The Agentic SOC: Autonomy Starts with Data

AI agents are often described as autonomous, but in SOC environments they operate primarily as advanced assistants rather than independent decision-makers. They summarize, correlate, recommend, and sometimes execute tightly scoped actions, but accountability and judgment still sit with analysts (human-in-the-loop or human-on-the-loop modes). Analysts still guide how agents interpret signals, validate conclusions, and initiate response actions. Even the most capable agents cannot compensate for incomplete, fragmented, or inconsistent data inputs.

Data Gaps Limit Autonomy

Agents make decisions based on the data they receive. When telemetry lacks continuity or context, agents rely on inference instead of evidence, introducing uncertainty that drives false positives, missed detections, and higher operational costs in security operations

After initial compromise, attackers move across the environment to escalate privileges and access sensitive systems. However, many AI agents operate primarily at the endpoint level, analyzing host activity in isolation. As a result, agents detect individual events but lack the continuous network context required to correlate those events into the behavioral patterns that indicate active lateral movement.

The Agentic SOC Needs Context

SOC analysts are currently trapped in the "human-in-the-loop" cycle, spending the majority of their time investigating alerts and trying to fill in critical context gaps manually.

For example, an AI agent may correctly flag an unusual login or a privilege change. The human analyst must then expend their valuable time to determine if it is a benign anomaly (like routine maintenance) or an indicator of an active attack (like account takeover).

This is the core limitation of current agent-focused approaches: the lack of complete, unified data prevents agents from achieving true autonomy. As a direct result, SOC teams remain over-burdened, and the promise of a fully autonomous security operation continues to be undercut by data-driven uncertainty.

Laying the Groundwork for AI Autonomy

The path to an autonomous SOC starts with continuous, high-fidelity telemetry across on-prem and cloud network communications, endpoints, identities, and dynamic and ephemeral workloads.

Each data source captures different elements of an attack lifecycle and combining them creates a complete picture of what’s happening. Equipped with this unified visibility and context, agents can make high-confidence, data-backed inferences, rather than low-confidence estimates. The outcome is a SOC that automates routine triage and investigation, enabling analysts to pivot their focus from manual data enrichment to high-impact investigation and hypothesis-based hunting.

Learn how to prepare your SOC for agentic AI and build data-ready workflows.