AI-Powered Threat Detection & SOC Automation
Privacy-first security operations center using local LLMs to analyze threat intelligence, detect anomalies in logs, and automate incident response with persistent memory of past attacks.
Threat Intelligence Ingestion
Multi-source intelligence aggregation from global security feeds and OSINT scraping
Provides real-time correlation of military, infrastructure, and cyber escalation signals across 435+ feeds—critical for early warning of nation-state APT activity
Scrapes threat blogs, CVE databases, and security advisories into LLM-ready markdown with 96% JavaScript coverage for modern security portals
Document & Log Processing
Parsing and structuring of security reports, PDFs, and unstructured log formats
Handles complex security PDFs (pentest reports, threat intel PDFs) with table/formula preservation—tens-of-times faster than alternatives for IOC extraction
CLI-first pattern extraction specifically designed for security researchers; pipes threat analysis patterns into Unix workflows for SIEM integration
AI Analysis Core
Local LLM inference for privacy-preserving log analysis and threat classification
Runs security-specialized models (SecLLM, DeepSeek-Coder) locally ensuring sensitive logs never leave premises; OpenAI-compatible API drops into existing SOC tools
High-performance vector search for attack signature matching and similarity detection against 10k+ historical IoCs with millisecond latency
Contextual Memory Layer
Persistent storage of investigation history and organizational baseline behavior
Automated Response Orchestration
SOAR-style workflow automation and autonomous IoC investigation
Visual workflow builder connecting detection alerts to remediation actions across 400+ integrations (Slack, Jira, SentinelOne) with human-in-the-loop approval gates
Autonomous browser automation for investigating suspicious URLs, checking domain reputation, and downloading samples to sandboxes without analyst manual browsing
Governance & Observability
Audit trails, compliance logging, and output validation for AI decisions
LLM-specific observability tracing every threat classification decision with prompt history—essential for SOC2 compliance and incident post-mortems
Input/output validation ensuring threat severity scores conform to organizational risk matrices and preventing AI hallucination of non-existent CVEs