Building the Agentic SOC

AI is fundamentally changing the speed, scale, and adaptability of cyberattacks. Breakout time—the window between initial compromise and lateral movement—has collapsed as attackers use AI to automate reconnaissance, adapt in real time, and evade static defenses without human intervention.

Traditional SOC workflows cannot keep pace. Security teams already operate under overwhelming alert volume, forced to triage thousands of signals daily while piecing together fragmented evidence across tools, environments, and identities. To manage the load, analysts suppress low-fidelity detections, aggressively tune alerts, automate closure, or allow queues to age out—introducing blind spots attackers increasingly exploit.

This creates a dangerous asymmetry: attackers now operate at machine speed while defenders still rely on workflows built for human-scale investigation.

Enterprise security has reached a tipping point. The shift to an agentic SOC is no longer optional—it is required to detect, understand, and respond at the speed of modern attacks.

Large language models (LLMs) are powerful, but expensive and inefficient when applied directly to raw telemetry, packet captures, and unfiltered logs. Feeding massive volumes of disconnected data into autonomous agents quickly overwhelms token budgets, saturates context windows, and degrades reasoning quality as meaningful signal gets lost in noise.

Autonomous agents require structured context: data that is already enriched, correlated, and optimized for machine reasoning. High-signal context allows models to reason efficiently about relationships, behaviors, and intent—who communicated with whom, over what protocol, in what sequence, how activity deviated from baseline, and whether it requires immediate attention.

When an autonomous triage or response agent encounters a signal, ExtraHop provides immediate, high-fidelity context across the environment. Agents can then drill into packet-level evidence only when a specific hypothesis or investigation requires deeper validation.

In this demo, we are given an FBI warning about the Handala hack team—an Iranian-linked, pro-Palestinian hacktivist group widely considered a front for Iran's Ministry of Intelligence and Security (MOIS). It provides information about a malicious file hash. We then use Claude to find indicators of compromise and suspicious activity, as Claude taps into network context from ExtraHop and threat context from ReversingLabs.

Leverage the network as a source of truth in order to stop cyberattacks. The ExtraHop RevealX platform allows you to get unparalleled visibility and security control across all assets in your organization.