The Difference Between SOAR and AI SOC: From Playbook Automation to Agent-Led Autonomous Investigation

"Even though we implemented SOAR, humans are still looking at most of the alerts." "We are so busy tweaking playbooks that we don't have time for new automation." We are hearing these complaints more and more in security operations. SOAR (Security Orchestration, Automation and Response) has spread as a mechanism to automate routine tasks and reduce the load on the SOC (Security Operation Center), but many organizations have learned from experience that its effects tend to be limited to the "pre-written procedures."

In this article, we will define SOAR and outline its strengths, explain its structural limitations, and explore how "autonomous investigation" by AI agents differs from SOAR and how they should be used together, from the perspectives of migration steps and governance. The goal is not to compare specific products, but to show how to progress operational automation from "procedure automation" to "autonomous investigation and decision-making." The overall concept of the AI SOC is explained in "What is an AI SOC? How it works, differences from traditional SOCs, and benefits of adoption."

What is SOAR: Definition, 3 Elements, and Background

SOAR refers to a framework or product category that orchestrates multiple security tools, automates repetitive tasks, and manages incident response within a single platform. While SIEM (Security Information and Event Management) plays the role of "collecting logs and detecting threats," SOAR emerged and spread in the late 2010s to handle "what to do after detection."

The background is driven by three challenges: SIEM and EDR (Endpoint Detection and Response) alerts exceeding human processing capacity, a growing number of operational tools requiring analysts to waste time switching screens, and response quality relying heavily on individual experience, necessitating standardization and documentation. NIST's incident response guide SP 800-61 Rev.3, published in April 2025, lists SIEM and SOAR as examples of tools for continuous monitoring of known malicious or suspicious activities (NIST, 2025). SOAR is now positioned as a standard operational component.

The 3 Elements: Orchestration, Automation, and Response

Orchestration is the ability to call APIs of different products to gather information and issue commands. For example, it executes a single query flow that queries threat intelligence for an IP address in an alert, retrieves endpoint info from EDR, and pulls user department data from an identity provider. Automation executes this flow without human intervention based on predefined conditions. Response refers to actions taken on the environment, such as isolating endpoints, disabling accounts, quarantining emails, or raising tickets. Together, these three elements allow machines to handle part of the "detect → gather information → prepare decision materials → initial response → record" workflow.

Playbooks and Case Management

The core of SOAR is the "playbook." This is a defined workflow of response and remediation steps and branching logic executed when a specific alert or incident occurs. Public documentation for SIEM products with integrated SOAR features describes playbooks as a "collection of response and remediation actions and logic that can be run as a routine," stating the primary goal of SOAR is to "automate repetitive, predictable enrichment, response, and remediation tasks to free up time and resources for deep investigation and hunting of advanced threats" (Microsoft Learn, 2026). "Repetitive and predictable" tasks are the targets of SOAR; conversely, unpredictable events are out of scope from the start.

Another pillar is case management. It tracks status, owner, severity, tags, and tasks for each incident, recording who did what and when. The same documentation explains that tasks can be added, status or severity changed, owners assigned, and tags applied using automation rules alone without playbooks (Microsoft Learn, 2026). While not flashy, this is practically one of the most valuable parts for audit readiness, handovers, and maintaining consistent response quality.

What SOAR Excels At

• Executing routine tasks: Querying reputation for IPs or hashes, user/device enrichment, raising tickets, sending notifications, and isolating devices or disabling accounts under clear conditions.

• Tool integration: Connecting EDR, SIEM, IAM, email security, and ticketing systems via APIs to reduce screen switching.

• Documentation and standardization: Automatically logging actions and establishing standard response procedures across the organization.

All of these are tasks where the "procedures are predetermined." In a 2023 survey of 900 security practitioners, 25% said they spend "more than half of their working hours on tedious manual tasks," and 93% believed that "increased automation would improve their work-life balance" (Tines "Voice of the SOC", 2023). The operational demand to reduce manual labor is clear, and SOAR has been embraced as a technology to meet it.

The Limits of SOAR: 4 Reasons Playbook-Centric Automation Stalls

This section does not highlight flaws in specific products, but rather the general constraints of the "pre-write and execute procedures" approach itself.

