# Optimizing Security Incident Response with LLMs at Google ## Overview Google has implemented a sophisticated LLM-based system to enhance their security incident response workflows. The primary focus was on automating and improving the creation of incident summaries and executive communications, which traditionally required significant time investment from security professionals. The case study demonstrates a careful approach to implementing LLMs in a sensitive security context while maintaining high standards for accuracy and data privacy. ## Technical Implementation ### Input Processing System - Developed structured input processing to handle various data types: ### Prompt Engineering Journey - Initial Version: - Second Version: - Final Version: ### Production System Design - UI Integration: ### Safety and Privacy Measures - Implemented strict data protection: ### Quality Control and Risk Mitigation - Established comprehensive safeguards: ## Performance Metrics and Results ### Summary Generation - 51% reduction in time spent per incident summary - 10% higher quality ratings compared to human-written summaries - Comprehensive coverage of key points - Consistent adherence to writing standards ### Executive Communications - 53% time savings for incident commanders - Maintained or exceeded human-level quality - Improved adherence to writing best practices - Better standardization of communication format ### Quality Assessment - Conducted comparison study: ### Edge Cases and Limitations - Identified challenges with small input sizes: ## Best Practices and Lessons Learned ### Prompt Design Principles - Progressive refinement approach - Importance of example-based learning - Clear structural guidelines - Balance between specificity and flexibility ### System Architecture Considerations - Privacy-first design - Human-in-the-loop workflow - Flexible review and modification options - Clear error handling procedures ### Production Deployment Strategy - Phased implementation - Continuous monitoring - Regular quality assessments - Feedback incorporation system ## Future Developments ### Planned Expansions - Exploration of memory safety projects - Code translation capabilities (C++ to Rust) - Security recommendation automation - Design document analysis ### Ongoing Improvements - Refinement of prompt engineering - Enhancement of input processing - Expansion to additional use cases - Integration with other security workflows