AI Chatbot for Data Platform Security Breach Analysis
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Solution Overview
Problem
Data platforms face challenges in summarizing and explaining security breaches due to the complexity of multiple security microservices, requiring end users to perform extensive keyword searches and manual summaries, which is resource-intensive and time-consuming.
Innovation Solution
Implementing an artificial intelligence chatbot powered by a large language model (LLM) that integrates general, data platform-specific, and account-specific knowledge bases to provide natural language responses to security queries, simplifying the process of identifying and understanding security breaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple security microservices are deployed to provide comprehensive security analysis, then security coverage and detection capability are improved, but system complexity and difficulty of summarizing security breaches increase
Solution Approach 1:
The patent combines multiple security microservices into a unified chatbot interface that presents consolidated security breach information to users. The chatbot aggregates data from various microservices (ransomware detection, malware analysis, intrusion detection) and provides a single point of interaction, merging the complexity of multiple services into one unified interface while maintaining comprehensive security coverage
Solution Approach 2:
The chatbot acts as an intermediary layer between the complex security microservices and end users. It receives user queries, processes them through the underlying microservices, and returns simplified summaries of security breaches. This intermediary absorbs the complexity of multiple microservices while presenting a simple conversational interface to users
2Reliability
If comprehensive security analysis is provided through multiple microservices, then security detection capability is improved, but user understanding and identification of security risks becomes more difficult
Solution Approach 1:
The chatbot serves as an intermediary that translates complex security microservice outputs into user-friendly natural language responses. It processes technical security data from multiple microservices and presents simplified summaries that are easy for end users to understand and act upon
Solution Approach 2:
The system changes the parameter of information presentation from detailed technical security data to simplified natural language summaries. The chatbot transforms complex security breach information into concise, actionable insights that maintain detection accuracy while improving user comprehension
3Loss of information
If keyword searches are performed across multiple security microservices, then security breach information can be retrieved, but computing resource consumption and response time increase
Solution Approach 1:
The patent replaces the mechanical keyword search approach with an AI-based natural language processing system. Instead of requiring users to perform manual keyword searches across multiple microservices, the chatbot uses NLP to understand user intent and automatically retrieves relevant security breach information, significantly reducing response time and computational overhead
4Loss of information
If manual summarization of security breaches is required, then comprehensive security analysis is achieved, but user time and effort investment increase
Solution Approach 1:
The chatbot performs self-service by automatically summarizing security breach information from multiple microservices without requiring user intervention. It proactively analyzes security data, generates concise summaries, and presents them to users in natural language, eliminating the need for manual summarization efforts while maintaining comprehensive security analysis
Data Source
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AI summary
In general, techniques are described that enable a computing system to execute an artificial intelligence model for data security analysis. A computing system that includes a memory and processing circuitry may be configured to implement the techniques. The memory may store a query from an end user regarding security services provided by the data platform. The processing circuitry may parse the query to identify one or more intents, and process the one or more intents to retrieve data for formulating a natural language response to the query. The processing circuitry may also process, using a large language model, the intents and the data to generate the natural language response, and output the natural language response to a user interface for display to the end user.