1. High Cost of Playbook Creation and Maintenance

Playbooks must be designed, implemented, and tested for each alert type and integration. If an integration changes its API spec, the playbook needs tweaking; if a detection rule is added, a corresponding playbook must be created. Generally, the scope of SOAR automation is proportional to the number and quality of playbooks an organization can write and maintain. However, writing playbooks requires talent that understands both SOC operations and development—a scarce resource in most enterprises. As a result, initial playbooks are left un-updated, and automation progress plateaus.

2. Vulnerability to the Unexpected

Because playbooks are written as "if this condition, run this procedure," they cannot handle unexpected events like alerts that don't fit the conditions, multi-vector attacks, or living-off-the-land techniques using legitimate tools. While the MITRE ATT&CK Enterprise matrix organizes observed attacker techniques into 15 tactics (MITRE ATT&CK v19.2, 2026), the combinations are vast, making it unrealistic to write playbooks for each one. With new AI-related cyber risks rapidly emerging, the speed of attack evolution will only accelerate.

3. Decisions Still Require Humans

SOAR executes procedures but does not make decisions like "Is this alert a true threat?" "What is the scope of impact?" or "Is it safe to isolate this device now?" Analysts must make the final call. SOAR shortens the tasks before and after the decision, but the core issue of decision time and the shortage of qualified decision-makers remains. The structured bottleneck of alert backlogs and analyst burnout cannot be solved by SOAR alone.

4. Operational Load After Deployment is Higher Than Expected

SOAR is not a set-it-and-forget-it tool; it requires continuous tuning, false positive adjustments, integration updates, and review of execution results. Playbooks that isolate devices or disable accounts must have highly restrictive trigger conditions to avoid disrupting business. This creates a constant trade-off between expanding automation and managing false-positive risks. Sustaining SOAR typically requires dedicated staff, which often contradicts the initial goal of reducing headcount.

How Autonomous Investigation by AI Agents Differs

AI agents refer to software built around a Large Language Model (LLM) that determines its own next steps to achieve a goal. It queries SIEM, retrieves endpoint data via EDR, adjusts its actions based on results, and reports conclusions with evidence. The fundamental difference from SOAR is whether humans write the "procedure" or provide the "goal."

Writing Procedures vs. Providing Goals

A SOAR playbook requires pre-defined steps: "If a suspicious login alert occurs, query the IP, search the user's logins for the past 24 hours, and attach results to the ticket." An AI agent is given a goal: "Determine if this alert is a true threat and provide the evidence." The agent designs the investigation dynamically, checking IP reputation, analyzing user behavior, tracing process execution history, and finding correlations. Because procedures are not fixed, you don't need playbooks for every alert type, and new detection rules do not require additional implementation on the investigation side.

This shift changes the nature of initial triage. While playbooks stop at "gathering materials for a human," the agent gathers materials and makes an initial determination: "This is a false positive" or "This requires deeper investigation," explaining its reasoning. For more on automated triage, see "Automating Alert Triage: Initial Investigation Steps and Accuracy Assurance with AI Agents."

Handling Non-Routine Alerts

AI agents excel at handling the "unexpected" scenarios where SOAR struggles. Even with undefined alerts, they formulate investigation plans using general knowledge of attacker behavior and organizational baselines. For example, for a late-night execution of an admin tool, the agent correlates the endpoint user, recent authentication, and similar behavior on other devices to evaluate the context. This holistic approach is also discussed in "Why Log Correlation Analysis Matters." However, handling non-routine alerts does not mean being 100% accurate. Because agent decisions can contain errors, system design must verify decisions and detect mistakes while scaling the investigation.

Designing for Explainability

SOAR playbooks naturally document execution steps, making them easy to explain. Because AI agents take different paths each time, explainability must be intentionally designed. Practically, investigation outputs should include:

• Conclusion and Confidence: Is it a true threat or false positive, and with what confidence level?

• Supporting Evidence: Logs used, queries run, and endpoint/user information referenced.

