Critical Start has released SOC AI, a production-proven multi-agent framework powering its AI-led Managed Detection and Response (MDR). SOC AI coordinates ten specialized agents across the full alert investigation and response lifecycle, covering detection, triage, response, threat hunting, and continuous improvement. Each agent operates with a discrete function, a defined scope, and a complete audit trail on every action taken.

After implementation of SOC AI, Investigation Agent enhanced thousands of investigations, compressing analyst time-to-investigate to 10 minutes and SOC AI generated investigation output delivered in seconds.

SOC AI follows a single, continuous flow from first signal to resolved analyst-led threat containment. Every alert streams directly from a customer’s existing tools into the Critical Start platform with no broker and no proxy.

The investigative work is completed at machine speed across every alert, correlating signals, surfacing context, and generating recommendations without waiting for a human to start the process. Every recommendation carries a complete record of how it was reached, and if AI is ever unavailable for any reason, the platform’s deterministic foundation and the SOC continue at full capacity without degradation. That combination of AI-driven speed, built-in resilience, and a clear accountability checkpoint is what allows Critical Start to back SOC AI with contractual SLAs no AI-first MDR vendor in the market has matched.

“SOC AI establishes a new benchmark for responsible AI in security operations. Our multi-agent architecture delivers full lifecycle coverage, reducing investigations from hours to seconds and driving security outcomes backed by the SLAs our customers have come to know and trust. We designed this framework to be transparent, auditable, and operationally resilient because in cybersecurity, trust, speed, and accountability are not trade-offs; they are requirements,” said Scott White, CEO, Critical Start.

Each agent handles a discrete function with a defined scope and a complete audit trail, so speed never comes at the cost of accountability:

Customer-facing agents

• Investigation Agent reasons over each alert, correlating signals, enriching with threat intelligence, and delivering a contextualized verdict recommendation directly inside the analyst’s workflow. Investigation-to-verdict time has compressed to seconds.
• Case Agent intelligently aggregates related alerts to build a unified investigation view, surfacing cross-alert context and helping analysts and the SOC team understand the full scope of an incident rather than triaging alerts in isolation.
• Threat Hunt Agent runs hypothesis-based hunts directly against ingested events and alerts, proactively surfacing threats before they escalate into active incidents.
• Detection Agent acts on the outputs of the Threat Hunt Agent, authoring new detections and expanding coverage gaps identified during proactive hunting, so each hunt makes the next one smarter.
• Response Agent authors and executes deterministic response actions through the automation layer. Sensitive actions are authorized by a human checkpoint before execution, with no direct API access and no risk of AI plan drift.
• Automation Agent suggests multi-step playbooks based on patterns observed across repeat work, accelerating detection and response by converting institutional knowledge into reusable, auditable automations.
• Insights Agent works alongside the Critical Start technical account team to surface cross-environment patterns, identify coverage gaps, flag repeat offenders, and deliver validated insights during monthly Cyber Risk Reviews.

Platform agents

• TBR Agent operates at the deterministic core of the platform, resolving known-good behaviors and reducing false positives before they ever reach the investigation layer. Built from more than a decade of real analyst investigations, the TBR has filtered approximately 99.8% of incoming events across customer environments. Every new closed case enriches the registry.
• AI Engineering Agent operates as a self-improving loop inside the SOC AI framework, proposing prompt and skill edits to agents based on observed performance, ensuring the system continuously improves and reduces manual intervention.
• Automation Builder Agent enables SOC engineers to rapidly author and deploy new automations through a no-code interface, turning analyst-identified patterns into deterministic playbooks available across the platform.