Don’t Settle for an AI SOAR: The Case for Autonomous SOC Operations
Security teams lose time on work that never ends. Building automations. Fixing automations. Explaining why automations failed.
The market has a tidy story for that pain. Take a SOAR or hyperautomation platform. Add a chat interface.
Don’t Settle for an AI SOAR: The Case for Autonomous SOC Operations
Security teams lose time on work that never ends. Building automations. Fixing automations. Explaining why automations failed.
The market has a tidy story for that pain. Take a SOAR or hyperautomation platform. Add a chat interface. Call it an “AI SOC.” The interface feels modern, but the operating model stays rooted in the past. It is a natural language abstraction layer over the same old static workflow orchestration playbooks and brittle integrations.
A chat layer can help you write a playbook faster. It does not change the fact that you still need to build, maintain, and fix that playbook.
The Core Problem: Static Playbooks Wired to Dynamic Environments
“AI SOAR” platforms work when a human team translates intent into workflow. You define use cases, map them into workbooks, then build playbooks tied to those workflows. It is a development process.
That process collides with the way modern incidents unfold.
The SOC has two clocks running at once. One clock measures attacker time in the environment: a global median dwell time of 11 days where narrative and correlation matter. The other measures the time it takes to build and keep automation working.
If it takes you a month to build coverage for a tier of alerts, a common benchmark for “fast” SOAR deployments, you are already behind. Playbook-driven automation struggles on that second clock.
The Four Burdens of the Chat-Based AI SOAR
A rebranded SOAR with a chat interface still asks your team to do four kinds of work that autonomy should eliminate.
1. The “Wrapper” Work (Architecture) AI SOARs are often just wrappers, generic LLMs bolted onto a legacy SOAR chassis. They rely on a Workflow-native architecture. This means the AI is waiting for a human to design the logic path. If the playbook doesn’t exist, the AI can’t act.
Why this sucks: You are still building agents and runbooks. It’s the same “engineering phase” disguised as a conversation.
2. Integration Maintenance (The Silent Killer) Enterprises run sprawling toolchains. APIs change. Auth rotates. Event fields drift. In a traditional AI SOAR, if your EDR or Identity vendor changes an output format, your playbook breaks and unprocessed alerts pile up.
Why this sucks: Self-healing integrations are already here. True autonomy detects API drift and schema changes, then generates corrective code without human intervention. If your AI SOAR can’t fix its own connections, your engineers are going to
3. The Quality Gap (L1 vs. L2) Most AI SOARs are stuck at L1 Triage. They can classify and route, but their investigation depth is bounded by the logic encoded in a pre-built template.
Why this sucks: You need L2-Equivalent Investigation. This requires a capability like Attack Path Discovery in Morpheus, which traces correlations horizontally across tools and vertically through time, for example, finding lateral movement + privilege escalation combinations, regardless of whether a playbook or incident type exists.
4. The Model Lock-In Many AI SOARs rely on proprietary, “black-box” AI systems or generic wrappers.
Why this sucks: You need transparency. Security demands explainability. You should be able to bring your unique tribal knowledge and SOC practices, and meet governance and data residency requirements.
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True Autonomy Carries the Burden
Real autonomy executes work without requiring a human to script it first.
Morpheus replaces the “chat-to-build” model with an Alert-Native architecture. It ingests an alert, analyzes the full context, and generates a bespoke playbook at runtime.
Dynamic Playbook Generation Morpheus generates a unique investigation path for every specific threat. This removes the need for a playbook engineering phase entirely, because a playbook is generated as a byproduct of the autonomous investigation. Connect your alert sources, and the system begins producing investigations immediately, without a library of pre-built templates.
Self-Healing Integrations The system autonomously detects API drift, schema changes, and output shifts, generating corrective code instantly. This capability eliminates the “silent failure” mode common in runbook-first stacks and removes the integration maintenance tax that consumes engineering resources.
Elite-level Investigation Morpheus performs Attack Path Discovery, tracing correlations horizontally across tools and vertically through time-series data. By mapping entity relationships and building a coherent threat narrative, the system mirrors the investigative depth of an experienced L2 analyst rather than a scripted bot.
Purpose-Built Intelligence The platform runs on a cybersecurity triage LLM developed over 24 months by 60 specialists, including red teamers, data scientists, ai experts, and SOC analysts. This model is purpose-trained to understand attack patterns and lateral movement, distinct from generic LLMs wrapped in security prompts.
Sovereignty and Governance Open YAML, Git-based change control, and the ability to swap LLMs ensures you are never locked into a vendor’s self-serving roadmap. Morpheus allows enterprises to operationalise their SOPs and tribal knowledge, ensuring compliance with data residency requirements.
Evaluation Criteria
D3 Morpheus
AI SOAR (Wrapper Model)
Playbook Model
Dynamic & Contextual. Playbooks are generated at runtime from live alert data. No pre-built playbooks to maintain.
Static with AI Assistance. Natural language abstraction over pre-built or template-based runbooks. Workflows remain static.
Investigation Depth
L2-Equivalent Autonomous Triage. Attack path discovery traces horizontal (cross-tool) and vertical correlations.
L1 Triage & Routing. Primarily focused on classification and routing. Complex investigations escalate to humans.
Integration Maintenance
Self-Healing. Autonomously detects and corrects API drift, schema changes, and output format shifts.
Manual Maintenance. When vendor APIs change, workflows break silently. Requires human intervention to fix.
Case Management
Full Lifecycle with evidence tracking, SLA management, and role-based access.
AI-Native / Immature. Often reliant on enriched summaries; lifecycle tooling is newer and less mature.
Multi-Tenant / MSSP
Purpose-Built. Proven MSSP platform with per-tenant performance isolation.
Supported but Less Documented. Functional multi-tenancy, but depth of isolation is often less clear.
Time to Value
Instant Contextual Playbooks. Connect alert sources and Morpheus immediately generates investigations.
Weeks/Months to Build. Requires building agents and runbooks. Benchmarks show months for broad coverage.
One System, One Record of Truth
Most stacks split the lifecycle. One place for automation. Another for reporting. Every handoff creates delay and ambiguity.
Morpheus treats case management and incident response as first-class capabilities, tied directly to the actions taken. It is “closed loop” design: ingest, investigate, respond, and report, all in one record of truth.
Don’t buy a chatbot to manage your legacy playbooks. And don’t pay a premium price for what is, at its core, a legacy SOAR.
Get a Morpheus demo on your own alert sources. Compare investigation depth (L1 vs L2), integration resilience (self-healing vs. manual), and time to value (instant vs. months of building).
The post Don’t Settle for an AI SOAR: The Case for Autonomous SOC Operations appeared first on D3 Security.
*** This is a Security Bloggers Network syndicated blog from D3 Security authored by Shriram Sharma. Read the original post at: https://d3security.com/blog/dont-settle-for-ai-soar/
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