ML-Driven Vulnerability Exception Workflow for Cloud Security Review
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Solution Overview
Problem
Cloud-based applications and images experience security vulnerabilities due to outdated software and lack of periodic refresh, leading to increased cyberattack susceptibility and inefficient manual exception processes that consume resources and may abuse the exception system.
Innovation Solution
A system utilizing a machine learning model to generate draft exception requests and risk scores, reducing user and administrator processing loads by automating the exception process and identifying repeat offenders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual exception processes are used to handle security vulnerabilities, then users and administrators can review and approve exceptions, but processing resources are consumed and the process becomes inefficient
Solution Approach 1:
The machine learning model performs preliminary action by automatically generating draft exception requests and risk scores before human review. The system pre-processes vulnerability data, generates justifications, and calculates risk assessments in advance, so that when administrators review exceptions, the foundational work is already complete, reducing their processing burden and improving overall efficiency.
Solution Approach 2:
The system implements self-service through automated machine learning-based exception generation. The ML model autonomously analyzes vulnerability data, generates exception requests, and assigns risk scores without requiring manual intervention for these preliminary steps. This self-service capability handles routine processing while human administrators focus only on reviewing and approving exceptions, significantly improving productivity.
2Productivity
If automated exception processes are implemented, then processing resources are reduced, but the system complexity increases due to machine learning integration
Solution Approach 1:
The machine learning model serves as an intermediary component between the vulnerability detection system and the exception approval process. It mediates by automatically generating draft exceptions and risk scores, translating complex vulnerability data into actionable recommendations. This intermediary role simplifies the overall system architecture by encapsulating complex ML logic in a dedicated component rather than distributing complexity throughout the entire system.
Solution Approach 2:
The system uses copying by generating draft exception requests that replicate the structure and content of formal exception requests. The ML model creates simplified copies of exception documentation that capture essential information, allowing administrators to review and approve without processing the full complexity of the underlying vulnerability data, thus reducing perceived system complexity.
3Loss of time
If exception requests are approved without thorough review, then processing time is reduced, but security risks increase due to potential abuse
Solution Approach 1:
The system implements feedback through automated risk score calculation and repeat offender detection. The machine learning model continuously analyzes exception requests and provides feedback in the form of risk scores and identifiers for repeat offenders. This feedback mechanism allows administrators to quickly identify high-risk exceptions and prioritize their review, reducing processing time for low-risk items while maintaining security through targeted human oversight of potentially harmful exceptions.
Data Source
AI summary
In some implementations, an exception system may receive an indication of a security vulnerability associated with a cloud environment. The exception system may provide a data structure that represents the security vulnerability to a machine learning model in order to receive a suggested exception justification. The exception system may output, to a user device associated with a user assigned to the security vulnerability, a draft exception request that includes the suggested exception justification. The exception system may receive, from the user device, a request to submit an exception request based on the draft exception request. The exception system may transmit a command to tag the security vulnerability as excepted.