Some security operations teams are stuck. It’s not that they’re doing anything wrong; their tech stack is just stuck in the past.
Security Orchestration, Automation, and Response (SOAR) was once the answer. Automate alerts. Speed up response. Limit fatigue. But it never lived up to its promise. Managed Detection and Response (MDR) providers filled some of the gaps, but they lack the organizational context to properly investigate and respond to threats.
Meanwhile, the world continued to change. Threat volume and complexity grew, leading to the deployment of new security tools that generated more alerts for the SOC. Recent advances in AI for security operations promise to change how SOCs operate. The rise of agentic AI, tools that reason, learn, and act, has allowed AI agents to take on the manual, repetitive tasks of triaging and investigating alerts.
The question now isn’t if you’ll replace SOAR. It’s how you’ll do it without breaking your SOC.
Here’s the playbook.
1. Know What You’re Replacing, And Why
Before you rip anything out, clarify what SOAR is doing today. Take inventory of every integration. Every playbook. Every alert it touches.
But don’t stop there. Ask what still works, and what doesn’t.
- Is your SOAR flooding the team with brittle automations?
- Are you writing custom scripts for every tool update?
- Are you still debugging the same workflows you built three years ago?
This isn’t about bashing SOAR. It did its job. But agentic systems (those that understand context, not just workflows) are built for the complexity of modern threats.
They can triage, reason, and act, without needing a playbook for every “if-then” path.
2. Build a Timeline That Doesn’t Kill Morale
You don’t switch from SOAR to agentic AI in a week. Or even a sprint. You phase. You prototype. You shadow-run. And you keep your analysts in the loop.
Set a timeline, but don’t tie it to a vendor roadmap. Tie it to operational readiness.
A basic structure:
- Month 0-1: Inventory and gap analysis
- Month 2-3: Shadow deployments of agentic tools
- Month 4-5: Parallel running of SOAR and AI
- Month 6: Controlled decommissioning of SOAR
Avoid going cold turkey; let the new tools prove themselves.
3. Stop Writing Playbooks. Start Mapping Behaviors.
SOAR lives on playbooks, Agentic AI learns from behaviors. To migrate, shift how you document response.
Instead of:
“If alert A and IP B, then quarantine endpoint C.”
Think in terms of: “When an analyst sees X pattern, they check telemetry from Y, confirm via Z, then act.”
This behavior-driven view helps agentic systems build internal models of your analysts’ decisions. You’re not feeding static instructions. You’re sharing context.
Start with low-risk incidents. Capture how humans solve them. Then test whether the AI can do the same, without being told every step, and finally escalate to high-risk, meaningful ones “without risky suppression.”
4. Keep the Humans in Control
Don’t view agentic AI as a self-driving car, but as a co-pilot.
SOC analysts shouldn’t just review what the AI does. They should guide it, correct it, and challenge it.
Build feedback loops:
- Can an analyst see why the AI chose that response?
- Can they ask it to explain its reasoning?
- Can they change its course if needed?
This isn’t just about trust, it’s about accountability. Security teams answer for the decisions made; automated or not.
5. Start with Use Cases That Matter
Not every SOAR use case needs an agentic twin. Some should just die off. Others need an upgrade.
Start with pain points:
- Repetitive phishing triage
- Alert deduplication
- Log correlation across tools
Then ask: “Where are humans adding most of the value today?”
That’s where agentic AI shines. It thrives in the gray area. It’s not about triggering a response.
It’s about deciding if a response is needed in the first place.
6. Don’t Let Integrations Drag You Back
One of SOAR’s main selling points was integration. It connected tools, it passed data. But it came at a cost. Maintaining those integrations was a job in itself.
Agentic systems work differently. They don’t need every tool hardwired in, they can consume APIs, ingest logs, and work across silos.
So don’t recreate the old spaghetti mess. Ask your AI:
“Can you work from the data I already collect?”
“Can you learn from the analysts without needing custom scripts?”
If the answer is no, it’s not the right tool.
7. Train Your Analysts, Not Just the AI
Tooling is only half the story. The real shift is cultural.
You’re not moving from SOAR to AI, you’re moving from workflow execution to decision augmentation.
Analysts need to know:
- How to interact with agentic tools
- How to validate their outputs
- How to teach them when they get it wrong
Invest in training, but make it hands-on. Let your team explore. Break things. Rebuild. Be directionally accurate rather than precisely wrong.
The best AI-enhanced SOCs are the ones where humans and machines evolve together.
8. Know When to Turn It Off
This one’s simple. If the AI starts making bad calls, shut it down.
You need kill switches, audits, and logs. You need observability into the decision-making. Agentic systems should earn trust, they don’t deserve blind faith.
9. Keep the Metrics Honest
Never fudge the numbers to make the new tools look good. If your response time drops, fantastic. If false positives spike, flag it.
Measure what matters:
- The hours analysts saved
- The incidents that were caught earlier
- More confidence in triage decisions
Let the data speak, and keep it visible.
Building Better Bridges
Phasing out SOAR isn’t about burning bridges, but about building better ones. Automation isn’t being traded for hype; rigid scripts are being traded for flexible intelligence.
Do it carefully. Do it transparently. And keep the humans sharp.
Because at the end of the day, the SOC is still about decisions. The machines just help us make better 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.
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