Google's Michael Sinno on Autonomous Detection at 7 Trillion Logs Per Day
Detection at Scale
Goal: AI writing detections autonomously
Michael shares ambitions for agents to ingest threat intel and autonomously generate detections within a few years.
What does it actually take to automate security operations when you're processing 7 trillion log lines daily and a single missed threat could compromise billions of users? Michael Sinno, Director of Detection & Response at Google, explains how his team handles this with less than 1% requiring human intervention through strategic AI implementation. He explores Google's methodical approach to AI autonomy, including fine-tuned models trained on golden datasets, validation through overseer agents, and the critical distinction between traditional automation and agentic AI that exercises judgment.
Michael also discusses groundbreaking work with Sec-Gemini and Timesketch that enables forensic analysis to surface attack patterns humans would never detect manually. Michael shares concrete metrics like reducing executive incident notifications from 30 minutes to 90 seconds, achieving 95% precision in ticket deduplication, and automating vulnerability coordination from hours to minutes.
Topics discussed:
Processing 7 trillion log lines daily with less than 1% of a million annual tickets requiring human intervention at Google
Strategic evolution from AI-assisted to AI-led to autonomous security operations using fine-tuned models and golden datasets
Building modular detection agents as pluggable components that can be combined like Legos for specific security use cases
Implementing quality assurance through overseer agents that review other agents' work to ensure precision in security decisions
Reducing executive incident notifications from 30 minutes to 90 seconds using AI-powered summarization and context gathering
Achieving 95% precision in ticket deduplication while managing the trade-off between precision and 38% recall rates
Integrating Sec-Gemini with Timesketch to surface attack patterns in forensic investigations that humans would never find manually
Shifting from traditional detection and response to infer-and-interrupt models that contain threats immediately before escalation
Automating vulnerability coordination workflows from hours to minutes through AI-powered data collection and impact analysis
Distinguishing between traditional automation and agentic AI that exercises judgment rather than following if-then logic
Setting a stretch goal of 70% automation in operations work while focusing humans on novel and complex security challenges
Measuring success through time-to-mitigation metrics and evaluating AI performance against human baseline capabilities
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