SOAR vs. Agentic AI: Why a Map Is No Longer Enough

Why Agentic AI is Replacing SOAR in the Modern SOC

In cybersecurity, speed is critical—but adaptability is decisive. For years, Security Operations Centers (SOCs) have leaned on SOAR (Security Orchestration,Automation, and Response) platforms to enforce speed by automating workflows. In its time, SOAR was revolutionary, bringing much-needed structure and reducing manual toil.

But it was built for a different era. Today, SOAR’s greatest strength—its rigid, playbook-driven nature—has become its fatal flaw.

Attackers don’t follow scripts. They improvise, exploit automation gaps, and pivot mid-attack. Static playbooks, no matter how meticulously crafted, are inherently brittle. They break when faced with the unknown. This is where Agentic AI enters the conversation. We're not talking about simple automation; we're talking about autonomous AI agents that can reason, plan, and act in real-time. Instead of blindly following a script, these agents assess novel situations, improvise solutions, and execute the mission.

Think of SOAR as a GPS following a pre-plotted route. Agentic AI is the autonomous vehicle that sees a sudden roadblock, analyzes all possible detours, and reroutes in a split second to still reach the destination safely.

The Breaking Point: SOAR's Limitations in a Dynamic Threat Landscape

The cybersecurity battlefield has evolved, but SOAR’s core logic has not. Modern SOCs face challenges that deterministic playbooks simply cannot handle:

• Novelty as a Weapon: With over 60% of malware being polymorphic (changing its code to evade detection), signature-based playbooks are consistently a step behind.
• Fragmented Data: Critical evidence is scattered across the SIEM, EDR, cloud, and identity platforms. A linear playbook cannot dynamically pivot between these sources to chase a lead.
• Complex Attack Chains: Modern attacks are multi-stage, cross-domain campaigns, not simple "if-this-then-that" events.

Case Study: A SOAR Playbook Failure Imagine a sophisticated phishing attack uses a novel fileless malware variant. The SOAR phishing playbook is triggered, but it hits an immediate wall. The attachment type is unknown, the file hash has no reputation in threat intelligence feeds, and the malicious behavior doesn't match a predefined signature.

The playbook halts. The automation stops. The incident now sits idle in a queue, waiting for an overworked human analyst to start a manual investigation from scratch—hours after the initial compromise. The attacker has a crucial head start.

The bottom line is that SOAR’s reliance on pre-defined rules makes it fragile. When confronted with novelty, it breaks.

The Next Evolution: What is Agentic AI in Cybersecurity?

Agentic AI introduces autonomous agents that can plan, reason, and adapt to achieve goals without pre-programmed scripts. They operate on a continuous loop, mimicking the cognitive process of an elite security analyst.

How It Works:

1. Perception: Ingests and interprets real-time data from all your security tools (SIEM, EDR, etc.).
2. Reasoning: Forms hypotheses about the threat, weighs the evidence, and decides on the best investigative path forward.
3. Action: Executes actions autonomously—from querying an endpoint for forensic data to isolating a host from the network.
4. Feedback: Monitors the outcome of its actions and adapts its strategy in real-time based on the new information it uncovers.

An agent doesn’t crash when data is missing; it reasons about what data it needs and actively seeks it out. It can dynamically pivot an investigation from a network log to endpoint telemetry to an identity provider, following the evidence wherever it leads.

SOAR vs. Agentic AI: A Head-to-Head Comparison

A Practical Strategy: Building the Hybrid SOC of the Future

The goal isn't to rip and replace your entire security stack. A pragmatic, hybrid approach offers the best of both worlds.

• Where SOAR Still Works Best: Let SOAR handle the high-volume, low-complexity tasks it was built for. Think password resets, phishing report triage, and blocking known-bad IOCs. This frees up resources and maintains efficiency for routine operations.
• Where Agentic AI Shines: Deploy Agentic AI to tackle the most complex and ambiguous threats—the ones that would normally consume days of your senior analysts' time. This includes investigating novel malware, insider threats, and multi-stage intrusions.

By addressing potential objections and risks head-on, you can implement this technology confidently. Concerns like AI "hallucination" and auditability are solved with a human-in-the-loop model, where every action taken by the AI is logged for review and critical decisions can require human approval.

The future of SOC automation is a move away from rigid playbooks and toward adaptable reasoning systems. We predict that Agentic AI will absorb much of SOAR's core functionality within the next 3-5 years, but human oversight will always remain essential for governance, strategy, and trust.

Conclusion

SOAR brought the power of rules-based automation to the SOC, and for that, it was invaluable. But today’s threat landscape demands more than rules; it demands reason. Agentic AI provides the critical ability to think, adapt, and act in the face of uncertainty.

If your SOC is still relying solely on SOAR, you are fighting a modern, adaptive adversary with a static, outdated map. It’s time to start auditing your workflows for brittleness and piloting Agentic AI for your most critical incidents.

FAQ

Q1: How is Agentic AI fundamentally different from SOAR? SOAR executes static, pre-defined playbooks based on "if-this-then-that" logic. Agentic AI uses a dynamic reasoning engine. It can handle unseen variables, adapt its investigation path in real-time, and make decisions without a pre-written script.

Q2: Will Agentic AI replace SOAR entirely? Not immediately. The most effective model is a hybrid one. SOAR excels at repetitive, predictable tasks, while Agentic AI is designed to handle novel, high-risk incidents. In the future, agentic platforms will likely incorporate SOAR's capabilities.

Q3: How does Agentic AI handle a completely new or unexpected threat? Instead of stopping, it begins an investigation. It enriches data from multiple sources (EDR, SIEM, threat intel), forms hypotheses based on observed behaviors (like MITRE ATT&CK TTPs), and dynamically updates its response as it uncovers more context.

Q4: What are the primary limitations of SOAR that Agentic AI solves? SOAR’s main limitations are its brittleness when facing novel threats, the high maintenance burden of constantly updating playbooks, and its inability to adapt an investigation once a playbook is running. Agentic AI is designed specifically to solve these problems through adaptability and autonomous reasoning.

Q5: How can we ensure Agentic AI is safe and trustworthy for critical security tasks? Trust is built through transparency and control. Reputable agentic platforms operate with strict guardrails, including comprehensive audit logs of every action taken, human-in-the-loop approvals for critical actions (like quarantining a server), and models that are specifically trained on cybersecurity data to ensure relevance and accuracy.