Agentic SOC

It was a Saturday when a friend called. His company had been hit, a ransom demanded, could I help. I walked into an incident that looked already over: every VM…

In a live run of the Part 2 attack, Stream.Security's AI SOC detected the compromised key, reconstructed the full attack chain including a hidden persistence backdoor, and deleted the stolen credential on its own, stopping the attack in about ten minutes with no human in the loop. Part 4 of the Building the Agentic SOC series is about the decision that made that safe: which response actions a human lets an AI agent take autonomously, and why that call belongs to the Security Architect.

One attack in Part 2 threw off four separate alerts, and the obvious fix, one investigation agent per alert, rebuilds every failure of the overloaded human SOC at machine speed and cost. Part 3 of the Building the Agentic SOC series lays out the alternative: every related alert attaches to one linked case, worked by a small set of specialized agents, a dispatcher and case leader, over one shared model of the case and the environment.

An AI agent given a single leaked AWS key escalated from a low-privileged Lambda user to full account takeover in under 25 minutes, wrong turns included. Part 2 of the Building the Agentic SOC series walks through the attack step by step, from credential theft to a brute-forced admin identity, to show exactly what the agentic SOC has to detect and stop.

Security Architects are measured on how well they design defense, almost never on how well the SOC can respond to what that design misses. As AI turns the SOC from an organization into a system of agents, closing that gap becomes an architecture problem, and the Security Architect is the only one positioned to own it. Part 1 of a series building an agentic SOC hands-on with the open-source CDRGoat project.

AI agents and tools won't scale security until the environment itself is modeled. Real context isn't enrichment — it's a live representation of state. That shift changes what tools, harnesses, and agents actually mean, and what StreamForce is built on.

AI oversight collapsed in steps as systems became "good enough." We got speed but lost resilience. Errors now accumulate silently and surface after dependencies compound. Adding more AI to the SOC accelerates this problem. The fix is architectural: a continuously updated model of your environment that knows what's true now.

AI tools are only as effective as the data they learn from. For the first time, the industry has tools to make this data accessible and meaningful. Let's look at how the industry sees AI SOC agents and how to maximize the use of their data sources.

Stream.Security just launched a new AI Triage feature to assist analysts in addressing the growing volume of alerts in the cloud and SaaS. The AI Triage agent brings human-like intelligence and decision making to SOC analysts. With Stream’s real-time CloudTwin model, the triage digests updated risk-based data that factors in asset criticality, exposure, and blast radius to prioritize alerts that truly matter.