How to Build an AI Security Governance Hub with Agent Bricks

When a breach happens, every second counts. If your playbooks are buried in manuals and your telemetry is siloed in tables, your SOC is operating with a handicap. In this video, we build a *Security Governance Hub* using *Agent Bricks* to turn passive documentation into an adaptive, multi-agent system. We bridge the gap between structured incident data (SQL/Genie) and unstructured institutional knowledge (PDFs) to ensure your SOC evolves as fast as the threats it faces. 🚀 What You’ll Learn: • 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭: Ingest and query complex PDF playbooks without manual parsing logic. • 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Use AI/BI Genie to ask natural language questions of your telemetry. • 𝐓𝐡𝐞 𝐌𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦: Use the Supervisor Agent to correlate live signals with detection steps defined in your playbooks. • 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: How Unity Catalog ensures the AI never sees data it isn't supposed to. 🛠️ The Tech Stack: • 𝐀𝐠𝐞𝐧𝐭 𝐁𝐫𝐢𝐜𝐤𝐬: Framework for building domain-specific agents. • 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞:The secure foundation for all your data. • 𝐔𝐧𝐢𝐭𝐲 𝐂𝐚𝐭𝐚𝐥𝐨𝐠: For identity-based access and automatic PII masking. • 𝐌𝐋𝐟𝐥𝐨𝐰: For tracing and evaluating LLM-as-a-Judge metrics. 🕒 Timestamps: 0:00 - Intro: The Hidden Cost of "Knowledge Debt" 0:39 - Analyzing SOC Data Sources (Structured vs. Unstructured) 2:03 - Demo: The Knowledge Assistant (KA) for PDF Playbooks 3:55 - Demo: AI/BI Genie for Structured Incident Telemetry 5:43 - The Supervisor Agent: Connecting the Dots 7:51 - Technical Deep-Dive: Tuning for SOC Accuracy 8:18 - 1. Tuning Genie: Golden SQL & Trusted Assets 12:02 - 2. Tuning Knowledge Assistant: Semantic Filtering & Examples 13:23 - 3. Tuning the Supervisor 13:53 - Monitoring & Evaluation: MLflow & LLM-as-a-Judge 15:27 - Governance & Privacy: Unity Catalog & Masking 16:12 - Conclusion: Agent Bricks as part of the Databricks ecosystem 🔗