What is a SOAR alternative for modern security operations?

The leading SOAR alternative is an AI SOC analyst—an agentic AI platform that reasons through security investigations dynamically instead of following static, pre-built playbooks. Unlike SOAR, which requires manually mapped decision trees for every alert type, an AI SOC analyst evaluates alert context in real time, queries data sources autonomously, and produces complete investigation case files without playbook maintenance overhead.

What is an AI SOC analyst and how does it work?

An AI SOC analyst is an agentic AI system that performs security alert triage and investigation using a reasoning engine rather than scripted playbooks. When it receives an alert, it dynamically builds an investigative plan, queries data across the security stack—SIEM, EDR, identity providers, cloud audit trails—correlates evidence, and delivers a complete case file with context, evidence, and a recommended disposition, mirroring the workflow of an experienced Tier 2 analyst.

How does an AI SOC analyst differ from a SOAR platform?

SOAR platforms execute deterministic if-then playbooks that must be manually built and maintained for every alert type and investigation path. An AI SOC analyst replaces this static logic with dynamic reasoning—it evaluates evidence, adjusts its investigative approach mid-analysis, and handles scenarios no one has written a playbook for. SOAR automates the 'doing' (running queries, checking reputation feeds); an AI SOC analyst also automates the 'thinking' (correlating anomalies, determining blast radius, weighing ambiguous evidence).

Why do SOAR playbooks fail in real-world SOC environments?

SOAR playbooks fail for three main reasons. First, they require exhaustive manual mapping of every investigation path—a single mature phishing playbook can exceed fifty discrete steps. Second, attackers do not follow static decision trees; when threats pivot to novel techniques, playbooks stall and alerts revert to human queues. Third, most organizations lack the specialized skills to build and maintain playbooks at scale, so the majority of alert types remain unautomated.

Can AI replace Tier 1 SOC analysts?

AI SOC analysts do not eliminate Tier 1 roles—they transform them. Instead of performing repetitive, mechanical triage like copying IOCs between tabs and running identical queries, junior analysts shift to reviewing complete AI-generated investigations, validating conclusions, and flagging edge cases. This accelerates professional development because analysts learn investigative reasoning from day one rather than spending months on rote tasks that drive high turnover.

How does an AI SOC analyst reduce Mean Time to Respond (MTTR)?

An AI SOC analyst reduces MTTR by correlating data across the entire security stack—SIEM, EDR, identity, network, and cloud telemetry—in seconds rather than waiting for sequential playbook steps or human handoffs between tiers. Organizations deploying agentic AI analysts report MTTR reductions from hours to single-digit minutes because the AI investigates every alert immediately and in parallel, with no queue wait time.

What happens to SOC team roles when an AI analyst is deployed?

Deploying an AI SOC analyst restructures team roles for the better. Junior analysts move from mechanical triage to investigation review and validation—work that builds real skills. Senior analysts and threat hunters reclaim time lost to queue management and refocus on proactive threat hunting, detection engineering, and strategic capability planning. The net effect is improved retention, faster skill development, and a stronger overall security posture.

How does an AI SOC analyst handle novel or zero-day attacks without a playbook?

Because an AI SOC analyst reasons through evidence dynamically rather than following a pre-scripted path, it can investigate attack patterns that no playbook has been written for. It evaluates contextual signals—anomalous process chains, unusual authentication patterns, lateral movement indicators—and correlates them across data sources in real time. This is the critical capability gap that SOAR, which requires a pre-defined branch for every scenario, was never designed to close.

What is the true cost of SOAR playbook maintenance?

Even a small SOC typically dedicates at least one full-time security engineer—sometimes more—exclusively to building, testing, debugging, and updating SOAR playbooks. This headcount produces zero direct security value because the engineer is maintaining automation infrastructure rather than investigating threats. When combined with licensing costs and the technical debt of hundreds of branching playbooks that break whenever the environment changes, SOAR's total cost of ownership far exceeds initial projections.

