Behavioral Security Control Using ML-Based Functional Role Detection
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Solution Overview
Problem
Conventional security platforms face inefficiencies and human errors in managing intrusive activities in computing resources, particularly those with machine learning-enabled services, due to manual and time-consuming processes for applying security controls, leading to increased resource consumption and reduced threat coverage.
Innovation Solution
Implementing a security control engine that utilizes machine learning models to identify functional roles of computing resources, apply security-related controls, and monitor for incompatibilities, thereby automating the detection and remediation of intrusive activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual security control processes are used by security professionals, then security controls can be applied to computing resources, but the process is time-consuming and strains human resources, decreasing overall effectiveness and security control coverage
Solution Approach 1:
The system enables self-service by automatically identifying functional roles of computing resources and determining appropriate security controls without requiring continuous human intervention. The machine learning model autonomously analyzes resource behavior patterns and applies security controls, allowing the security system to serve itself rather than relying on manual security professional intervention for each control application.
Solution Approach 2:
The patent replaces the mechanical manual process of security control application with an automated machine learning-based system. The machine learning model substitutes human security professionals in analyzing computing resource behavior and determining security controls, transforming the manual mechanical process into an automated intelligent system that operates without human physical intervention.
2Reliability
If manual security control processes are used, then security controls can be applied, but human errors occur and human resources are strained, reducing security control coverage
Solution Approach 1:
The patent replaces the complex manual security management process with an automated machine learning system. The machine learning model eliminates human errors by substituting human judgment with algorithmic analysis, and reduces operational complexity by automating the entire workflow from resource identification to security control application, requiring no manual intervention despite the sophisticated analysis performed.
3Reliability
If conventional security platforms are used with manual processes, then security monitoring is performed, but resource consumption increases and threat coverage is reduced
Solution Approach 1:
The system applies partial action by focusing security analysis only on the functional roles actually performed by computing resources rather than monitoring all possible activities. The machine learning model identifies specific functional roles and applies security controls targeted to those roles, avoiding excessive resource consumption that would result from comprehensive monitoring of all computing resource activities.
Data Source
AI summary
Baseline data characterizing actions of one or more computing resources associated with one or more entities is received. One or more functional roles performed by the one or more computing resources are identified using a machine learning model, wherein the baseline data is provided as input to the machine learning model. A security-related control to be applied to the one or more computing resources is identified based on the one or more functional roles. The security-related control is applied to the one or more computing resources.