Automated API Security Assessment System with Risk Profiling
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Solution Overview
Problem
Current API security assessment methods are inefficient, inaccurate, and not scalable, requiring multiple approaches that lead to low confidence remediation and are time-consuming, prone to errors, and complex, necessitating a holistic and automated system for comprehensive security analysis.
Innovation Solution
An API assessment system utilizing a processor, data corpus builder, data classifier, risk profiler, and data rectifier with cognitive learning operations to create a sequence classification model, risk profile, and rectification model for real-time security assessment and remediation, enabling automated detection and mitigation of security risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple API security assessment approaches are deployed, then coverage of security concerns is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent combines multiple API security assessment approaches (penetration testing, design document review, source code analysis, and runtime monitoring) into a single unified platform that orchestrates all these methods through a common architecture, reducing overall system complexity while maintaining comprehensive security coverage
Solution Approach 2:
The assessment platform is designed as a universal system that can perform multiple security assessment functions (static analysis, dynamic analysis, penetration testing, and monitoring) through a single integrated architecture, eliminating the need for separate complex systems for each assessment type
2Measurement precision
If traditional API security assessment methods are used, then security issues can be detected, but assessment time and resource consumption increase
Solution Approach 1:
The system performs preliminary static analysis and source code review during the API development phase before deployment, enabling early detection of security issues without requiring time-consuming penetration testing and monitoring in production environments
Solution Approach 2:
The patent replaces manual security assessment processes (human code review, manual penetration testing) with automated computational analysis systems that can rapidly analyze API code and behavior, significantly reducing assessment time while maintaining or improving detection accuracy
3Measurement precision
If API security assessment is performed manually, then detailed analysis can be conducted, but error rate increases and scalability decreases
Solution Approach 1:
The assessment platform incorporates automated self-correction capabilities where the system can automatically remediate certain security issues without human intervention, and automatically learn from assessment results to improve future analyses, enabling scalable operation without proportional increases in human resources
Solution Approach 2:
The system replaces manual security analysis with automated computational methods including static code analysis, dynamic runtime monitoring, and machine learning-based threat detection that can process large numbers of APIs simultaneously without human error and with consistent accuracy
4Ease of manufacture
If security assessment is performed late in the development cycle, then fewer changes are needed, but remediation complexity increases
Solution Approach 1:
The system performs security assessments during the API design and development phases before deployment to production, enabling security issues to be identified and remediated early when changes are simplest and most cost-effective
Solution Approach 2:
The assessment platform provides continuous feedback to developers during the API creation process, enabling real-time correction of security issues as they are introduced, rather than requiring complex remediation after deployment
Data Source
AI summary
Examples of an API assessment system are provided. The system may obtain a security assessment requirement from a user for surveillance of a plurality of application programming interfaces. The system may create a data corpus from the assessment data associated with the query. The system may create a sequence classification model from the data corpus. The system may identify a plurality of risk parameters and a plurality of risk mapping levels associated with the query. The system may create a risk profile for the plurality of application programming interfaces based on mapping the plurality of risk parameters to the plurality of risk mapping levels and the sequence classification model. The system may create a rectification corpus and a data rectification model comprising a plurality of remediations for automated healing of a risk identified by the risk profile. The system may generate a security assessment result for the resolution of the query.