Security Investigation Summarization With Explainable AI Workflows
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Solution Overview
Problem
Cybersecurity platforms lack transparency and explainability, especially with AI systems, leading to unclear incident resolution and limited understanding for non-specialists, and traditional methods are inefficient and lack adaptive responses.
Innovation Solution
An automated summarization and explanation system using AI techniques, including a retrieval augmented generation architecture and tokenization framework, provides transparent and adaptive security threat detection and response, integrating a unified interface, dynamic mapping, temporal reasoning, and natural language summarization to enhance explainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual review methods are used for security investigations, then detailed analysis can be performed, but efficiency and productivity are reduced
Solution Approach 1:
The system enables self-service through automated investigation execution where the AI agent independently performs security investigations by automatically executing queries against data sources, generating timelines, and producing summaries without requiring manual intervention for routine tasks
Solution Approach 2:
Manual mechanical processes are replaced with automated AI-based systems that use large language models to execute investigations, query data sources, and generate reports, substituting human manual operations with automated intelligent systems
2Productivity
If AI systems are used for security investigations, then productivity is improved, but explainability and transparency are reduced
Solution Approach 1:
The system implements feedback mechanisms where the AI agent provides explanations for its investigation decisions, actions taken, and reasoning processes, allowing users to understand the AI's decision-making logic and receive feedback about the investigation methodology
Solution Approach 2:
An intermediary explanation layer is introduced between the AI investigation system and users, providing transparent documentation of the investigation process, data sources queried, and reasoning used to reach conclusions, making the AI system's operations interpretable
3Measurement precision
If detailed technical information is provided in summaries, then accuracy is improved, but ease of operation for non-specialists is reduced
Solution Approach 1:
The system applies local quality by tailoring the level of detail and technical complexity in summaries to match the specific user's expertise level, providing detailed technical information to specialists while presenting simplified explanations to non-specialists
Solution Approach 2:
The system changes the parameter of information complexity dynamically based on user characteristics, adjusting the summary's technical depth, terminology, and detail level according to the user's role and expertise to optimize both accuracy and accessibility
Data Source
AI summary
In some implementations, a cybersecurity system is provided for summarizing network security investigations. The system receives a request to summarize an investigation sequence performed in response to a computer security incident, retrieves tokenized elements that correspond to the investigation sequence, and provides the tokenized elements to a large language model (LLM) for translation into a data operation format. The system receives, from the LLM, and for each tokenized element, a corresponding translated data operation. For each translated data operation, the system submits the translated data operation for execution by a data source, and receives a corresponding data operation response. The system performs a summarization process of the investigation sequence, and outputs a natural language summarization.