Secure Lens: Multi-Agent AI System for Security Telemetry Analysis | Kaggle Capstone 2025

Secure Lens is my official submission for the Kaggle Agents Intensive Capstone Project (2025).
It’s a lightweight, multi-agent AI system designed to analyze synthetic security telemetry, detect suspicious behavior, rank it with a quantum-inspired optimizer, enrich it with contextual intelligence, and generate clear, human-friendly security reports.

This project demonstrates all major agent concepts taught during the 5-Day AI Agents Intensive by Google:
• Multi-agent orchestration
• Tool-based actions (ingest, optimizer, enrichment)
• Session memory + long-term memory
• Context engineering
• Observability (logs, traces, metrics)
• Explainable AI
• Lightweight evaluation

System Architecture:
Telemetry → Ingest Tool → Analyzer Agent → Quantum-Inspired Optimizer → Researcher Agent → Explainer Agent → Memory Bank → Observability → Final Report

Key Features Implemented:
✔ Multi-agent system
✔ Custom tools (ingest + optimizer + enrichment)
✔ Memory (session + long-term)
✔ Context engineering
✔ Transparent logs and trace IDs
✔ Ranking of suspicious events
✔ Fully synthetic, safe, and reproducible pipeline

Value:
Secure Lens shows how structured agent design can turn raw telemetry into actionable insights, making security workflows more efficient, explainable, and transparent.

Project Notebook:
(Add your Kaggle notebook link here)

Competition:
Kaggle Agents Intensive — Capstone Project (2025)

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Видео Secure Lens: Multi-Agent AI System for Security Telemetry Analysis | Kaggle Capstone 2025 канала Cozyman