Neural Dialogue System for Security Ruleset Management
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Solution Overview
Problem
Existing dialogue systems struggle to efficiently manage large and dynamic security rulesets, leading to issues like misconfiguration, policy bloat, and policy sprawl.
Innovation Solution
A neural dialogue system is designed to provide a conversational user interface for security posture management, utilizing natural language understanding, intent classification, and entity extraction to ground user inputs into organizational contexts and determine the lowest impact implementation for ruleset edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rule-based dialogue systems are used for security posture management, then the system structure is simple and easy to implement, but the system cannot efficiently handle large and dynamic security rulesets, leading to misconfiguration and policy bloat
Solution Approach 1:
The patent replaces rule-based mechanical dialogue systems with neural network-based AI systems. The neural network model processes natural language queries, extracts entities, and determines intent automatically, eliminating the need for complex manual rule configuration while improving reliability in handling large security rulesets.
Solution Approach 2:
The system enables self-service through natural language interaction. Users can query and manage security posture without requiring technical expertise in ruleset configuration. The neural network automatically interprets user intentions and executes appropriate operations, making the system accessible to non-technical users while maintaining reliable ruleset management.
2Reliability
If statistical data-driven dialogue systems with neural networks are used, then the system can handle complex security rulesets and provide intelligent responses, but the system complexity increases significantly
Solution Approach 1:
The patent segments the complex neural network system into distinct functional modules: natural language processing module for query interpretation, entity extraction module for identifying security objects, intent determination module for understanding user goals, and ruleset management module for executing operations. This segmentation makes the complex system more manageable and maintainable.
Solution Approach 2:
The patent introduces an intermediary ruleset manager component that bridges the neural network AI layer and the underlying security ruleset storage. This intermediary layer translates high-level natural language queries into specific ruleset operations, managing complexity by separating the intelligent decision-making from the execution logic.
3Device complexity
If manual management of large security rulesets is performed, then the system structure remains simple, but policy sprawl and misconfiguration occur due to the vast number of rules
Solution Approach 1:
The patent implements feedback mechanisms where the neural network continuously learns from user interactions and ruleset changes. The system provides feedback to users about rule impacts and automatically adjusts to new security policies, preventing policy bloat by identifying and removing redundant rules while maintaining simple system architecture through automated management.
Data Source
AI summary
A neural dialogue system has been designed to present a conversational user interface for managing security posture of an organization. The neural dialogue system determines intent and extracts entity names from a user input. The neural dialogue system grounds the entity names to an organizational context and maps the intent to a defined functionality related to security posture management for intent realization. Some of these defined functions involve editing the ordered ruleset. When realization of an intent involves editing the ordered ruleset, the neural dialogue system determines various ruleset command sequences that implement the ruleset editing and corresponding impacts on the ordered ruleset (e.g., creation of ruleset anomalies). The dialogue system collects the information retrieved based on the intent and grounded entities, including any ruleset impact assessment, and generates a response.