SBOM Risk Scoring for Vulnerability-Driven Software Updates
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Solution Overview
Problem
Modern software applications, built with reusable components, are susceptible to security threats due to increased attack surfaces and vulnerabilities in cloud-native architectures, posing a risk of catastrophic security breaches.
Innovation Solution
A risk assessment engine calculates a risk score for software applications using Software Bill of Materials (SBOM) data, identifying vulnerabilities and updates to reduce the risk score below a threshold, and an access management engine controls access based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reusable components and cloud-native architectures are adopted to enable rapid application development, then productivity and flexibility are improved, but the attack surface increases and security vulnerabilities are worsened
Solution Approach 1:
The system performs preliminary risk assessment by analyzing Software Bills of Materials (SBOM) to identify vulnerabilities in reusable components before they are deployed to production environments. This proactive approach allows organizations to address security issues in advance, preventing attacks rather than reacting to them after deployment.
Solution Approach 2:
The system continuously monitors and updates risk scores by comparing SBOM data against known vulnerability databases. When new vulnerabilities are discovered in reusable components, the system automatically updates the risk assessment and notifies relevant stakeholders, creating a closed-loop feedback mechanism that improves security posture over time.
2Reliability
If comprehensive vulnerability assessment of all software components is performed, then security reliability is improved, but system complexity and assessment time are worsened
Solution Approach 1:
The system applies different levels of assessment rigor to different components based on their risk profiles. High-risk components with known vulnerabilities receive more intensive scrutiny, while low-risk components undergo streamlined assessment. This differentiated approach maintains comprehensive security coverage while reducing overall system complexity.
Solution Approach 2:
The system dynamically adjusts assessment parameters such as risk thresholds, component prioritization criteria, and update frequencies based on organizational context and threat landscapes. This flexibility allows the system to adapt to changing security requirements without requiring complete reassessment of all components, simplifying the overall process.
3Reliability
If frequent updates are applied to reduce vulnerability risk scores, then security reliability is improved, but loss of time and operational disruption are worsened
Solution Approach 1:
The system identifies and prioritizes only the critical updates necessary to address high-risk vulnerabilities, rather than requiring implementation of all available updates. This selective approach allows organizations to address the most pressing security issues with minimal operational disruption while maintaining acceptable security posture.
Solution Approach 2:
The system automatically generates update recommendations and can even automate the deployment process for certain types of updates, reducing the manual time investment required. By self-managing portions of the update lifecycle, the system minimizes operational overhead while maintaining security improvements.
Data Source
AI summary
Techniques are described herein for reducing a risk exposure related to a software application. The techniques may comprise receiving an indication of a software application, identifying a number of components associated with that software application as well as a number of vulnerability scores corresponding to the number of components, determining, based on the number of vulnerability scores, a risk score associated with the software application, and identifying one or more updates associated with the number of components and determining a change to the risk score associated with those one or more updates. In embodiments, the techniques may further comprise receiving an indication of a threshold risk score and determining at least one of the one or more updates that, when applied to at least one of the number of components, is determined to lower the risk score to below the threshold risk score.