By now, you’ve heard the hype. Agentic AI; self-directed and goal-oriented. Supposedly, the next big thing in security automation. If you’re working in a Security Operations Center (SOC), it might sound like déjà vu.
Agentic AI brings autonomous, decision-making security agents that learn, adapt, and act across environments, while SOAR focuses on orchestrating and automating predefined workflows. The key difference: SOAR executes playbooks, Agentic AI thinks and evolves beyond them.
Didn’t SOAR promise the same? Automate incident response? Reduce alert fatigue? Free up analysts’ time?
So what’s changed?
Here’s the truth: Agentic AI and SOAR solve some of the same problems, but they don’t do it the same way. One follows a flowchart, the other thinks for itself.
Let’s break it down.
SOAR: Rigid but Reliable
Security Orchestration, Automation, and Response (SOAR) was built with order in mind. At its heart, it’s workflow automation. You define a playbook. A suspicious login from an unusual location triggers multi-factor authentication. A phishing report from a user quarantines the email, isolates the endpoint, and sends a Slack message to Tier 1.
It’s structured. It’s programmable. It’s predictable. And that’s both its strength and its weakness.
SOAR doesn’t improvise. If the conditions don’t match the script, it stops. Or worse, it misfires. That’s why most SOAR deployments end up semi-automated; a human analyst still needs to approve actions or fill in missing context. You spend hours building integrations, mapping out every possibility, and updating playbooks when the threat landscape shifts.
In theory, SOAR replaces the grunt work. In practice, it still needs babysitting.
Equally important is the fact that SOARs are challenging to operationalize and implement even with a dedicated team to build and maintain the playbooks and integrations. The cost quickly goes up while time to value remains out of reach.
Agentic AI: Thinks, Plans, Acts
Agentic AI takes a different approach. Instead of hard-coded playbooks, it uses objectives. It doesn’t wait for every condition to be perfect. It makes decisions, adjusts course, and adapts.
You don’t have to tell it what you want. It comes pretrained to “investigate this suspicious process,” or “triage this phishing alert.” It reasons through alerts, dynamically gathers relevant evidence, and explains its conclusions in ways analysts can audit and trust, mirroring how a human would investigate, but with greater scale and consistency,” explains Prophet Security, a leading AI SOC Platform provider.
It doesn’t just follow steps. It chooses them.
Crucially, it reflects on results. If an action fails, it tries something else. If it needs more data, it knows how to get it. That loop (think, plan, act, reflect) is what sets Agentic AI apart.
The Real Difference: Autonomy
SOAR automates tasks. Agentic AI automates decisions.
That’s it. That’s the crux.
With SOAR, you’re still in charge. You draw the map and the tool just follows it. With Agentic AI, you give a destination. It finds the route. Maybe not the one you’d expect, but one that works.
It’s hardly magic, just a different philosophy.
SOAR assumes stability, known inputs, and known outputs. Agentic AI assumes complexity, uncertainty, and incomplete data, but it still moves forward.
Where It Shows Up
Let’s take a real SOC scenario.
SOAR: An alert comes in. It matches a known phishing signature. Your playbook kicks off. It cross-references the sender’s domain, checks for lookalikes, pulls threat intel from VirusTotal, and triggers containment.
Agentic AI: The same alert comes in. It notices a user-reported email with unfamiliar language. It scans mail headers, compares tone and grammar to past correspondence, extracts IOCs, pivots to the firewall, and flags lateral movement – all while chatting with the analyst: “Would you like me to block this IP?”
SOAR connects tools. Agentic AI understands them.
The Cost of Control
Now, let’s talk tradeoffs.
SOAR gives you control. Every step is pre-approved. There are fewer surprises. It’s safe, assuming you have the time and talent to maintain the workflows.
Agentic AI gives you flexibility. It handles the unexpected. But you have to trust it. That’s a cultural leap. Many teams aren’t ready to let a machine make judgment calls, yet.
However, businesses need to be cautious and not trust these agents blindly. Human oversight is core to ensure these agents don’t run aloof, especially in critical environments. Human oversight is a key requirement across all AI governance regulations, including the EU AI Act, for a good reason. No one wants a rogue agent deleting logs or suspending users based on a misread signal.
Guardrails matter, as do clear boundaries and auditable actions.
But here’s the kicker: Agentic AI learns from experience. It improves; SOAR doesn’t.
What About a Hybrid Approach?
Some vendors are blending the two. Think of it as “SOAR with a brain.” Your platform still runs playbooks; but now, those playbooks include agents that make real-time decisions within predefined limits.
It’s not full autonomy. But it’s a start. A safer middle ground for risk-averse organizations. And for now, that might be the best of both worlds: human guardrails, machine initiative.
Why It Matters Now
SOC burnout is real. Alert volumes keep climbing. Threats are more subtle. Tools are fragmented. People are tired. We don’t need more dashboards. We need intelligent action.
Agentic AI offers that, and not in the future, it’s here already.
It doesn’t just take tasks off your plate, it handles complexity, finds patterns, and responds to nuance. That’s the leap.
If you think of SOAR as your hands, Agentic AI could be viewed as your brain.
A Question of Evolution
This isn’t a question of replacement but one of evolution.
SOAR is still useful. Especially for repetitive, low-risk tasks. But it’s brittle when the unexpected happens.
Agentic AI is for the grey areas. The messy ones. It gives your SOC a thinking partner, not just another tool.
Don’t fall for the buzzwords. Look at what your team needs. If it’s control, stick with SOAR. If it’s adaptability, experiment with agents.
The difference is autonomy. And in today’s threat landscape, autonomy isn’t a luxury; it’s a necessity.
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