Machine Learning Security Policy Modification
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Solution Overview
Problem
Complex computer security policies in organizations increase the likelihood of cyber security breaches due to complicated permissions and access grants, making it difficult to identify and control potential security incidents.
Innovation Solution
A machine learning model is used to analyze computer security policies by converting them into graph structures and embeddings, allowing the system to identify unsafe combinations of access grants and recommend modifications to prevent incidents without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computer security policies include more permissions and access grants to govern more computing systems, then the coverage and functionality of security policies improve, but the complexity of the policies increases and the likelihood of cyber security breaches increases
Solution Approach 1:
The patent segments complex security policies into individual permission entities, each representing a specific access grant. The machine learning model processes these segmented permission entities independently, analyzing their individual risk profiles rather than attempting to analyze the entire complex policy at once. This segmentation enables the system to handle large numbers of permissions while maintaining analytical tractability.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the complex security policies and the security analysis process. This intermediary automatically processes and evaluates permission combinations, identifying unsafe configurations without requiring manual analysis. The ML model acts as a mediator that translates complex policy data into actionable security insights.
2Adaptability or versatility
If computer security policies include more permissions and access grants, then the functionality and access control capabilities improve, but the difficulty of identifying unsafe combinations increases
Solution Approach 1:
The patent replaces manual security analysis with an automated machine learning-based system. Instead of relying on security experts to manually review and identify unsafe permission combinations, the system uses ML algorithms to automatically detect risky configurations. This substitution of mechanical human analysis with automated computational analysis significantly improves the ability to detect unsafe combinations in large-scale environments.
Solution Approach 2:
The system enables self-service security analysis by automatically processing security policies and generating risk assessments without requiring manual intervention. The machine learning model autonomously evaluates permission combinations, identifies unsafe configurations, and provides recommendations, allowing the security system to serve itself rather than requiring constant human oversight.
3Ease of operation
If manual methods are used to analyze and modify security policies, then user control and understanding are maintained, but the system cannot automatically identify and modify unsafe policies at scale
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously learns from security incidents and policy modifications. The system provides feedback loop capabilities that allow it to improve its analysis over time, while still maintaining the ability to operate autonomously at scale. This feedback-driven approach enables both automation and continuous improvement without sacrificing user oversight capabilities.
Data Source
AI summary
In some aspects, a computing system may use a machine learning model to determine whether a computer security policy should be modified to reduce the likelihood of a cyber security incident. Through the use of a machine learning model, unsafe combinations of access grants or permissions may be identified and modified to prevent cyber security incidents from occurring. The computing system may input a representation of a computer security policy into a machine learning model, which has been trained on a dataset that includes representations of computer security policies. The computing system may generate output indicating a likelihood that the first computing system will be involved in a cyber security incident. Based on the output satisfying a first threshold, the computing system may generate a recommendation to modify the first computer security policy. The computing system may modify the first computer security policy based on the recommendation.