• Mapping to Attack Frameworks: Mapping observed behaviors to MITRE ATT&CK tactics and techniques, providing a shared vocabulary for humans and AI (MITRE, 2026).

• Separation of Proposed vs. Executed Actions: Clearly distinguishing what was recommended from what was actually run.

This output shifts the analyst's role from starting investigations from scratch to reviewing agent conclusions. This concept of augmenting rather than replacing analysts is detailed in "The Era of the AI Agent SOC: Augmenting Analysts, Not Replacing Them."

The Hybrid Model: SOAR as the Execution Engine, AI Agents as the Investigation Layer

SOAR and AI agents are not mutually exclusive; they are complementary, making a hybrid model the most realistic approach. The core concept is "SOAR for execution and record-keeping, AI agents for investigation and initial triage." When an alert fires, the agent autonomously investigates and outputs a conclusion, impact scope, and recommended actions. Based on this, high-impact response actions like device isolation are executed by SOAR playbooks, which have established permissions and audit logging. Case management remains on the SOAR side, with the agent's findings attached to the case.

This division has three benefits: it protects existing SOAR investments and integrations; it centralizes response execution in SOAR for simplified access control and auditing; and it resolves the human decision bottleneck by providing rapid initial agent triage. From a SOAR perspective, this extends playbook triggers from "after human review" to "after agent validation with evidence." This setup also integrates well with outsourced MSS or MDR. For details on how different service models compare, see "MDR vs. MSS vs. XDR vs. AI SOC: How to Choose Security Operations Services."

5 Steps to Migration

1. Assess the Status Quo: Map alert volume by type, percentage of manual investigations, automated playbook coverage, and average investigation times. The volume of alerts waiting for human triage determines the potential ROI of an AI agent.

2. Read-Only Parallel Run: Grant the agent read-only access to SIEM and EDR to run investigations in parallel with existing operations. Analyze agreement rates and the causes of discrepancies with human decisions.

3. Delegate Initial Triage: Integrate the agent's initial triage into formal operations, starting with alert types that meet accuracy and explanation standards. Sample and review false positive verdicts, and have humans review true positive verdicts to decide on actions.

4. Connect Responses: Create paths to trigger SOAR playbooks based on agent verdicts. Start with human-in-the-loop approval and expand auto-execution to low-impact, reversible actions like raising tickets or gathering extra logs.

5. Continuous Evaluation and Improvement: Monitor MTTR, investigation rates, and false-positive rates to feedback error corrections. As NIST SP 800-61 Rev.3 notes, lessons learned should be shared as identified rather than waiting for post-incident reviews (NIST, 2025), which applies directly to optimizing AI agents.

Steps 2 and 3 are critical during the PoC (Proof of Concept) phase of evaluating AI SOC products. Key evaluation criteria are detailed in "How to Choose an AI SOC: Evaluation Criteria for PoCs and Steps for a Successful Deployment."

Risks and Governance: Human Approval, Privilege Separation, and Audit Logs

Operating AI agents carries specific risks, such as missed threats or over-responses due to incorrect decisions, unintended operations, and lack of post-hoc explainability. Gartner predicts that "over 40% of agentic AI projects will be canceled by the end of 2027" due to rising costs, unclear business value, or poor risk governance, warning against "agent-washing" where legacy products are simply rebranded (Gartner, 2025). However, IBM's "Cost of a Data Breach Report 2026" shows that while average breach costs rose 12% to $4.99 million, organizations utilizing AI and automation extensively saved an average of $1.93 million (IBM, 2026). Avoiding AI due to risk or bypassing governance for speed are both flawed approaches. Organizations must focus on three core controls:

Human-in-the-Loop Approval

Irreversible or high-impact actions (isolating production servers, disabling privileged accounts) must follow the principle of "agent proposes, human approves." Fully automated actions should be restricted to low-risk, reversible activities defined in writing. NIST SP 800-61 Rev.3 suggests considering automated containment, like malware isolation, using security technologies (NIST, 2025), but this does not imply fully automating response. Establishing clear boundaries for automated vs. approved actions is the foundation of governance.

