Fighting Fire with Fire: The Case for an Agentic SOC

Here is an uncomfortable number for you to start your morning: 4. In a documented intrusion this year, an attacker gained access, moved laterally, and began exfiltrating data in just four minutes. And that is not the extreme: The fastest breakout time CrowdStrike recorded was just 27 seconds. That’s less time than it takes most of us to find a parking spot or buy a coffee.

Your analysts are talented. They are also human. This means they sleep, they go on vacation, and they occasionally need a second cup of coffee before the alerts start making sense in the morning. The threat actors running AI agents doing reconnaissance against your environment require none of those things. They do not get tired at 2 a.m. on a Saturday night and do not wait for your change control approvals to execute their attacks.

This is the heart of the problem we keep raising with security leaders. The threat has accelerated past humans-in-the-loop for every decision and action. CrowdStrike’s 2026 Global Threat Report measured an 89% year-over-year jump in AI-enabled adversary activity. The gap is widening. While you cannot out-click a machine, you can out-think one. This requires giving your people agents of their own, enabling them to fight fire with fire.

Your Adversary Already Put AI on the Payroll. Your SOC Deserves the Same Advantage.

Throughout the history of cybersecurity, attackers held a structural advantage. Nation-state actors built their zero-day war chests and chained them together for years before defenders could respond. By the time they were detected, the attackers had already won.

AI broke that pattern. The same foundational advances powering attacker’s automation became available to defenders to out-automate their opponents. We call this the Cyber AI Parity Window: a rare stretch of time when defenders hold technology equal to their adversaries.

Parity is a gift. It is also temporary. Advantage will go to the team that operationalizes AI fastest and most thoughtfully. While sitting on the sidelines and waiting for technology to mature is an option, it’s a decision that quietly concedes defeat.

What Does an Agentic SOC Actually Do?

When we walk clients through the Agentic SOC, we are careful to differentiate it from the scripted automation SOAR has been promising since it became a tool vendors could monetize. These automations followed rigid playbooks and broke the moment anything changed.

Agentic SOCs deliver intelligent and adaptive reasoning. They plan and take action with human oversight. A modern triage agent investigates an alert the way a seasoned analyst would: building the timeline, working out how far the problem has spread and which systems it touched, pattern and context matching (e.g. connecting the dots across your different tools), and delivering a clear verdict you can trace back to the evidence, all in seconds. Work that used to consume hours to create arrives at your analyst’s desk already structured and ready for them to act. And the best part? These agents don’t need that second cup of coffee or vacations. They deliver constant vigilance.

Architecture matters here. The early assumption was that the alert landing in the SOAR platform could run an entire investigation on its own. Real-world use taught us otherwise. A real investigation pulls clues from many different tools, validates the data against threat intelligence feeds, and checks its work along the way (with or without human-in-the-loop guidance).

A coordinated team of agents is what security organizations require. Each agent has a specialty. Some gather the supporting evidence, some work through what it means, some confirm the findings, and some carry out the response, with the whole team adjusting based on the kind of alert and environment in scope.

The Human/AI Operating Model: Who Does What

This is work In Balance IT helps our clients define. It’s the part that we see most vendors skip yet CISOs and security organizations require. What is the most effective way for my organization to adopt AI and build an agentic SOC?

Buying agents is easy. Governing them is the job that requires focus. The In Balance IT Human/AI Operating Model divides security activities into four quadrants, with each based on how much human judgment an agent’s actions require.

1. AI runs autonomously – This is where the call is clear and the clock is unforgiving. Confirmed threat actor malware does not need a committee’s sign-off. Kill the session, revoke the access, and quickly isolate the affected machine. In low level non-production environments, this is a great option. Containing the problem in real time keeps the threat from spreading while cutting the appropriate ticket for further investigation and triage.
2. AI acts with human oversight – This is where context changes the answer. An anomalous behavioral pattern might be a real intrusion, or it might just be a developer doing something creative in a test environment. AI assembles the evidence and recommends the response option. An experienced practitioner makes the call.
3. Humans lead with AI assistance – Volume is high and judgment is critical. AI narrows 3,000 known security flaws down to the 30 that are actual risks. People approve the remediation windows and certify the actions. The rubber-stamping era is over.
4. Humans own the decision outright – This is where the consequences of decisions made cascade for years. Risk acceptance, breach disclosure timing, ransom decisions, board reporting, and multi-year strategy belong in the boardroom. No algorithm should be weighing whether to take production offline or how to report risk to your board. A named executive owns accountability for that.

The logic is simple. AI absorbs the repetitive, high-volume, lower-risk, time-sensitive work. Your people move upstream toward oversight, design, and judgment calls that carry real weight.

Hand those repetitive investigations to agents and your analysts shift from overburdened alert triage experts to real SOC analysts. They define the investigation logic, set escalation thresholds, and refine the playbooks the agents run their logic against. When this is done correctly, backlogs shrink, engagement climbs, and your SOC analysts get to work on the problems that drew them into security in the first place.

Where to Start

The first move is honest measurement:

• Where does your SOC stand today?
• Which decisions can safely be moved to autonomous response?
• Where does your operating model still depend on a human?

That is exactly what In Balance IT’s Adaptive Defense Agentic SOC Maturity Assessment is built to answer. We measure whether your defenses can operate at machine speed, where there are gaps and opportunities for improvement, and we map an operating model that keeps humans accountable while machines handle the high-volume work.

The parity window is still open. Let’s make your SOC agentic.

About This Series

This post is the third in the Adaptive Defense series. Each article addresses a specific domain where traditional frameworks fall short of today’s agentic AI threat landscape.

Post 1 — Why NIST, ISO 27001 & COBIT Can’t Keep Up With AI Threats

Post 2 — Agentic Adoption, the New Pattern for Cybersecurity

Post 3 — Non-Human Identity Security: An Attack Surface You Can’t See

Post 4 — Your Coding Agents Have Admin Rights and Trust Issues