Automated Policy Agents for Adaptive Security Enforcement
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Solution Overview
Problem
Current policy management systems face challenges in authoring, managing, and enforcing complex security policies across dynamic and interconnected IT environments, requiring significant manual effort and skilled human resources, which is costly and error-prone, and lacks adaptive intelligence for real-time updates and contextual responses.
Innovation Solution
A method and system for automatically configuring action determination models for agents to execute actions in environments, using processor-based simulations and reinforcement learning to adjust models based on objectives and results, enabling adaptive and intelligent policy management and automation of vulnerability assessments and penetration testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual policy management approaches are used, then policies can be authored and enforced, but the process becomes time-consuming, error-prone, and requires scarce skilled human resources
Solution Approach 1:
The system enables self-service through automated policy authoring agents that autonomously generate, validate, and enforce security policies without requiring manual intervention from skilled security professionals. The agents continuously monitor system states and automatically adjust policies to maintain security requirements.
Solution Approach 2:
Manual mechanical processes of policy authoring and management are replaced with intelligent software agents that use machine learning and automated reasoning to generate and enforce policies. This substitution eliminates human error and significantly reduces the time required for policy management while maintaining consistency.
2Reliability
If complex security policies are implemented to capture business requirements and compliance rules, then security protection is enhanced, but policy complexity increases making management and enforcement difficult
Solution Approach 1:
Complex security policies are segmented into modular rule sets that can be independently managed and enforced. The system breaks down comprehensive security requirements into discrete, manageable policy components that can be automatically generated and validated by agents, reducing overall system complexity while maintaining security effectiveness.
Solution Approach 2:
Policy authoring agents serve as intermediaries between business requirements and technical policy enforcement. These agents translate high-level security goals and compliance rules into enforceable policy statements, managing complexity by handling the translation and validation processes automatically without requiring direct human intervention in complex policy authoring.
3Reliability
If manual penetration testing and vulnerability assessment are performed, then security weaknesses can be identified, but the process is costly and requires highly skilled cybersecurity experts
Solution Approach 1:
The system creates virtual copies of security testing capabilities through automated agents that replicate the functions of human penetration testers. These agents can perform vulnerability assessments and penetration testing tasks automatically, maintaining detection accuracy while eliminating the need for scarce skilled human security testers and reducing costs.
Solution Approach 2:
Security systems perform self-assessment through automated agents that continuously monitor for vulnerabilities and security weaknesses without requiring external human intervention. The agents use machine learning to identify security issues autonomously, making security testing accessible and affordable without requiring expensive skilled professionals.
4Reliability
If policies need to be enforced consistently across many interconnected computing devices, then security coverage is improved, but the complexity of ensuring correct implementation increases
Solution Approach 1:
Policy authoring agents are designed as universal components that can operate across diverse interconnected systems and devices. The agents implement a standardized approach to policy enforcement that can be applied consistently across different platforms and technologies, simplifying implementation while maintaining broad security coverage across the entire system of systems.
Data Source
AI summary
A method of automatically configuring an action determination model includes determining an environment model, determining an action determination model that indicates an action option, determining whether the action determination model indicates a next action option, and if so, determining an action based on the action determination model, simulating execution of the action across the environment model, obtaining a simulated result, adjusting the action determination model. Then, until environment or an agent reach an end state, the following are repeated: determining whether the action determination model indicates the next action option, and if so, determining the action based on the action determination model, simulating the execution of the action across the environment model, obtaining the simulated result, and adjusting the action determination model.