The Agentic SOC: Four Myths, Busted

A security leader finishes a pitch to the board about an agentic SOC that pairs AI agents with analysts to address the coverage gap. A hand goes up. “So, the AI decides who’s a threat?”

It’s a fair question. It’s also the wrong one, built on a myth that’s common enough to stall adoption on its own.

Here’s the real story: An agentic SOC embeds AI agents into threat detection, investigation, and response (TDIR) workflows, but people still own every decision that carries real business, legal, or operational weight. Agents bring speed, scale, and a tireless first pass at the evidence. Analysts bring judgment, context, and the accountability no model can carry.

Four myths keep getting in the way of that partnership in board meetings, analyst Slack channels, and vendor evaluations. Based on our conversations with security leaders, industry analysts, and other industry leaders, here’s the truth.

Myth 1: AI Replaces SOC Analysts

Those calls take context no alert provides: risk tolerance, operational impact, and what the business can absorb. An agentic SOC doesn’t take that judgment away from analysts. It gives them more time to use it, because less of their shift goes to chasing evidence and switching tools, and more goes to deciding what’s worth acting on and completing other proactive tasks like detection engineering.

The job gets more focused on expertise, not less dependent on it.

Myth 2: AI Makes the Security Decision

Containment can cut off access, interrupt operations like assembly lines, affect employee productivity, or trigger a reporting obligation. Someone has to be able to explain why the action was taken, what evidence supported it, and who signed off on it. No model can carry that accountability for you.

The speed gain is real, and it doesn’t erase that line. In a cyberespionage campaign disclosed by Anthropic, AI agents handled most of the tactical operations while human operators still chose the targets and made every strategic call. Even inside a machine-speed attack, humans stayed in charge of the decisions that counted.

Agents move fast. Analysts still decide.

Myth 3: An Agentic SOC Eliminates Investigations

An AI agent, Exabeam Nova AI among them, can do that first pass for you: gather the context, connect related entities, assemble a preliminary timeline, and summarize the findings before you start your own review. You open an organized case instead of a pile of disconnected alerts.

What’s left is the part that actually counts: figuring out intent, checking scope, weighing impact, and deciding what happens next. AI assembles the facts. Analysts decide what those facts mean.

Myth 4: AI Agents Are Just Tools

Access controls still do real work, but authorized access doesn’t guarantee expected behavior. You also need to know how an agent normally operates: what it touches, which workflows it follows, and when its activity drifts from that baseline.

Agent Behavior Analytics (ABA) extends the same behavioral baselining Exabeam built for human users to AI agents and other non-human accounts, so a growing population of agents and service accounts doesn’t turn into a growing visibility gap. Securing the agentic enterprise means governing agents with the same rigor you apply to people, not giving code a pass a human would never get.

What Busting These Myths Changes for Your Role

Security Leaders: Govern Speed Without Giving Up Accountability

Evaluate the agentic SOC as an operating model, not a promise of full autonomy. Define where agents can recommend, where they can act, and where human approval is required. These four myths are usually the real objection behind a stalled initiative, even when the stated concern sounds like something else, and naming the myth directly tends to move a board conversation faster than a general case for AI adoption does.

Security Architects: Govern Agents Like Any Other Account

Treat AI agents as accounts with scoped permissions, authorized context, behavioral monitoring, and traceable actions. Myth four is the one to bring into your architecture reviews. If your organization is deploying agents anywhere in the business, not just the SOC, the same governance question applies to all of them. Build oversight into TDIR workflows instead of bolting on a separate process after deployment.

Security Analysts: Move From Evidence Collection to Risk Judgment

None of this is a case for a smaller team. It’s a case for spending your time on the investigations that actually need you instead of the ones that just need someone to pull the evidence together. Use AI-generated context as a starting point, not a verdict. Validate it, test it, and apply what you know about the business. The agent doesn’t decide. You still do.

Agentic SOC Terms to Know

Agentic SOC: A security operations model that combines AI-assisted investigation and automation with human oversight and decision authority.

AI agent: A software program that can pursue a defined task, use tools, retrieve information, and take actions within established boundaries.

Agent Behavior Analytics (ABA): Exabeam technology that applies behavioral baselining and risk scoring to AI agents and other non-human accounts the same way user and entity behavior analytics (UEBA) does for human users.

Non-human account: An account or actor that performs actions in a system without direct human control at the time of action, including AI agents, service accounts, and automated workflows.

Human-in-the-loop: A control model in which a person reviews, approves, or overrides an AI recommendation or action at defined points.

Nova AI: A suite of AI agents built into New-Scale Fusion that assist analysts with investigation, including drafting timelines, summarizing context, and recommending next steps.

The Agentic SOC Keeps People in Control

Back to that hand going up in the board meeting: The AI doesn’t decide who’s a threat. It decides what’s worth an analyst’s attention, and that’s a narrower claim than any of the four myths give it credit for.

The agentic SOC doesn’t remove analysts, decisions, or investigations. It applies AI where speed, scale, and repetition create the biggest bottlenecks, and it leaves judgment, business context, and response authority exactly where they belong. AI agents also become another account the moment you deploy them, so adoption depends on the same visibility and accountability you’d expect from any actor with enterprise access.

See How to Secure Humans and AI Agents Together

Explore the Securing the Agentic Enterprise white paper for a deeper look at behavioral risk, non-human accounts, and the operating model behind the agentic SOC.