SOAR Alternative: Why AI SOC Analysts Are Replacing Static Playbooks

Q: What is the difference between SOAR automation and AI-based reasoning in security operations?

SOAR automation handles 'doing' tasks—mechanical, repeatable actions like querying a SIEM, checking IP reputation, or disabling a user account. AI-based reasoning handles 'thinking' tasks—correlating an anomalous login with a concurrent endpoint process, deciding whether a DNS pattern indicates beaconing or a misconfigured application, and determining the blast radius of a compromised credential. SOAR automates individual steps; AI reasoning automates the entire investigative workflow including judgment and contextual analysis.

If you have run a security operations center for any length of time, you already know the pitch: deploy SOAR, automate your playbooks, and watch your alert backlog disappear. The reality is different. Most SOC teams that invested heavily in Security Orchestration, Automation, and Response platforms have never managed to build and optimize the necessary playbooks to cover their use cases due to the complexity involved in that process.

More recently, the industry started moving toward a fundamentally different model: agentic AI SOC analysts that reason through investigations instead of executing static scripts.

This is a structural change in how security operations work.

The Technical Debt Problem Nobody Budgeted For

SOAR platforms run on deterministic if-then logic. Every investigative path for every alert type must be manually mapped by a security engineer. A mature phishing playbook alone can require fifty or more discrete steps: header parsing, URL reputation lookups, sandbox detonation, mailbox sweeps, and user notification workflows. Multiply that by every alert category your SOC handles, and you are looking at hundreds of branching playbooks that need constant upkeep.

Unfortunately, attackers do not follow your decision trees. When a threat actor pivots from a known malicious domain to a legitimate file-sharing service for payload delivery, there may not be a branch for that in the playbook. The automation stalls, the alert lands back in a human queue, and you have gained nothing.

Industry data backs this up. Surveys consistently show that the majority of security teams find SOAR too complex and time-consuming to maintain at scale. From what we’ve seen, even a small-sized SOC ends up dedicating at least one full-time engineer — sometimes more — purely to playbook maintenance rather than actual threat investigation. That is an expensive headcount line item producing zero direct security value. The automation platform itself becomes legacy infrastructure, and you are now managing technical debt on top of your original alert problem.

Generic Engines vs. Embedded Expertise

The number of required playbooks is not the only issue affecting organizations that invested in SOAR. The playbook development concept leaves the burden of the “know how” of security investigations entirely in the hands of the users. Writing playbooks means telling the SOAR machine exactly what it needs to do for each step of an investigation. Many organizations were not able to develop those playbooks, or created overly simplistic versions, because of a gap in skills and knowledge.

SOAR vendors usually address this issue by mentioning vast “libraries” of playbooks that customers can adopt to accelerate deployment and time to value. However, these playbooks are rarely useful and almost never in a “ready to use” state. Each organization has their own combination of technologies and services the playbooks need to use as part of the investigation process, so adopting a pre-built playbook means replacing and adapting all pieces that are not aligned with their environment. In practice, most SOAR users will look at those playbooks for reference only, reverting back to the “create from scratch” approach.

Why Playbooks Fail: The "Doing" vs. "Thinking" Gap

SOC work breaks into two categories. "Doing" tasks are mechanical and repeatable: query a SIEM, check an IP reputation, disable a user account. SOAR handles these well. "Thinking" tasks require judgment: correlating an anomalous login with a concurrent endpoint process, deciding whether a DNS pattern indicates beaconing or a misconfigured application, determining the blast radius of a compromised credential.

Static playbooks cannot think. They cannot weigh ambiguous evidence or adjust their approach based on what they find mid-investigation. When a playbook encounters a scenario it was not designed for — and in a real SOC, this happens constantly — the investigation dead-ends and reverts to a human analyst. This is the automation gap that keeps Tier 1 analysts buried in repetitive triage despite having an automation platform deployed.

