Remember when Security Orchestration, Automation, and Response (SOAR) was the hot new thing? It promised to be the silver bullet for every overwhelmed Security Operations Center (SOC). Automate the alerts, speed up the response, banish analyst fatigue forever. For a while, it felt like the answer we’d all been waiting for.
But let’s be honest: for many teams, SOAR never quite lived up to its lofty promises. The integrations were clunky. The playbooks were brittle. Every tiny tool update meant debugging workflows you’d built years ago. It left many SOCs feeling less like a well-oiled machine and more like a never-ending IT project.
Meanwhile, the threat landscape didn’t wait. Threats multiplied, new security tools piled up, and with them, a tsunami of alerts. Now, a new contender has arrived: agentic AI. These aren’t just intelligent scripts; they’re tools that reason, learn, and act, fundamentally changing how we approach security operations. They’re taking on the manual, repetitive grunt work of triaging and investigating alerts, and they’re doing it with a contextual understanding that SOAR simply couldn’t touch.
So, if you’re one of the many SOCs feeling the shift, the question isn’t *if* you’ll replace SOAR, but *how*. How do you make this transition without bringing your entire security posture crashing down? This isn’t just about swapping out one piece of tech for another; it’s about evolving your entire operational philosophy. Here’s a practical playbook for making that happen.
Understanding the Shift: Why SOAR’s Story is Ending
Before you even think about unplugging anything, you need a clear-eyed view of what SOAR is actually doing in your environment today – and, crucially, what it isn’t. This isn’t a SOAR-bashing session; it served a purpose in its time. But times change, and so does technology.
Inventorying Your Current SOAR Footprint
Start with a deep dive. Take stock of every integration SOAR touches. Map out every playbook, every automation, and every alert it routes or enriches. This isn’t just a technical exercise; it’s a diagnostic one. What still works effectively? What’s a constant source of headaches?
Are your automations like finely tuned instruments or a house of cards? Are you constantly rewriting custom scripts for every minor tool update? Are you still debugging the same workflows you painstakingly built three years ago? These are the real indicators that your current system might be holding you back.
Agentic systems, by contrast, are built for the complexity of modern threats. They understand context. They can triage, reason, and act without needing a rigid playbook for every single “if-then” scenario. This allows them to adapt to novel threats and evolving TTPs in a way that static SOAR rules simply can’t.
A Phased Approach to a Future-Ready SOC
Let’s be clear: transitioning from a deeply embedded SOAR platform to agentic AI isn’t an overnight switch. You can’t just go cold turkey without risking operational chaos. This requires a deliberate, phased strategy – one that builds confidence and minimizes disruption.
Building Your Transition Timeline
Think of this as a strategic migration, not a sprint. Your timeline shouldn’t be dictated by a vendor’s product roadmap, but by your operational readiness and the confidence you build in the new tools. A basic structure might look something like this:
- Month 0-1: Inventory and Gap Analysis. Understand your SOAR’s current state and identify where agentic AI can deliver immediate value.
- Month 2-3: Shadow Deployments of Agentic Tools. Run the new AI alongside your existing SOAR. Let it learn your environment without impacting live operations.
- Month 4-5: Parallel Running & Pilot Use Cases. Introduce select, low-risk use cases to the agentic AI, allowing it to take action while SOAR still handles the bulk.
- Month 6+: Controlled Decommissioning. As confidence grows and the AI proves its efficacy, gradually phase out SOAR modules and playbooks.
This phased approach allows the new tools to prove themselves in your specific environment, building trust and minimizing risk every step of the way.
From Playbooks to Behaviors: Rewiring Your Response
SOAR thrives on explicit playbooks – step-by-step instructions for every conceivable scenario. Agentic AI, however, learns from behavior. To migrate effectively, you need to shift your mental model of documenting response processes.
Instead of writing “If alert A and IP B, then quarantine endpoint C,” start thinking in terms of analyst behavior: “When an analyst encounters X pattern, they typically check telemetry from Y, confirm via Z, then act.” This behavior-driven approach helps agentic systems build internal models of your analysts’ decision-making processes.
