Adaptive Cybersecurity Policy Learning for Real-Time Threat Response
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Solution Overview
Problem
Conventional cybersecurity policy management systems fail to adapt dynamically to evolving security landscapes, leading to implementation gaps, configuration vulnerabilities, and inadequate protection against emerging threats due to static policies, complex vendor ecosystems, and overwhelming exposure volumes.
Innovation Solution
A system incorporating an Adaptive Learning System with a Large Language Model (LLM) and centralized policy hub that processes contextual data to automate policy validation, remediation, and exposure management, leveraging modular workflows and continuous feedback loops for real-time security posture adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static cybersecurity policies are implemented with periodic reviews, then policy documentation is maintained, but the system cannot adapt dynamically to evolving security threats and organizational requirements
Solution Approach 1:
The patent transforms static cybersecurity policies into dynamic, adaptive policies that automatically adjust to changing threats and organizational contexts. The system continuously monitors security exposures, threat intelligence, and organizational changes, then automatically updates security controls and policies in real-time, eliminating the need for manual periodic reviews while maintaining adaptability.
Solution Approach 2:
The system implements continuous feedback loops where security exposures, threat intelligence, and policy effectiveness data are constantly collected, analyzed, and used to automatically adjust security policies. This closed-loop feedback mechanism enables the system to learn from past security events and proactively adapt to new threats without increasing operational complexity for users.
2Reliability
If comprehensive security monitoring is implemented across diverse vendor solutions, then security visibility is improved, but the complexity of managing multiple vendor configurations increases
Solution Approach 1:
The patent consolidates monitoring and management of multiple diverse vendor security solutions into a single unified platform. The system integrates with various security vendors through standardized connectors, aggregating security data, exposures, and configurations into one centralized view, thereby improving security visibility while eliminating the complexity of managing each vendor solution separately.
Solution Approach 2:
The system acts as an intermediary layer between organizational security requirements and multiple vendor solutions. It translates organizational security policies into vendor-specific configurations and vice versa, abstracting away vendor-specific complexities while maintaining comprehensive visibility across the entire security ecosystem through a unified interface.
3Reliability
If security administrators enable comprehensive security features, then protection coverage is improved, but false positives and productivity disruptions increase
Solution Approach 1:
The system applies security controls with local precision rather than blanket application. It analyzes organizational context, asset criticality, user roles, and specific security exposures to tailor security measures to each particular situation. This contextualized approach maximizes protection coverage for critical assets while minimizing false positives and productivity impact for lower-risk operations.
Solution Approach 2:
The system dynamically adjusts security control parameters based on real-time analysis of security exposures, threat levels, and organizational context. Rather than maintaining fixed security settings, it continuously optimizes control sensitivity, blocking thresholds, and alert priorities to achieve optimal balance between protection coverage and operational smoothness, reducing false positives while maintaining robust security.
4Productivity
If manual policy updates are performed on fixed schedules, then policy review consistency is maintained, but the system cannot respond in real-time to emerging security exposures
Solution Approach 1:
The system performs preliminary security assessments and policy adjustments automatically based on pre-configured security rules, threat intelligence feeds, and organizational policies. It proactively identifies security exposures and applies appropriate controls before threats can exploit them, eliminating the need for reactive manual updates while maintaining consistent policy application across the organization.
Solution Approach 2:
The system replaces periodic manual policy reviews with continuous automated security monitoring and policy management. It constantly scans for security exposures, evaluates emerging threats, and updates security controls in real-time without interruption, ensuring uninterrupted security protection and eliminating the gaps inherent in scheduled review approaches.
Data Source
AI summary
A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.