AI Zero-Trust Source Code Monitoring for Execution Anomalies
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Solution Overview
Problem
Current zero-trust security models do not adequately address security risks associated with source codes executed in managed compute facilities, leading to potential breaches and vulnerabilities.
Innovation Solution
Implementing zero-trust principles using artificial intelligence algorithms to analyze source codes and their execution behaviors, including natural language processing and machine learning techniques to attribute codes to developers, monitor coding and execution patterns, and establish baseline profiles to detect anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If zero-trust security models are implemented for network access control, then network security is improved, but source code security risks are not addressed
Solution Approach 1:
The patent segments the security model into two distinct layers: network-level zero-trust security for access control and source code-level AI analysis for code-specific threats. This allows each layer to specialize in its domain while working together comprehensively.
Solution Approach 2:
The AI system is designed to perform multiple functions including code attribution, anomaly detection, security risk identification, and behavioral analysis, making it a universal solution for source code security across different programming languages and development scenarios.
2Measurement precision
If AI algorithms are used to analyze source code and execution behaviors, then security risk detection capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces execution environment intermediaries that act as mediators between the source code and the AI analysis system. These intermediaries capture execution behaviors and translate them into analyzable data, simplifying the overall system architecture while maintaining high detection accuracy.
Solution Approach 2:
Traditional rule-based code analysis mechanisms are replaced with AI/ML algorithms that automatically learn security patterns and anomalies from data, reducing the need for manual configuration and complex rule sets while improving detection precision.
3Reliability
If continuous monitoring of source code development and execution is implemented, then security breach detection is improved, but computational resource consumption increases
Solution Approach 1:
The system implements periodic sampling of code execution behaviors rather than continuous uninterrupted monitoring. AI analysis is performed at strategic intervals and triggered by specific events, reducing computational overhead while maintaining effective security detection.
Solution Approach 2:
The AI system focuses analysis on partial but critical aspects of code execution such as unusual API calls, unexpected control flow changes, and anomalous data access patterns, rather than analyzing every single operation, optimizing the balance between detection capability and resource consumption.
Data Source
AI summary
Computer system security is improved by implementing zero-trust principles for management of source codes in a software development lifecycle. In some embodiments, a source code may be processed with a natural language processor to attribute the source code to its particular developer. Further, a machine learning algorithm may be applied to data related to the coding behavior of the code developer to identify any behavioral anomalies of the code developer in developing the source code. In addition, the interaction, of applications executing in an execution environment of an organization's network of managed compute facilities, with the various components of the network may be analyzed with a machine learning algorithm to identify code execution anomalies.