LLM-Powered Security Assistant

An AI investigation engine that augments SOC analyst capabilities through natural language interaction and automated tool orchestration.


Overview

The security assistant uses Retrieval-Augmented Generation (RAG) to combine LLM intelligence with real-time security data from 25 integrated tools. Analysts can ask questions in natural language and receive enriched, contextual responses.

Key Capabilities


Investigation Tools (25)

Endpoint Detection & Response

Tool Capabilities
CrowdStrike Host Lookup Query endpoint details, sensor status, containment state
CrowdStrike Detection Search Find detections by host, severity, time range
CrowdStrike Containment Network isolate/release hosts
Tanium Endpoint Status Real-time endpoint health and compliance
Tanium Live Query Execute ad-hoc endpoint queries

SIEM & Log Analysis

Tool Capabilities
QRadar Log Search Query logs across all sources
QRadar AQL Query Advanced Ariel Query Language searches
QRadar Offense Investigation Investigate correlated offenses

Threat Intelligence

Tool Capabilities
Recorded Future Risk scores, threat context, related indicators
VirusTotal Hash, URL, domain reputation from 70+ engines
URLScan Website scanning and screenshot capture
AbuseIPDB IP reputation and abuse reports
Abuse.ch Malware hash, C2 server, and phishing URL lookups
Shodan Internet-facing asset discovery
IntelX Dark web and leak database search

Case Management & SOAR

Tool Capabilities
DFIR-IRIS Case creation, IOC management, timeline events
TheHive Case management, observable tracking, alert handling
XSOAR Ticket enrichment, summary generation, incident management

Identity, Email & Workflow

Tool Capabilities
Have I Been Pwned Email breach checking
ServiceNow CMDB queries, ticket creation
Abnormal Security Email threat investigation
Zscaler URL categorization

Analysis & Remediation

Tool Capabilities
Tipper Analysis LLM-powered threat intel novelty detection against historical data
Remediation Automated playbook and runbook suggestions

How It Works

  1. User Query: Analyst asks a question in natural language
  2. RAG Retrieval: System retrieves relevant runbooks and context
  3. Tool Selection: LLM determines which tools to invoke
  4. Execution: Tools query security platforms in parallel
  5. Synthesis: LLM combines results into actionable response

Technical Implementation


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