Privilege Separation

Separate "investigation" privileges from "response" privileges. Because investigation relies on reading logs and telemetry, read-only access is sufficient. Keep response execution privileges restricted to the SOAR execution engine, leaving the agent in a position to only "request" actions. Even in SIEM-integrated SOAR, roles for manually running playbooks, running them via automation rules, and linking playbooks to rules are strictly separated (Microsoft Learn, 2026). Design agent permissions based on this principle of least privilege, which is critical for compliance and vendor risk management in highly regulated sectors like finance and defense.

Audit Logs

Store agent queries, referenced data, decisions, recommended responses, and human approval histories in a tamper-resistant format. Integrating these with SOAR case management allows tracing human and AI actions in a single timeline per incident. Audit logs are vital not just for post-incident accountability but also as raw data for tuning and correcting decision logic.

Yagura AI SOC Positioning

Yagura AI SOC is designed as an AI agent that covers this "investigation and decision-making layer." Replicating the methodologies of elite analysts, the AI agent autonomously investigates and responds to EDR and SIEM alerts 24/7/365. It integrates with over 100 security products, including EDR, SIEM, and IAM systems. As a proprietary service, it integrates with existing products like Microsoft Sentinel, allowing you to deploy it while leveraging your existing SIEM and SOAR investments. It also features context memory to learn your specific environment, improving investigation accuracy over time.

When comparing performance, measure tasks saved by SOAR's procedure automation separately from tasks saved by AI's investigation and triage support. Yagura AI SOC aims to alleviate analyst burden by automating alert investigations. Test investigation coverage, manual analysis time, re-investigation overhead, and pending approval times in your environment to verify how combining Yagura with your existing SOAR optimizes overall operations.

Summary

SOAR remains a valuable foundation for routine task automation, tool integration, and incident record keeping. Its limits—playbook maintenance costs, vulnerability to unexpected events, dependence on human decisions, and high operational overhead—all stem from the "pre-write procedures" approach. Autonomous investigation by AI agents differs from SOAR by taking goals rather than procedures, allowing them to handle non-routine alerts and perform initial triage. However, because decisions can contain errors, they must be designed with strong governance controls: explainability, human approvals, privilege separation, and audit logs.

The most practical path forward is a hybrid model that uses SOAR as the execution and recording engine and layers AI agents on top for investigation and decision-making. Starting with a read-only parallel run and gradually expanding delegation based on accuracy and explainability allows organizations to protect existing investments while solving the primary bottleneck of security operations: human decision-making.

Related Service: Click here for details on "Yagura AI SOC," where AI agents autonomously investigate and respond to EDR and SIEM alerts 24/7/365. We can also help design architectures that automate your investigation and decision layer while leveraging your existing SOAR and SIEM investments.

References & Sources

• Microsoft Learn "Automation in Microsoft Sentinel" (2026): https://learn.microsoft.com/en-us/azure/sentinel/automation/automation

• Microsoft Learn "Automate threat response with playbooks in Microsoft Sentinel" (2026): https://learn.microsoft.com/en-us/azure/sentinel/automation/automate-responses-with-playbooks

• Microsoft Learn "Automate threat response in Microsoft Sentinel with automation rules" (2026): https://learn.microsoft.com/en-us/azure/sentinel/automate-incident-handling-with-automation-rules

• NIST SP 800-61 Rev. 3 "Incident Response Recommendations and Considerations for Cybersecurity Risk Management: A CSF 2.0 Community Profile" (2025): https://csrc.nist.gov/pubs/sp/800/61/r3/final (PDF: https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-61r3.pdf)

• MITRE ATT&CK (v19.2, 2026): https://attack.mitre.org/ (Enterprise Tactics: https://attack.mitre.org/tactics/enterprise/ / Versions: https://attack.mitre.org/resources/versions/)

• Gartner "Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027" (2025): https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027

• IBM "Cost of a Data Breach Report 2026" (2026): https://www.ibm.com/reports/data-breach

• Tines "Voice of the SOC 2023" (2023): https://www.tines.com/reports/voice-of-the-soc-2023/

• IPA "10 Major Information Security Threats 2026" (2026): https://www.ipa.go.jp/security/10threats/10threats2026.html