Agentic AI: How Will it Impact SOC Analysts’ Roles?
Agentic AI and cybersecurity innovation.
Agentic AI: How Will it Impact SOC Analysts’ Roles?
Among several interesting topics and trends discussed at RSAC 2025 in the USA, agentic AI emerged as a genuinely exciting development. Indicating that it’s more than just a buzzword, Microsoft’s Vasu Jakkal delivered a 20-minute speech focused solely on security in the age of agentic AI.
These systems hold immense promise because they can autonomously pursue security objectives, initiate actions, and adapt to evolving threats. In other words, they act rather than just assisting in the way traditional AI tools do for security tasks.
But what does this automated action mean for the role of the SOC analyst? Are we entering an era where human judgment is sidelined, or will the analyst's role evolve into something more strategic, interpretive, and intervention-focused?
This article breaks down what agentic AI really is and why it’s all the rage in cybersecurity right now. The blog also explores how agentic AI might reshape the day-to-day reality of SOC analysts’ roles.
What Is Agentic AI?
While AI use cases in the SOC have helped with tasks such as correlation, enrichment, or ticket triage, the most commonly used systems and models are fundamentally reactive. Instead of reacting to instructions, agentic AI systems take initiative. They interpret intent, pursue objectives, chain tasks together, and adapt in real time, all without constant human prompting.
For example, let’s say an agentic AI detects an unusual authentication attempt. Instead of flagging it and waiting in the queue, it might autonomously cross-reference endpoint logs, check recent access behavior against threat intel, launch a containment script, and kick off a broader investigation for analysts.
So, why now?
Because three major shifts have converged:
1. The rise of advanced orchestration layers built on LLMs
LLMs like GPT-4 or Claude aren't agentic on their own. But when embedded into agentic frameworks like Microsoft's AutoGen Studio, OpenAI’s function-calling agents, or ReAct-style multi-step planners, they can interpret goals, reason through intermediate steps, and call APIs to take actions in sequence.
2. Contextual memory and goal-setting capabilities
Rather than just automation, agentic AI is also about persistence and purpose. New frameworks allow AI agents to remember, reflect, and course-correct, which are crucial for navigating dynamic, high-noise SOC environments. An agent might pursue a goal over hours or days, adjusting as new telemetry flows in.
3. The need for autonomous defense at scale
Modern attack surfaces, including hybrid cloud, distributed identities, and ephemeral containers, shift faster than human teams can track. Human-led triage in SOCs falls apart under the sheer volume of telemetry. The demand for a proactive, scalable response drives agentic adoption.
The SOC Analyst Role
The traditional SOC has long been structured around layers: analyst tiers, playbooks, and escalation paths. At the base, junior analysts sift through noise by triaging alerts, tagging false positives, and cross-referencing threat intel feeds. At higher tiers, more seasoned analysts handle escalation, conduct root cause analysis, and maybe (if time allows) engage in proactive threat hunting.
Most AI introduced to the SOC in the past few years hasn’t changed this significantly. Machine learning helped group alerts while SOAR tools automated ticket handling and predefined response playbooks. It sped things up a bit, but alert fatigue remains the norm.
With agentic AI, the SOC analyst can work alongside a system that behaves with intent. It’s not waiting for the analyst to trigger a playbook. These systems initiate and escalate on their own, then report to analysts what they’ve found. They can suppress false positives, enrich alerts, and even complete low-complexity investigations faster and more consistently than a human ever could.
This doesn’t mean entry-level SOC roles will disappear, though. Instead of churning through queues, junior analysts' tasks might be more about monitoring agent behaviour. They’ll audit decisions, assess confidence levels, and flag when the AI gets it wrong.
At a more senior SOC analyst level, the impact will be evident, but more subtle. Senior-level analysts may need to take on new tasks, such as refining goals and defining the operational boundaries of agentic AI.
With agentic AI in the SOC, certain skills start climbing the hierarchy:
Interpretability – Can analysts explain what the AI did, and why? If not, how can they justify it to leadership (or regulators)?
Escalation judgment – When should they let the AI run, and when should one intervene? Knowing the difference is now a critical skill.
Collaborative threat modeling – Analysts will need to consider scenarios that anticipate the actions AI agents might take, rather than focusing solely on threat actors’ actions.
AI Behavior Analysis – As agents get more complex, debugging them becomes a core part of the role.
Opportunities and Risks in the New Analyst-AI Dynamic
Any time a paradigm shifts, there’s a temptation to frame it as a trade-off: human or machine, control or automation, intuition or efficiency. But the reality of agentic AI in the SOC is more nuanced.
Less Noise and More Signal
The most immediate benefit of agentic AI is relief: fewer false positives, fewer repetitive decisions, fewer hours wasted parsing logs for the hundredth time. Recent research suggests 83 percent of SOC professionals feel overwhelmed by false positives and high-alert volume. Systems that take initiative can handle low-level triage, dynamically prioritize alerts based on contextual risk, and escalate only when something actually warrants your attention.
When AI handles the mechanics, analysts have space to engage in something far more valuable, which is narrative-driven analysis. Instead of racing through tickets, they’ll have time to craft high-fidelity incident stories. These stories can capture attacker intent, dwell time, kill-chain movement, and explain how it all ties back to business impact.
Agility in Crisis
Agentic AI also enhances speed. In fast-moving incidents like ransomware propagation or cloud credential theft, the system can initiate containment actions while you’re still scanning the alert. That kind of head start buys both time and options.
Blind Trust
On the flip side, one risk is to blindly trust that the agentic system will usually get things right, which can be dangerous. The moment you stop questioning, you stop analyzing and become a bystander. In commercial aviation, much of the flight, from takeoff to landing, can be automated. But no seasoned pilot blindly defers to autopilot. Instead, they monitor, cross-check, and even prepare for edge cases the system can’t anticipate.
For SOC analysts, the job is to remain situationally aware even in the face of such clearly useful automated systems. It’s about staying ready to take the wheel and knowing when to do so.
Why Cyber Ranges Are Essential in the Age of Agentic AI
Agentic AI is powerful, but it isn’t infallible. It can make the wrong call, act prematurely, or miss subtle context that a human would catch. And when that happens, the question is, can your SOC analysts intervene, and how quickly and confidently will they be able to?
The ability to take back control under pressure and deal with unfolding cyber incidents comes from practical experience of making real-time judgment calls in complex environments. Live-fire cyber ranges help build and practice these skills without real-world consequences by giving SOC analysts the chance to operate in high-fidelity simulations that replicate your network conditions and use actual threat actor TTPs.
Another benefit of cyber ranges in the era of agentic AI is that they’ll keep human defenders sharp. As more decisions get delegated to machines, there's a risk of deskilling. Analysts who once thrived under fire may start losing their edge because they’re not being tested.
Cyber ranges reverse that drift through engagement, varying scenarios, and exposure to uncertainty.
Cloud Range is a leading cyber range as-a-service platform. With dozens of cybersecurity simulations mapped to the MITRE ATT&CK Framework, Cloud Range helps SOC teams defend against complex attacks and keep skills sharp, even in a world of agentic AI in the SOC.
Cloud Range’s customized cyber range program to boost SOC readiness.
