Code Intent Discovery Using Static and Runtime Security Signals
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Solution Overview
Problem
The deployment of code into computing environments introduces cybersecurity risks due to vulnerabilities in software, inadequate authentication, misconfigured access control, insecure APIs, and insufficiently secured endpoints, which can lead to exploitation and unauthorized access.
Innovation Solution
A system that detects code objects in a computing environment, analyzes their potential intents, generates cybersecurity signals based on event records, and initiates remediation actions when unexpected events occur, utilizing static analysis and runtime sensors to identify and mitigate threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If code is deployed into a computing environment, then the system gains functionality and productivity, but cybersecurity risks and vulnerabilities increase
Solution Approach 1:
The system performs static analysis on code objects before deployment to identify potential security vulnerabilities, insecure functions, and poor error handling. This preliminary detection allows developers to fix issues before the code is deployed into the computing environment, preventing security risks from being introduced in the first place.
Solution Approach 2:
The system continuously monitors deployed code in the computing environment by detecting events from runtime sensors and comparing them against expected behaviors. When unexpected events are detected, the system provides feedback by initiating remediation actions, creating a closed-loop security system that adapts to runtime conditions.
2Measurement precision
If static analysis is performed on code objects to detect vulnerabilities, then security detection capability is improved, but analysis time and processing complexity increase
Solution Approach 1:
The system segments the codebase into individual code objects (functions, classes, modules) and analyzes them independently. This segmentation allows the static analysis to focus on specific units of code rather than analyzing the entire codebase at once, reducing processing time while maintaining detection accuracy for each segment.
Solution Approach 2:
The system performs partial analysis by focusing static analysis on code objects that are most likely to contain security vulnerabilities based on their type, location, and historical data. Rather than analyzing every line of code equally, the system applies analysis selectively to high-risk areas, reducing overall analysis time while maintaining effective security detection.
3Reliability
If runtime sensors and event monitoring are implemented, then real-time threat detection is improved, but system overhead and resource consumption increase
Solution Approach 1:
The system implements event monitoring with local quality by focusing runtime sensors on specific critical operations and data flows within the computing environment. Rather than monitoring all system activities uniformly, the system concentrates monitoring resources on high-risk operations such as authentication, data access, and external communications, reducing overall system overhead while maintaining effective real-time detection.
Solution Approach 2:
The runtime sensors are designed with multi-functionality to perform multiple tasks simultaneously: detecting security events, collecting performance metrics, and gathering diagnostic information. This universal approach allows a single monitoring infrastructure to serve multiple purposes, reducing the need for separate specialized systems and thereby lowering overall system overhead.
4Reliability
If comprehensive code analysis and monitoring are performed, then security coverage is improved, but device complexity and processing requirements increase
Solution Approach 1:
The security system is segmented into distinct functional modules: static analysis engine, runtime sensor deployment, event detection system, and remediation orchestrator. Each module handles a specific aspect of security, allowing the comprehensive security coverage to be achieved through coordinated simple modules rather than a single complex system, thereby managing device complexity.
Solution Approach 2:
The system introduces an intermediary layer that sits between the code objects and the analysis/monitoring systems. This intermediary handles the complexity of coordinating static and runtime analysis, managing event correlations, and orchestrating remediation actions. By centralizing complex logic in this intermediary layer, the individual components remain simpler while the overall system achieves comprehensive security coverage.
Data Source
AI summary
A system and method for detecting code intent of code objects in a computing environment for assessing cybersecurity risk is presented. The method includes detecting a plurality of code objects in a computing environment; statically analyzing each code object of the plurality of code objects to determine a plurality of potential intents, each potential intent corresponding to an action; generating a cybersecurity signal in the computing environment based on a plurality of event records; detecting in the cybersecurity signal a first event corresponding to a potential intent; determining that the potential intent is a code intent based on the detection; detecting in the cybersecurity signal a second event which does not correspond to any potential intent; and initiating a remediation action in the computing environment based on the detected second event.