Architecture overview

Six layers. One continuous loop.

SIRP functions as a closed-loop Autonomous SOC built around six core layers. Every layer runs continuously as your environment changes.

Not per ticket. Not per alert. Evaluated as an evolving system state.

Six layers architecture overview
Step one

Signal Ingestion

SIRP ingests telemetry from your entire stack: Identity providers, endpoints, cloud, network, SaaS, threat intel, and your existing SIEM/EDR/XDR. Every signal is normalized into structured entities. No blind spots. No filtered snapshots.

Signal Ingestion screenshot 1
Relational Context screenshot 1
Step two

Relational Context

Most tools treat alerts as isolated events. Three signals, three tickets, zero connected thinking.

OmniMap maps the relationships between identities, devices, workloads, access paths, and historical incidents so SIRP can estimate blast radius and detect exposure paths before a human would even think to connect them.

Step three

Risk Evaluation & Reasoning

OmniSense continuously evaluates system state against behavioral baselines, threat intel, privilege levels, asset sensitivity, and OmniMap's relational context. For every event: a risk score, a confidence level, eligible actions, and a clear execution authorization.

It doesn't just flag what's bad. It decides what to do about it.

Risk Evaluation & Reasoning screenshot 1
Policy Validation screenshot 1
Step four

Policy Validation

Autonomy without governance is just chaos with better tooling. Every decision is validated against your policies permitted actions, risk thresholds, asset constraints, escalation rules.

Conditions met? It acts.

Conditions not met? It escalates.

No ambiguity.

Step five

Autonomous Execution

This is where most platforms stop and send you a Slack message. SIRP's Agentic Mesh executes — endpoint isolation, identity restriction, session termination, network containment, cloud workload isolation — the moment governance and confidence conditions are met.

Autonomous Execution screenshot 1

The agentic mesh in action

Step six

Decision Memory & Learning

Every decision is recorded: context, actors, actions, outcomes, analyst feedback. This memory feeds back into OmniSense, refining future risk evaluation continuously.

Static systems degrade. Learning systems compound.

Decision Memory & Learning screenshot 1

Watch your
Autonomous SOC
drive itself