Founding Members

Autonomous security validation: continuous testing against realistic attacker behavior to determine whether the architecture withstands attacks rather than only looking correct on paper.

Identity activity context: an identity access graph showing what identities are actually doing, not only what policies permit, giving agents behavioral identity context for investigation and remediation.

Autonomous investigation and evidence-based verdicts: targeted questions and cross-source context to investigate threats and validate findings, turning distributed telemetry into defensible conclusions.

Endpoint, identity, and security control plane: endpoint telemetry, threat detection, and response through the Falcon platform, extending agentic investigations from network context into endpoint and enterprise controls.

Autonomous SOC analysts: AI agents that investigate security events and perform SOC workflows at machine speed, consuming shared context and operating across the security stack.

Multi-source AI investigation: Exabots are autonomous security agents that consume context from across the ecosystem, showing how richer shared context improves the confidence of autonomous investigation.

Real-time network context and ground truth: high-fidelity, protocol-level visibility across east-west and encrypted traffic, giving agents the real-time evidence to understand what happened across the environment.

Reliable security-data movement: the connective tissue that moves security data through the SecOps stack, so agents and detections keep receiving accurate context as environments change.

Autonomous forensic investigation: AI-driven investigation across identity, endpoint, email, and cloud sources, including analysis of code, behavior, and provenance.

Governed AI runtime and harness: the governed environment in which agents interact with tools and data, including permissions, policy controls, and human-in-the-loop governance.

Agent engineering and lifecycle management: frameworks and tooling for building, testing, and governing agents, including control over context, tools, actions, and human approval.

AI-native SOC reasoning: AI-driven threat investigation and hunting that uses network context as ground truth to improve the accuracy and transparency of autonomous SOC decisions.

Software and threat intelligence context: deep software intelligence and threat context that moves beyond static indicators toward behavioral understanding and automated detection and investigation guidance.

Autonomous security operations and deployment: automation that operationalizes investigation and response workflows, helping organizations move from agentic experimentation to repeatable enterprise SOC operations.

Security orchestration and autonomous response: the workflow and orchestration layer through which AI can triage, investigate, and execute response actions across the security environment.