Build an AI-Powered Threat Detection System
A real-time threat detection pipeline that ingests security events, analyzes them with LLMs for anomaly classification, and orchestrates automated response workflows with human-in-the-loop escalation.
Data Ingestion & Enrichment
Collect, parse, and enrich raw security events from logs, network traffic, and endpoint telemetry
RAG pipeline ingests and chunks security logs, CVE databases, and threat intel feeds for contextual retrieval during analysis
Converts heterogeneous security artifacts (PDFs, emails, reports) into structured data for downstream LLM consumption
Crawls OSINT threat intelligence sources and vulnerability databases to keep the enrichment layer current
Threat Analysis & Classification
LLM-powered reasoning layer that classifies events as benign, suspicious, or critical using contextual threat intelligence
Models the threat analysis pipeline as a stateful graph — triage, enrichment, classification, and escalation nodes with conditional edges based on severity
Programmatic prompt optimization for threat classification ensures consistent, measurable detection accuracy across evolving attack patterns
Validates LLM threat classification outputs against structured schemas, preventing hallucinated severity scores or malformed alerts
Orchestration & Automated Response
Coordinate multi-step incident response workflows including containment, notification, and remediation
Visual workflow automation connects detection alerts to response actions — SIEM integration, ticket creation, Slack notifications, and firewall rule updates
Durable workflow execution ensures long-running incident response processes survive failures and support human approval gates for critical containment actions
Pre-built tool integrations for security platforms (Jira, PagerDuty, Slack) accelerate connecting detection to response without custom API work
Observability & Evaluation
Monitor detection accuracy, track false positive rates, and continuously evaluate the system's threat classification performance
Traces every threat analysis chain end-to-end — tracks latency, token costs, and classification decisions for audit and tuning
AI observability with built-in evaluation metrics helps identify classification drift and degrading detection accuracy over time
Automated evaluation framework benchmarks threat detection against labeled datasets to measure precision, recall, and false positive rates
Vector Memory & Threat Intelligence Store
Persistent knowledge base of historical threats, attack signatures, and incident patterns for similarity-based detection
High-performance vector search enables real-time similarity matching of new events against known attack patterns and historical incidents
Lightweight vector store suitable for embedding threat signatures and enabling semantic search across the intelligence corpus
Persistent memory layer maintains evolving context about ongoing investigations and attacker TTPs across detection sessions