AI-driven cyber threat intelligence and external attack surface management
CatchProbe builds an AI-powered intelligence platform for threat detection, dark web monitoring, and deception systems. The stack spans Python, Node.js, React, and big-data infrastructure (Hadoop, Kafka, Elasticsearch, HBase), pointing to heavy data ingestion and real-time correlation pipelines. MongoDB performance bottlenecks and ad-hoc script execution appear as operational friction points, while capacity planning and ROI measurement surface as business pressures. Hiring remains sparse but senior-weighted, concentrated in engineering.
CatchProbe delivers an AI-driven SaaS platform—IntelligencySuite—for cyber threat intelligence, OSINT, deception systems, and digital crime analytics. The product centralizes intelligence workflows: gathering from open sources and private feeds, enriching via AI engines, and surfacing actionable insights for threat response and attack-surface mapping. Key modules include autonomous deception environments (SmartDeceptive), dark and deep web monitoring (DarkMap), data-leak detection (LeakMap), and external attack surface visualization (RiskRoute). The platform targets mid-to-enterprise security teams seeking predictive, preventive intelligence rather than post-breach detection.
Python, JavaScript, Node.js, React, TypeScript for frontend and backend. Data layer: PostgreSQL, MongoDB, Elasticsearch, Hadoop, HBase, Kafka, Kinesis for real-time ingestion and correlation. Infrastructure: Docker, Terraform, Ansible.
Active projects include security intelligence webinars, risk mitigation planning, Hadoop cluster deployment, log monitoring automation, and ad-hoc script execution. Operational priorities center on capacity planning and ROI measurement.
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