gapilongo/AegisGraph

AI Summary: Develop a Python framework for integrating various security tools into the AegisGraph project. This framework should include a base tool class, a registry for managing tools, and mechanisms for configuration, credential management, health monitoring, error handling, and efficient execution. The framework must ensure tool calls complete within 5 seconds and include comprehensive documentation.

AI Summary: Develop a correlation agent for a security orchestration framework (AegisGraph) that uses graph-based analysis to identify related security alerts across different systems and timeframes. The agent should consider entities (IP, user, host), configurable time windows, asset criticality, MITRE ATT&CK mapping, and provide visualization of results. Performance and accuracy targets are specified.

AI Summary: Develop a triage agent for a security operations center (SOC) framework. This agent should utilize a combination of rule-based filtering and large language model (LLM) analysis to prioritize alerts, reduce false positives, and improve the efficiency of SOC analysts. The agent needs to meet specific performance and accuracy targets, including processing alerts within one second and reducing false positives by 40%. It should also be configurable and support custom rules and machine learning models.

AI Summary: Develop an ingestion agent for the AegisGraph project that reliably pulls alerts from multiple SIEM systems (Splunk, QRadar, Sentinel). The agent must handle authentication, rate limiting, error handling, alert deduplication, and achieve a processing speed of 100 alerts per second. Integration tests with real SIEM systems are required.

AI Summary: Implement a robust logging infrastructure for the LangGraph SOC Triage & Orchestration Framework (AegisGraph) that supports structured JSON logging, request correlation IDs, log levels, sensitive data masking, log rotation, integration with a centralized logging system (like ELK or Splunk), and maintains performance under 5ms overhead.

AI Summary: Implement a hierarchical configuration management system for the AegisGraph project, supporting environment-specific settings, secure credential management (e.g., via HashiCorp Vault or AWS Secrets Manager), configuration validation, hot-reloading, and comprehensive testing. The system should handle environment variables, .env files, and dedicated configuration files.

AI Summary: Develop an abstract `BaseAgent` class in Python with a `run` method that takes and returns a `SOCState` object. The class should include common utilities for logging, error handling, and metrics, along with agent lifecycle management and health monitoring. Unit tests and documentation are also required.