Securonix, in collaboration with Amazon Web Services (AWS), has introduced Sam, the AI SOC Analyst, and the Securonix Agentic Mesh, outlining a new operating model for security operations focused on analyst productivity, AI governance in production, and measurable outcomes.
Amid ongoing pressure on security operations—marked by high alert volumes, analyst shortages, and rising SIEM costs—Securonix is positioning the launch around measurable work output rather than feature expansion. With Sam and Agentic Mesh, Securonix aims to enable security leaders to quantify AI-assisted analyst work, track AI-supported actions, and report impact in operational and business terms.
Sam is designed to operate as a digital SOC teammate, extending SOC capacity without additional headcount. It automates Tier 1 and Tier 2 security operations tasks, including alert triage, investigation, correlation, and response preparation.
Operating natively within the Securonix Unified Defense SIEM and coordinating specialised AI agents through the Agentic Mesh, Sam incorporates human-in-the-loop oversight. This is intended to ensure that AI-assisted actions are policy-bound, auditable, and explainable, with analysts retaining control.
Within the Securonix Agentic Mesh, Sam coordinates specialised AI agents across detection, investigation, response, and reporting workflows. The Agentic Mesh functions as an orchestration layer designed to maintain shared context and enterprise policy enforcement, with AI-driven actions that can be reviewed, approved, or reversed.
Built on Amazon Bedrock AgentCore, the model operates within the customer environment, supporting isolation, resiliency, and scalability requirements. The approach is positioned as enabling governed AI use in security operations, with reporting aligned to executive and board-level requirements.
Economic Model with DPM Flex
Securonix supports this AI-driven productivity model with Data Pipeline Manager Flex Consumption (DPM Flex). DPM Flex routes telemetry based on analytical value rather than raw data volume, aiming to control SIEM costs while supporting AI-driven investigations. The model is designed to align productivity gains with predictable data economics as environments scale.
HDFC Bank is cited as an example of a regulated organisation using Securonix’s agentic AI capabilities at scale, with an emphasis on regulatory oversight, transparency, and analyst control during investigative processes.
