Network Asset Cryptography Prioritization for Quantum Risk Mitigation
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Solution Overview
Problem
Large-scale communications service providers face challenges in identifying and prioritizing network assets that are most at risk and crucial to protect against quantum computer attacks, as quantum computers can potentially breach existing cryptographic systems, compromising sensitive information and critical services.
Innovation Solution
A system utilizing machine learning models to analyze metadata of network assets, generating risk scores, and implementing remedial actions based on these scores to enhance the security of high-risk assets against quantum computer threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment of quantum risk for each network asset is performed, then assessment accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by collecting and organizing metadata about network assets beforehand, including cryptographic implementations, data sensitivity classifications, and asset criticality information. This preparatory work enables the machine learning model to quickly generate accurate risk scores without manual assessment at the time of quantum risk evaluation.
Solution Approach 2:
A machine learning model serves as an intermediary between raw asset metadata and quantum risk assessment results. The model processes metadata features and generates risk scores automatically, eliminating the need for manual expert assessment while maintaining high accuracy through learned patterns from training data.
2Measurement precision
If comprehensive metadata collection for all network assets is performed, then risk assessment accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from comprehensive asset metadata, such as cryptographic algorithm types, key lengths, data classification levels, and asset criticality markers. By selecting and extracting only the essential features needed for quantum risk assessment, the system maintains high accuracy while reducing data processing complexity and storage requirements.
3Reliability
If remedial actions are initiated for all assets meeting the threshold, then security coverage is improved, but resource allocation efficiency decreases
Solution Approach 1:
The system applies local quality by differentiating remedial actions based on individual asset characteristics and risk profiles. Instead of uniform treatment, assets receiving the same remedial action share similar cryptographic vulnerabilities and business criticality levels, optimizing resource allocation while maintaining comprehensive security coverage.
Data Source
AI summary
A method includes identifying a plurality of assets of an enterprise for which quantum risk is to be assessed, acquiring a plurality of sets of metadata, where each set of metadata describes one asset of the plurality of assets, executing a machine learning model that takes the plurality of sets of metadata as input and generates as output a plurality of scores, where each score of the plurality of scores quantifies a quantum risk associated with one set of metadata of the plurality of sets of metadata that corresponds to one asset of the plurality of assets, and initiating a remedial action for a first asset of the plurality of assets, based on a first score of the plurality of scores that is assigned to a first set of metadata of the plurality of sets of metadata corresponding to the first asset at least meeting a predefined threshold.