Notifications
Clear all
Topic starter
02/08/2026 9:45 am
TL;DR: SOC teams struggle to maintain complex SOAR playbooks, and the article argues that agentic AI SOC analysts can reason through investigations dynamically instead of following brittle if-then scripts, according to Prophet. Static automation handles repeatable tasks, but it breaks when attacks diverge from prewritten paths, making reasoning the real operational gap.
NHIMG editorial — based on content published by Prophet: SOAR Alternative: Why AI SOC Analysts Are Replacing Static Playbooks
Questions worth separating out
Q: Where do static SOAR playbooks fail in practice?
A: Static playbooks fail when an investigation requires judgment, adaptive branching, or evidence that was not anticipated in the original script.
Q: Why do SOC teams struggle to scale SOAR automation?
A: SOC teams struggle because every alert type needs a carefully designed decision tree, and those trees require constant tuning as tools, environments, and attacker behaviour change.
Q: How do AI SOC analysts improve investigation quality?
A: AI SOC analysts improve investigation quality by dynamically selecting evidence sources instead of following a fixed sequence.
Practitioner guidance
- Map high-friction playbooks to reasoning candidates Identify the investigations that require the most branching, manual tuning, or exception handling, then separate them from deterministic response tasks that still suit SOAR.
- Unify identity and endpoint telemetry for investigations Ensure authentication logs, privilege context, endpoint telemetry, and cloud audit trails are available in the same case workflow so analysts or AI systems can test whether a login anomaly aligns with real compromise.
- Measure queue pressure, not just alert volume Track how many cases are delayed because they require manual playbook maintenance, sequential approvals, or repeated enrichment steps.
What's in the full article
Prophet's full article covers the operational detail this post intentionally leaves for the source:
- The article’s full reasoning-led SOC comparison between static playbooks and agentic AI investigations.
- Operational examples of how AI analysts assemble case files from identity, endpoint, network, and cloud telemetry.
- The performance claims and operational outcomes tied to reduced alert backlog and faster triage.
- The role changes for junior and senior analysts as investigation workflows become more automated.
👉 Read Prophet's analysis of why AI SOC analysts are replacing SOAR playbooks →
AI SOC analysts vs SOAR playbooks: what changes for SOC teams?
Explore further
02/08/2026 12:37 pm
Static playbooks have become an operational debt problem, not a maturity marker. SOC teams often treat SOAR coverage as proof of automation maturity, but this article shows that the real cost is upkeep. When every investigative branch must be hand-mapped, the platform starts consuming engineering time that could have gone into detection engineering or response design. The practical conclusion is that playbook volume is not the same thing as operational resilience.
A question worth separating out:
Q: What should security teams do before replacing SOAR with agentic AI?
A: Security teams should first identify which workflows are genuinely deterministic and which depend on analyst judgment. They should also confirm that the necessary telemetry is available, normalized, and accessible across identity, endpoint, and cloud sources. Without that foundation, agentic AI will inherit the same blind spots that limited SOAR.
👉 Read our full editorial: AI SOC analysts are replacing SOAR playbooks in security operations
