The Cognitive Layer of the SOC

Rudra
AI SOAR.

AI-powered Security Orchestration, Automation, and Response. Built on the Claude Agent SDK. Already running in a live, production SOC. Replaces workflow SOAR with agents that reason.

LiveProduction SOC
3-LayerDefence in depth
Read-OnlyDefault posture
Why replace workflow SOAR

Playbooks don’t scale. Reasoning does.

Traditional SOAR, from n8n, Shuffle, and Tines to their enterprise predecessors, assumes you can write a fixed playbook for every alert pattern you will ever see. SOC teams know that assumption does not hold. The alerts that cause the most damage are the ones no playbook anticipated.

Rudra AI SOAR replaces the fixed-playbook model with agents that read each alert in context, query the systems they need, reason about what the data means, and propose a verdict and remediation, with every action logged for review.

Analysts spend less time clearing noise and more time on the work that requires their judgement.

Capability Suite

Eight capabilities. One cognitive layer.

Deploy what your SOC needs first. Add the rest as trust compounds.

01

Auto triage.

When Wazuh, AWS, or any source fires a webhook, an agent spawns with full alert context, queries the systems it needs, classifies the alert, and produces a verdict and recommendation before an analyst would have opened the case. The L1 queue disappears.

02

Alert enrichment.

Pulls live context from Neo4j topology, session-recording stores, Git repositories, the Wazuh archive, and your asset inventory. Transforms a single line of alert text into who has access, what paths lead in, whether the asset is a crown jewel, and how this pattern has played out before.

03

Vulnerability prioritisation.

Re-scores every scanner finding against actual network exposure, DNAT path, and asset criticality, not CVSS alone. The remediation list collapses from thousands of findings to the few that matter.

04

Rule refinement.

Agents analyse alert patterns over time, identify the legitimate activity generating false positives, draft a tightened rule, and open a merge request. Detection engineering stops being the bottleneck on signal-to-noise.

05

Application code review.

Full-repo scans plus commit-triggered scans on every push. Detects SQL injection, XSS, credential leakage, vulnerable dependencies, and security misconfiguration. Where the fix is straightforward, the agent opens a PR with the proposed remediation.

06

Automated threat hunting.

Agents proactively hunt across infrastructure using read-only access, sandboxed by the defence-in-depth model. They run hypothesis-driven hunts on schedule, on signal, or on operator question, and surface findings to the human team for action.

07

AI-generated dashboards.

Composed live from the operator’s question. No configuration, no dashboard sprawl. The right view, generated on demand, backed by real telemetry.

08

Production operator UI.

A full interface around the agent layer, built for analysts working live incidents. Not a chat window strapped to a SIEM.

Defence-in-Depth Security Model

Agentic AI built for regulated environments.

Three independent enforcement layers prevent an autonomous agent from being misused, whether by an attacker, by prompt injection, or by its own misjudgement.

01

Layer 1 — Agent.

Prompt-level enforcement of a read-only policy. Refuses writes, modifications, and deletes. Filters credential output. Rejects bypass attempts. Every host access must be justified.

02

Layer 2 — Program.

The boundary-exec wrapper blocks destructive operations, remote-execution patterns, credential access, and privilege escalation before they reach a host. Production hosts and elevated privileges are protected by default.

03

Layer 3 — Server.

A locked-down, read-only account on every target host, with privileges restricted to observation. No write, delete, or modify operation is possible at the operating system level.

Read-only enforcement extends to AWS (describe / list / get only), GitLab (read-only API tokens), and Wazuh (read-only Manager and Indexer). Every operation logged for compliance and forensics.

Integration Footprint

Plugs into the stack you already run.

01

Detection and response.

Wazuh Manager, Indexer, and Archive. Elasticsearch. HashiCorp Boundary session recordings.

02

Graph and asset intelligence.

Neo4j topology and EASM. BBOT external attack surface. Vulnerability dependency databases.

03

Cloud and code.

AWS (describe / list / get). GitLab (internal and external). PostgreSQL. MCP servers for custom data sources.

Designed for hybrid deployment, with an on-premises agent layer and cloud data sources over IPsec VPN. The current production deployment runs fully on-premises.

Engage

Bring Rudra AI SOAR
to your SOC.

In production with launch partners. Selectively onboarding additional SOC environments. Briefings available for security leadership.