VaultSense
VaultSense
Project overview
VaultSense is a real-time behavioral anomaly detection engine designed for streaming platforms. It analyzes live user activity, detects suspicious behavioral patterns such as credential stuffing and account takeovers, and delivers AI-powered security assessments with automated Slack alerts in seconds. Detected 3 attack patterns across 250,000 simulated user sessions with Gemini AI delivering unique natural language risk assessments in under 5 seconds.
VaultSense is a real-time behavioral anomaly detection engine designed for streaming platforms. It analyzes live user activity, detects suspicious behavioral patterns such as credential stuffing and account takeovers, and delivers AI-powered security assessments with automated Slack alerts in seconds. Detected 3 attack patterns across 250,000 simulated user sessions with Gemini AI delivering unique natural language risk assessments in under 5 seconds.
Services
Data Engineering
AI Automation
Security Intelligence
Industry
Streaming Platforms
Year
2026
Challenge
Modern security systems often rely on predefined rules, making it difficult to identify sophisticated behavioral attacks that appear legitimate. The challenge was to design an intelligent pipeline capable of enriching user context, combining deterministic fraud detection with AI reasoning, and generating actionable security alerts in real time.
Modern security systems often rely on predefined rules, making it difficult to identify sophisticated behavioral attacks that appear legitimate. The challenge was to design an intelligent pipeline capable of enriching user context, combining deterministic fraud detection with AI reasoning, and generating actionable security alerts in real time.
Outcome
Developed an end-to-end behavioral intelligence pipeline processing 250,000+ simulated user events with PostgreSQL, Python, n8n, Gemini AI, and Slack. The system successfully identifies high-risk user behavior, stores audit logs for investigation, and delivers contextual AI-powered alerts in under five seconds, demonstrating how data engineering and AI can work together for proactive threat detection.
Developed an end-to-end behavioral intelligence pipeline processing 250,000+ simulated user events with PostgreSQL, Python, n8n, Gemini AI, and Slack. The system successfully identifies high-risk user behavior, stores audit logs for investigation, and delivers contextual AI-powered alerts in under five seconds, demonstrating how data engineering and AI can work together for proactive threat detection.
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