Security teams have never had more tools or more distance between them.
Alerts arrive in one system, context lives in another, investigation happens in a third, and the decision to act happens in a meeting. Every handoff costs time, and every gap is where the detail that mattered gets lost. Adding another product to the stack usually adds another handoff.
TENEX takes a different approach. Instead of bolting AI onto individual steps, we built a single operating model, a security operations platform, that spans the security lifecycle: context, triage, investigation, orchestration, threat intelligence, hunting, detection engineering, transparency, and governed response. AI operates throughout. Human experts remain accountable for consequential decisions.
Here’s what that looks like in practice.
- Security Context Graph. A living model of your users, devices, applications, vulnerabilities, relationships, baselines, standard operating procedures, and historical activity; so every investigation starts with context instead of building it. And every signal that flows through the TENEX platform improves context; TENEX continuously connects knowledge across endpoint, identity, cloud, email, and network systems.
- Autonomous Alert Investigation. Every alert receives a complete, context-aware investigation and a supported disposition. Specialized agents combine the Security Context Graph with a novel take on playbooks called PromptBooks: customer-specific procedures that define the queries, tools, evidence, skills and escalation criteria required to investigate a particular detection or technology. Benign activity closes itself, within the policies and guardrails you set. Threats and uncertain findings escalate with the evidence already assembled.
- Security Workflow Orchestration. Agents, evidence collection, investigative queries, cases, promptbooks, and connected security products operate as one continuous workflow. Work moves from ingestion through disposition and reporting without manual handoffs between tools or teams.
- Glass-Box AI. Every investigation shows its work: the context used, the PromptBooks applied, the hypotheses considered, the queries executed, the evidence collected, the agent findings, pivots made and the reasoning behind the disposition. Every agent action and human decision stays visible and auditable.
- Machine-Speed Threat Intelligence. AI-driven monitoring, combined with expert validation, identifies emerging attacks as they unfold. New intelligence immediately informs customer reporting, active investigations, threat hunts, and detection-coverage analysis. Emerging threats become defensive action in minutes; not another feed for analysts to monitor.
- Autonomous Threat Hunting. Analysts can hunt directly or task TENEX agents to pursue a hypothesis. Agents search across SIEM and source-native telemetry, execute multi-step hunts, evaluate the evidence, synthesize results and determine the next pivots. Results become findings, affected entities, and governed hunt reports.
- Agentic Detection Engineering. Agents measure detection effectiveness, identify coverage gaps, and move improvements through a governed lifecycle: measure, recommend, test, deploy, monitor. Rules and PromptBooks evolve together, so new and updated detections arrive with the investigative logic required to work them consistently.
- Human-Governed Response. Agents recommend and execute pre-authorized actions according to policies you define. Consequential, uncertain, or out-of-policy decisions route to human experts for judgment and approval.
Why the model matters more than the features
Any one of these capabilities is useful on its own. Delivered separately, they’d just be eight more things to integrate.
The value is in the connections. Threat intelligence sharpens detections. Detections arrive with the investigative logic to work them. Investigations draw on a context graph that every hunt enriches. Hunts surface gaps that detection engineering closes. And because every step shows its reasoning, the humans responsible for consequential calls can audit the work behind a recommendation rather than take it on faith.
That’s the difference between automating tasks and continuously operating a security program.
One week, your telemetry, your alerts. You’ll see every investigation start to finish: the context used, the queries executed, the evidence collected, and the reasoning behind every disposition. No black boxes to take on faith. If you’re on Google SecOps or Microsoft Sentinel, take the challenge and move to fully-agentic human-led SecOps now.