Dynamic Compact Cybersecurity Policy Generation Across Platforms
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Solution Overview
Problem
Existing DLP platforms face challenges in balancing security with employee productivity, leading to issues like false positives and inefficiencies due to overly restrictive policies, especially in complex environments with remote work and cloud adoption, which increase the risk of blind spots in data protection.
Innovation Solution
A system utilizing generative artificial intelligence (AI) to convert cybersecurity policies from a first policy language to a second policy language, accommodating different constraints and intents, thereby generating optimized policies for various digital security platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If overly restrictive DLP policies are implemented to enhance security, then data protection is improved, but employee productivity deteriorates due to blocked legitimate activities
Solution Approach 1:
The patent implements dynamic policy generation using generative AI that adapts policies based on contextual factors such as user role, data sensitivity, and operational needs. Instead of static restrictive policies, the system dynamically adjusts policy strictness to balance security requirements with productivity needs, allowing legitimate business activities while preventing actual data breaches.
Solution Approach 2:
The system changes policy parameters dynamically by generating multiple policy versions with different constraint levels and selecting the most appropriate one based on current context. This allows the organization to maintain strong data protection where needed while permitting greater flexibility for legitimate business operations, thereby resolving the contradiction between security and productivity.
2Adaptability or versatility
If multiple policy languages are used across different security platforms, then platform-specific requirements are met, but policy management complexity increases
Solution Approach 1:
The patent introduces a generative AI intermediary that sits between the centralized policy source and multiple security platforms. This AI intermediary automatically translates and adapts policies into the specific syntax and constraint requirements of each platform (e.g., Azure AD, Okta, OneLogin), eliminating the need for manual policy rewriting and reducing management complexity while maintaining platform compatibility.
Solution Approach 2:
The system creates a universal policy generation capability that can output policies for multiple different platforms from a single source. The generative AI model is trained on multiple policy languages and can generate context-appropriate policies for any target platform, making the policy management system universally applicable across diverse security infrastructures.
3Measurement precision
If manual policy translation and adaptation is performed, then policy accuracy can be maintained, but time consumption and operational overhead increase
Solution Approach 1:
The patent replaces the manual mechanical process of policy translation with an automated generative AI system. The AI model has been trained to understand policy intent and accurately translate it into platform-specific policies, maintaining high accuracy while eliminating the time-consuming manual work. This substitution of human effort with intelligent automation resolves the contradiction between accuracy and speed.
Solution Approach 2:
The system performs preliminary training of the generative AI model on extensive policy translation tasks before deployment. This preliminary action equips the AI with the knowledge to accurately translate policies without requiring manual intervention during actual policy updates, thereby maintaining accuracy while significantly reducing implementation time.
Data Source
AI summary
A system and method for dynamically generating a compact cybersecurity policy based on an original policy is presented. The method includes receiving a cybersecurity policy in a first policy language, the first policy language including a first data format; determining a constraint of a second policy language; generating a context for a generative artificial intelligence (AI) based at least on the determined constraint; configuring the generative AI to generate a second cybersecurity policy in the second policy language based on the determined constraint and the generated context; and applying the generated second cybersecurity policy in a cybersecurity platform configured to utilize the second policy language.