Part 1: Overview and Business Context

Executive Summary

In today's cybersecurity landscape, organisations face an ever-increasing volume of security alerts, logs, and potential threats. Security teams often struggle to process this information efficiently, leading to alert fatigue, delayed responses, and potential security gaps. At Pebble we have developed an automated AI Cyber Analyst Agent that combines artificial intelligence, workflow automation, and security tools to enhance security operations.

Yes, there are vendor solutions that allow you to do similar things but they come at a hefty price and lock you into yet another vendor and their tools. Our approach is to use open source tools to create a process that you own and that you can evolve and modify as your requirements change.

Current Challenges in Security Operations

Manual Process Limitations

1. Alert Volume
   • Security analysts typically process hundreds to thousands of alerts daily
   • Manual investigation of each alert is time-consuming
   • High risk of alert fatigue and missed threats
2. Data Correlation
   • Analysts must manually correlate data from multiple sources
   • Time-consuming process of switching between different tools
   • Potential for human error in data interpretation
3. Response Time
   • Average time to respond to incidents can be hours or days
   • Manual documentation and reporting processes slow down response
   • Limited ability to handle multiple incidents simultaneously
4. Resource Constraints
   • Shortage of skilled security analysts
   • High cost of 24/7 security operations
   • Training and maintaining expertise across multiple tools

The Automated Solution

Overview of the AI Cyber Analyst Agent

Our automated solution combines several key technologies to create an intelligent security analysis system:

1. Intelligent Analysis
   • AI-powered analysis of security logs and alerts
   • AI interpretation of external data
   • Automated correlation of threats across multiple data sources
   • Context-aware decision making using local language models
2. Workflow Automation
   • Automated data collection and preprocessing
   • Orchestrated response actions
   • Integrated reporting and documentation
3. Continuous Monitoring
   • 24/7 automated security monitoring
   • Real-time threat detection and analysis
   • Automated escalation of significant threats

Key Components

1. Data Collection and Analysis
   • Centralised log management and analysis
   • External threat intelligence integration
   • Automated asset discovery and monitoring
2. Intelligence Processing
   • Local AI processing for sensitive data
   • Multi-agent collaboration for complex analysis
   • Automated report generation
3. Response Automation
   • Automated alert triage and prioritisation
   • Orchestrated incident response workflows
   • Automated documentation and reporting