Companies like Prophet Security suggest starting with low-risk incidents. Observe and capture how your human analysts solve them. Then, test if the AI can replicate that problem-solving, without being explicitly told every single step. This iterative process allows the AI to learn and prove its capabilities before you escalate to high-risk, high-impact scenarios.
Empowering Your Team: The Human-AI Partnership
This isn’t about replacing your analysts with robots. It’s about arming them with a powerful co-pilot. Agentic AI should be seen as an augmentation, a force multiplier that frees your most valuable talent to focus on complex, strategic threats rather than repetitive tasks.
Keeping Control in Human Hands
Your SOC analysts shouldn’t be passive observers of the AI. They need to be active participants – guiding it, correcting it, and challenging its decisions. Build robust feedback loops into your processes:
- Can an analyst easily see *why* the AI chose a particular response?
- Can they ask the AI to explain its reasoning in natural language?
- Can they override or change the AI’s course if needed?
This isn’t just about fostering trust; it’s about maintaining accountability. Your security team remains accountable for every decision made, whether automated or not. Transparency and control are paramount.
Targeting the Right Use Cases
Not every SOAR use case needs a direct agentic twin. Some automations might be so simple they can be retired. Others need a significant upgrade. Start by identifying your team’s biggest pain points:
- Repetitive phishing triage.
- Alert deduplication across disparate tools.
- Log correlation that currently requires manual stitching.
Then, consider where humans are adding the most unique value today. That’s often where agentic AI can shine brightest. It excels not just at triggering a response, but at making the nuanced decision of *if* a response is needed at all, navigating the gray areas where static rules fail.
Beyond Integrations: Simpler Connections
One of SOAR’s initial selling points was its ability to integrate security tools. But the dirty secret was the cost: maintaining those integrations often became a job in itself. Custom scripting, API version changes, brittle connections – it was a never-ending battle.
Agentic systems operate differently. They don’t necessarily need every tool hardwired into a rigid workflow. They can often consume data via APIs, ingest logs, and learn to operate across silos more flexibly. When evaluating new AI tools, ask: “Can this work from the data I already collect?” and “Can it learn from my analysts without needing dozens of custom scripts?” If the answer is “no,” it might not be the right fit for a truly adaptive SOC.
Navigating the Cultural and Operational Realities
The technology shift is only half the battle. The real transformation happens at the cultural level. You’re not just moving from SOAR to AI; you’re moving from workflow execution to decision augmentation. This requires investment in your most critical asset: your people.
Upskilling Your Analysts for the AI Era
Your analysts need training, but not just on buttonology. They need to understand:
- How to interact effectively with agentic tools.
- How to validate the AI’s outputs and reasoning.
- How to “teach” the AI when it gets something wrong.
Invest in hands-on training that encourages experimentation. Let them explore, break things, and rebuild. The best AI-enhanced SOCs are those where humans and machines evolve together, constantly learning from one another.
Trust, But Verify: Observability and Off-Ramps
Agentic systems should earn your trust, not demand blind faith. If the AI starts making bad calls, you need to know immediately, and you need to be able to shut it down. Implement clear kill switches, robust audit trails, and comprehensive logs for every AI-driven action.
You need deep observability into its decision-making process. Understanding *why* the AI made a certain choice is just as important as the choice itself. This ensures accountability and allows for continuous improvement.
Honest Metrics for Real Progress
As you transition, resist the temptation to fudge the numbers to make the new tools look good. If your mean time to respond (MTTR) drops, celebrate it. If false positives spike, flag it immediately. Measure what truly matters:
- Hours saved by analysts on repetitive tasks.
- Incidents detected and contained earlier.
- Increased confidence in triage decisions.
Let the data speak for itself, and keep those metrics transparent and visible to the entire team. This reinforces trust and provides tangible proof of value.
Phasing out SOAR isn’t about burning bridges; it’s about building better, more resilient ones. It’s not trading automation for hype, but swapping rigid, static scripts for flexible, intelligent decision augmentation. Do it carefully. Do it transparently. And always keep your human analysts sharp and empowered.
Because at the end of the day, the SOC is still about making critical security decisions. Agentic AI just helps us make better, faster, and more informed ones. SOAR had its day. Agentic AI is here to stay. Phase it out like a pro: start slow, stay grounded, and never give up control.