The result is a SOC that has automated some of the easy parts and left the hard parts exactly where they were.

How AI SOC Analysts Actually Work

An AI SOC analyst operates on a reasoning layer rather than a scripted decision tree. The distinction matters because it changes what the system can handle without human intervention.

When an agentic AI analyst receives an alert, it evaluates the alert context and builds an investigative plan dynamically. Take a login anomaly as an example. A static playbook would check the IP against a threat feed and stop. An AI analyst asks a series of contextual questions: Has this user authenticated from this geography before? What processes executed on the endpoint around the time of login? Did any DLP or CASB policies trigger in the same window? Is there lateral movement in the network telemetry?

This mirrors the workflow of a skilled Tier 2 analyst. The agent synthesizes unstructured data from across the security stack — identity provider logs, EDR telemetry, network metadata, cloud audit trails — and produces a cohesive investigation narrative. The output is not a raw alert with a severity score. It is a complete case file with evidence, context, and a recommended disposition.

Because the system reasons through evidence rather than following a script, it handles novel attack patterns that no one has written a playbook for. That is the capability gap SOAR was never designed to close.

Measurable Outcomes That Matter to the Business

The shift from playbook-based automation to reasoning-based investigation produces operational improvements that translate directly to metrics your board and executive team care about.

Mean Time to Respond (MTTR) compresses dramatically. AI agents correlate data across the security stack in seconds rather than waiting for sequential playbook steps or human handoffs. Organizations deploying agentic AI analysts are reporting MTTR reductions from hours to single-digit minutes for the majority of alert types.

Alert coverage goes from partial to comprehensive. Every SOC leader knows the dirty secret of triage: analysts prioritize based on perceived severity and available time, which means low-and-medium-priority alerts often go uninvestigated. An AI analyst has no capacity constraint. It investigates every alert, which matters because sophisticated attackers deliberately generate low-severity signals that blend into noise.

False positive resolution improves because the AI gathers full context before making a determination. By autonomously enriching alerts across multiple data sources, AI analysts can confidently dismiss the vast majority of false positives — some teams report up to 90 percent — before a human ever touches the case. That is the single biggest lever for reducing costs as well as analyst burnout and attrition.

What This Means for Your SOC Team

This is the part that matters most to anyone managing people. Deploying an AI SOC analyst restructures the analyst role in a way that is better for retention, skill development, and overall security posture.

In the traditional tiered model, junior analysts spend most of their time on mechanical triage — copying IOCs between tabs, running the same queries, closing the same false positive patterns. It is tedious work, and it is why SOC analyst turnover is notoriously high.

In a reasoning-led SOC, junior analysts shift from performing rote triage to reviewing complete investigations produced by the AI. They validate conclusions, flag edge cases the model may have misjudged, and escalate genuine incidents with full context already assembled. This accelerates professional development significantly because analysts learn investigative logic rather than tool mechanics from day one.

Senior analysts and threat hunters reclaim time currently lost to queue management. They focus on proactive threat hunting, detection engineering, and strategic capability planning — the high-impact work that actually improves your security posture over time.

The Bottom Line

SOAR was the right idea at the wrong level of abstraction. Automating individual actions was a necessary step, but it was never going to solve the hard part of security operations: the reasoning.

AI SOC analysts do not patch the gaps in your playbooks. They replace the playbook model entirely with a reasoning engine that scales with your environment. Request a demo of Prophet Security to see how the Prophet Agentic AI SOC Platform can help your team triage, investigate, and respond to all of your alerts.

A Buyer's Guide to AI SOC Analysts

Your definitive guide to evaluating AI SOC solutions

Download eBook

Download Ebook

Frequently Asked Questions

Insights

Text Link

Ajmal Kohgadai

AUTHOR

As the Director of Product Marketing at Prophet Security, Ajmal drives marketing and growth strategies and helps security professionals see how AI is transforming security operations.