Agentic AI is changing what security operations can automate.

Unlike traditional security automation, which typically executes predefined workflows, AI agents can investigate activity across multiple systems, gather additional evidence, interpret what they find, recommend next steps and, within defined boundaries, take action.

That creates an obvious opportunity for security teams. As attackers increasingly use AI and automation to accelerate their own operations, defenders need ways to investigate and respond at comparable speed.

But faster execution alone does not make a SOC more effective.

As SecurityHQ CEO Feras Tappuni argues in his recent CPO Magazine article, “The Agentic SOC Won’t Win on Automation Alone,” the more important question is what sits behind that autonomy.

For security leaders evaluating agentic SOC capabilities, three things matter in particular: whether agents understand the environment in which they are operating, whether they can learn from experience beyond a single investigation, and whether someone remains accountable when an automated recommendation becomes a real security decision.

Context Determines Whether Automation Is Useful

Security teams rarely suffer from a lack of data. They struggle to connect that data to the context required to make a good decision.

An agent investigating suspicious activity might have access to endpoint telemetry, identity data, network activity and threat intelligence. Access to those sources does not necessarily tell it which information matters.

That requires knowledge of the environment itself.

Which assets are business-critical? What behavior is normal for this user? Has this alert been investigated before? Which response actions have been approved? What happened the last time similar activity appeared?

This kind of operational history allows an agent to build on previous investigations rather than approaching every event as if it were new.

Over time, validated analyst decisions can improve how similar activity is interpreted. Confirmed incidents can inform detection logic. Previous false positives can prevent unnecessary escalation. Successful response actions can establish the conditions under which similar actions might safely be taken again.

The advantage is not simply that the SOC can process more work. It is that accumulated knowledge can improve the quality of future decisions.

Effective Learning Has to Extend Beyond One Environment

Customer context is critical, but it presents another challenge.

An AI agent that learns exclusively from one organization can become highly familiar with that environment while remaining unaware of threats the organization has never encountered.

Security operations therefore need both local context and broader security intelligence.

Patterns emerging from investigations, adversary activity and changing attack techniques can provide information that an individual organization could not generate on its own. That broader knowledge can then be evaluated against the customer’s own environment to determine whether it is relevant.

The distinction matters. Global intelligence should not replace customer-specific understanding, and customer-specific learning should not isolate an organization from what is happening elsewhere.

The strongest agentic models will need both.

Human Accountability Becomes More Important as Autonomy Increases

The discussion around agentic SOCs often focuses on how much work an agent can perform independently.

Security leaders should also consider what happens when the agent reaches the limits of that independence.

Some actions are relatively narrow and reversible. Others can interrupt users, isolate critical systems or affect business operations. The technical ability to execute an action does not determine whether that action should be taken.

That is why autonomy needs clearly defined boundaries and accountable human oversight.

Early in the adoption of an agentic workflow, an agent might investigate an event and recommend a response for validation. As the same scenario occurs repeatedly and its recommendations prove reliable, certain low-risk actions may become candidates for pre-authorization.

Higher-impact or unfamiliar situations require a different threshold.

The goal should not be maximum autonomy. It should be appropriate autonomy based on evidence, history and risk.

Measuring the Agentic SOC Differently

This also changes how security teams should evaluate success.

The number of automated tasks completed is useful operational information, but it says little about whether security outcomes are improving.

A more meaningful evaluation asks whether agents are helping analysts reach validated incidents faster, reducing unnecessary investigation, improving the quality of recommendations and enabling appropriate response without creating additional business disruption.

Those outcomes depend on more than the sophistication of the AI model itself.

They depend on the operational knowledge surrounding it, the intelligence informing it, the controls governing what it can do and the people responsible for the decisions that matter.

The future agentic SOC will not be defined simply by how much human work it can automate. It will be defined by how effectively machine speed can be combined with accumulated knowledge, customer context and human accountability.

Read Feras Tappuni’s full perspective, “The Agentic SOC Won’t Win on Automation Alone,” in CPO Magazine.