The Agentic SOC: Accelerating Defense or Expanding Enterprise Risk?

The Anthropic G2G-1002 campaign marked a turning point: the first real-world, AI-orchestrated intrusion. In response, enterprises are rushing to adopt agentic technology to keep pace with attacks that move at machine speed, bypass standard perimeters, and create 'alert noise' to hide deep network penetration.

But speed without strategy is a liability. Automating an overloaded SOC only serves to exacerbate existing gaps.

Because agentic AI performance is tethered to data quality, incomplete inputs produce dangerous outputs. The result is a 'fast mess': a system that scales human error to machine speed, allowing sophisticated threats to slip through the cracks faster than any team can catch them.

Incomplete Data Leads to Hallucinations in the Agentic SOC

Most SOCs rely on a fragmented set of data sources, from logs to endpoint activity to threat intelligence feeds. These sources provide only brief snapshots of activity across the enterprise. When AI engines are fed these snapshots, they are missing a large portion of the context.

To fill the gaps, AI makes assumptions. These assumptions often produce hallucinations. They appear valid, but are not entirely based in reality.

When agentic systems make autonomous decisions based on partial information, it creates a false sense of safety. Analysts can miss real threats while chasing phantom alerts. Mean time to respond (MTTR) increases and investigative hours are wasted. The critical question becomes: is the AI actively defending the enterprise or is it merely guessing?

Establishing the Network as the Ground Truth

What SOCs are missing is detailed, continuous insight into who is communicating with whom, over which protocols, and with what outcomes. Logs and alerts capture events, but they do not show the relationships between them.

Without network-level insights, agents only see isolated events – a login here, a file transfer there – rather than how the events and other elements connect.

For example, a single unusual file transfer might seem harmless on its own. However, when viewed in the context of network activity – revealing what data is moving and where connections occur, and which user initiated it – could indicate a coordinated intrusion. The absence of this network context leads to misjudged threats, misdirected responses, and compounded risks.

Network traffic provides continuous visibility into the movement of data across the enterprise. Unlike logs, which are summaries that can be edited or suppressed, network traffic is raw evidence. The agentic SOC performs best when fed a “diet” of high-fidelity network data. With this information, guesswork disappears and autonomous decisions are grounded in observable behavior, rather than assumptions.

Anchoring the Agentic SOC in Complete Data

The competitive advantage does not belong to the organization with the latest AI. It belongs to the organization that ensures the agentic SOC is operating on complete, trustworthy data.

Data integrity, not model sophistication, determines whether AI-powered automation reduces risk or accelerates it.

The path to a reliable agentic SOC is not faster algorithms – it’s full visibility, continuous monitoring, and evidence-based AI.

Learn how the agentic SOC can make smarter decisions with complete, high-fidelity network insights.