Deep agents take point: Swimlane's AI SOC shifts security from reactive to proactive

Swimlane debuts AI SOC built on agents: what Ops leaders need to know

Swimlane announced an AI-driven security operations center that leans on proactive agents instead of reactive assistants. The core idea: "deep agents" carry the cognitive load while staying transparent and auditable - giving operations teams speed without losing control.

"Deep agents tackle tough, complex problems with methodical reasoning, while expert agents quickly handle specific, skilled tasks," said co-founder and Chief Executive Cody Cornell. That split mirrors how mature SecOps teams already operate - strategic brains, fast hands - now automated.

What Swimlane is shipping

• Two primary agents: an Investigation & Response agent and a Playbook Generator agent.
• Out-of-the-box knowledge: 100+ best-practice articles seeded with organizational context.
• Explainability and auditability: every action is reviewable, modifiable, and traceable.
• Operational guardrails: built-in controls for trustworthy action at scale; humans can approve, edit, or roll back.
• Tool calling + MCP access: integrates across your stack, with graph and feedback loop visibility, reasoning, and memory.
• Edge execution: playbooks can run near the event source for rapid containment.

"With Swimlane AI SOC, we're augmenting our analysts with intelligent automation that improves efficiency, sharpens focus and strengthens our ability to proactively counter cyberthreats," said LeAnn Cary, vice president and practice leader at Optiv Security's Advanced Fusion Center.

Why this matters for operations

• Always-on detection and action: agents watch continuously, not just when a human opens a ticket.
• Lower MTTR without losing oversight: fast, explainable decisions you can audit and refine.
• Consistent execution: standardized playbooks that adapt with feedback loops.
• Capacity without headcount spikes: offload repetitive triage and response so human analysts focus on high-signal work.

Context: agents are moving into security fast

LLM assistants started as "on-call" analysis tools. Agents make them autonomous and persistent - the brain of a monitor. Enterprise platforms are already moving this way. Microsoft's cloud-native SOC platform, Sentinel, has leaned heavily into AI and agentic workflows since 2025 see product overview. Google introduced Agentic Threat Intelligence to act as a virtual teammate that sources and synthesizes threat data.

The arms race continues - defenders and attackers both use AI, a Red Queen's race dynamic. The takeaway for Ops: speed, transparency, and control are non-negotiable.

What to expect in Swimlane's operating model

• Investigation & Response agent: pulls context across tools, reasons through hypotheses, and executes approved actions.
• Playbook Generator agent: drafts, refines, and improves runbooks using feedback and performance data.
• Human-in-the-loop checkpoints: reviewers can approve, modify, or reject actions and plans before they go live.
• Knowledge base growth: generated articles and investigations become reusable building blocks for future incidents.

"We've built the foundation for enterprise-grade deep agents that operate autonomously, reliably, and at real scale," said Srikant Vissamsetti, chief operating officer at Swimlane. "AI SOC isn't just a product, it's a new operating model for how organizations defend, scale and stay ahead of tomorrow's threats."

Implementation checklist for Ops teams

• Start with two to three high-volume alert types (phishing, endpoint malware, identity anomalies).
• Define guardrails: authorization tiers, auto-action limits, and rollback procedures.
• Map your toolchain APIs and event sources; enable Model Context Protocol via your MCP integration.
• Set human review points for containment, isolation, and external comms.
• Turn on full telemetry: decision traces, action logs, and rationale capture for audits.
• Pilot in a canary segment before scaling across environments.

KPIs to track from day one

• Mean Time to Acknowledge (MTTA) and Mean Time to Respond (MTTR).
• Percent of incidents auto-triaged and auto-resolved.
• Escalation rate to human analysts and reasons for escalation.
• False positive rate and dwell time trends.
• Playbook coverage across top incident categories and change velocity.
• Audit pass rate and explainability completeness (decision trace coverage).

Risk controls to keep in place

• Approval gates for destructive actions (account disablement, network isolation).
• Drift detection on playbooks; scheduled reviews of agent reasoning quality.
• Red-team scenarios targeting agent blind spots and tool-call failures.
• Data minimization and context scoping to avoid overexposure of sensitive data.

Your 30/60/90-day rollout plan

• 0-30 days: Select pilot use cases, connect data sources, define guardrails, and enable decision logging.
• 31-60 days: Move to supervised auto-response on a subset of incidents; tune playbooks; review audit traces weekly.
• 61-90 days: Expand to additional incident types; introduce limited unsupervised actions with rollback; lock KPIs into Ops reviews.

Skills and enablement

Upskill analysts and operations engineers on agentic workflows, prompt patterns for investigations, and SOC automation. A structured path helps shorten the learning curve - see the AI Learning Path for Cybersecurity Analysts.

Bottom line

Swimlane's AI SOC pushes security operations toward autonomous, explainable execution with humans firmly in control. If you lead Ops, this is a practical way to cut response times, standardize execution, and keep your team focused on what truly moves risk down and resilience up.