC teams say the volume of alerts they receive exceeds their capacity to respond. That stat matches our own experience. Without the ability to pre-triage and enrich alerts – at scale – even the most skilled analysts can get overwhelmed. That’s exactly where Agentic AI makes the difference.

How Agentic AI Changes the Game

Agentic AI isn’t just another automation layer. It’s built to act with autonomy—pulling relevant user, device, and historical data together to tell a story before an analyst steps in. Here’s what that looks like in practice:

User Role Awareness

When we get an alert, Agentic AI immediately identifies the user involved, what department they’re in, whether their role involves travel or remote access, and what a “normal” pattern looks like for them.

That matters. For example, if a marketing user logs in from multiple geographies in a week, that might be normal. But if a finance administrator suddenly logs in from an unknown IP in a country they’ve never accessed from, that’s a red flag.

Device Behavior and History

The AI examines device activity over the past 7–30 days. Has this device connected to suspicious domains before? Did it have a previous malware alert? Was it flagged in a phishing campaign recently?

With that history surfaced instantly, analysts can assess risk with much greater confidence.

Incident Correlation and Confidence Scoring

Agentic AI evaluates how similar alerts have been handled across other customers. If an alert type has been confirmed as a true positive in 90% of cases, especially involving the same malware strain or tactic, that confidence score gets fed into the current case.

That’s huge. Instead of starting from scratch, our analysts begin with a data-backed judgment, often supported by dozens of prior incidents.

What This Looks Like in the SOC

Recently, we saw an alert that initially looked like phishing, but Agentic AI dug deeper and it correlated the user’s activity with a previously targeted phishing campaign, uncovered JavaScript execution on the landing page, and flagged malware installation on the device.

Before the analyst even opened the case, the system had pieced together that this was not just a phishing email, it was part of a multi-stage attack in progress. That early visibility allowed us to take automated action, contain the device, and notify the customer within minutes.

According to the 2024 IBM Cost of a Data Breach Report, organizations that use AI and automation extensively cut breach lifecycles by an average of 108 days compared to those that don’t. We’ve seen similar results. What used to take 20–30 minutes to triage can now be addressed in 5–10, sometimes automatically.

Analysts + Agentic AI = Better Decisions, Faster

Here’s what Agentic AI means for my team:

• Less noise: By pre-filtering irrelevant or benign alerts.
• Faster action: Because context is ready from the start.
• Stronger confidence: Thanks to pattern recognition and historical insights.
• Better escalation: Because our recommendations are backed by data.

Most importantly, it frees up our analysts to focus on the hard problems—the zero-days, the novel behaviors, the creative anomalies. We’re not just reacting faster, we’re getting ahead.

Agentic AI doesn’t replace human judgment. It enhances it. It takes the repetitive, context-gathering work off analysts’ plates so they can think critically and act decisively.

In today’s threat landscape, speed and clarity are everything. Agentic AI delivers both, and that’s why it’s becoming the first line of defense in the modern SOC.