What's the difference between SOAR and an AI SOC?
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Security orchestration, automation, and response (SOAR) tools were built to solve a real problem. Analysts were buried in repetitive work, so SOAR let teams encode that work as playbooks. When a known alert type fires, the playbook runs a fixed sequence of steps, such as enriching an IP address, checking a reputation feed, and opening a ticket.
An AI SOC takes a different approach to the same workload. Instead of following a script, AI agents reason through each alert using context about your environment and decide what to examine next based on what they have already found.
How SOAR playbooks work
SOAR automation is deterministic. Every path through a playbook has to be anticipated and written by an engineer in advance. That makes SOAR predictable and easy to audit, and it works well for tightly defined tasks like quarantining a known-bad file or disabling an account after a confirmed compromise.
The trouble starts with everything else. Investigations rarely follow the same path twice, and a playbook cannot handle a situation nobody wrote a branch for. Playbooks also break whenever a connected tool changes its API, which leaves security engineers maintaining integrations instead of improving detections. Over time many teams end up with a large library of brittle scripts and a queue of alerts that still need a human to investigate.
How an AI SOC works
An AI SOC uses AI agents to carry out triage, investigation, and response. When an alert arrives, the agent gathers relevant context, compares activity against what is normal for that user or resource, and reaches a verdict with the evidence attached. It can then recommend or take a response action, with humans keeping oversight of what happens.
The quality of that reasoning depends heavily on what the AI is reasoning over. Tools that point a single large language model at raw logs tend to produce inconsistent answers and grow expensive as data volumes rise. Platforms that resolve events against structured context first give their agents a much more reliable picture to work from.
The main differences
- Logic. SOAR follows a fixed playbook, while an AI SOC reasons over context and adapts to what it finds.
- Maintenance. SOAR playbooks need engineering time whenever a tool or process changes. AI agents tune themselves from analyst feedback.
- Scope. SOAR executes the steps a human designed. An AI SOC handles the judgment work of triage and investigation as well.
- Output. SOAR typically produces an enriched ticket for an analyst to review, whereas an AI SOC produces an explainable verdict.
Do you still need SOAR?
For many teams the answer is less than they have today. Exaforce, an agentic SOC platform, is often used to replace both SIEM and SOAR stacks, with task-specific AI agents called Exabots handling detection through response. Others keep a small set of SOAR automations for narrow, well-understood actions and move investigation work to AI agents. Either way, the shift frees analysts from maintaining scripts so they can spend their time on the threats that need human judgment.