LLM Database Querying with Dynamic Cybersecurity Policy Generation
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Solution Overview
Problem
The challenge of applying a unified cybersecurity policy across diverse computing environments with varying technological landscapes, security tools, compliance standards, and operational management complexities leads to potential security vulnerabilities and inconsistencies.
Innovation Solution
A system utilizing a large language model (LLM) generates policies by processing natural language queries, leveraging a unified data schema and policy engine to ensure consistent security standards across on-premises and cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a unified security policy is applied across diverse computing environments, then security consistency is improved, but the complexity of integration and interoperability increases
Solution Approach 1:
The patent introduces a policy translation layer that acts as an intermediary between the unified security policy and diverse computing environments. This translation layer converts high-level security requirements into environment-specific configurations, enabling consistent security enforcement without direct complex integrations between each environment and the policy source.
Solution Approach 2:
The system implements a universal policy framework that can be applied across multiple computing environments (cloud, on-premises, hybrid) through a common interface. The unified security policy is designed to be environment-agnostic, allowing the same policy to be enforced across diverse platforms without requiring separate policy implementations for each environment.
2Adaptability or versatility
If environment-specific security configurations are maintained, then local security requirements are met, but achieving unified security management becomes more difficult
Solution Approach 1:
The patent segments security management into two distinct layers: a unified policy definition layer that handles high-level security requirements, and environment-specific configuration layers that handle local implementations. This segmentation allows administrators to manage security at the unified level while automatically generating appropriate configurations for each environment, reducing management complexity.
Solution Approach 2:
The system performs preliminary actions by automatically generating environment-specific security configurations before deployment. The policy translation layer pre-processes unified security policies into environment-appropriate formats, validating and adapting configurations in advance, which reduces the need for manual adjustment and simplifies ongoing management.
3Reliability
If manual policy adaptation is performed for each environment, then security compliance is maintained, but time and operational resources are consumed
Solution Approach 1:
The patent implements self-service automation where the policy translation layer automatically adapts unified security policies to environment-specific configurations without manual intervention. The system autonomously generates, validates, and updates security configurations across diverse environments, eliminating the need for manual policy adaptation while maintaining compliance accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor policy implementation across different environments and automatically adjust configurations as needed. The policy translation layer receives feedback from environment-specific security tools and continuously optimizes translations, ensuring ongoing compliance without requiring manual reconfiguration when environments change.
Data Source
AI summary
A system and method for dynamically applying controls on a representation of a computing environment utilizing a large language model is presented. The method includes generating a representation of the computing environment, the representation based on a unified data schema; receiving a natural language query directed to the computing environment; generating a first prompt for the large language model (LLM), which when processed outputs an identifier of a policy from a policy engine; generating a second prompt for the LLM, which when processed outputs a generated policy, based on: the unified data schema, and the natural language query; and applying the generated policy on the representation of the computing environment.