BOM Management & Risk Reduction

Take Software Supply Chain Security to the Next Level: Blockchain + AI 

Cyber warfare is increasingly being used to exploit vulnerabilities within software supply chains. Challenges exist with the current methods of sharing and managing Software Bill of Materials (SBOMs) including potential data tampering, hesitation among vendors to disclose information, and bespoke requirements from software users. Plus, the use of Large Language and Fine-tuned modeling within software development will require a new approach to the SBOM processes. 

Our ground-breaking fusion of blockchain-secured data with generative AI modeling can revolutionize the management and analysis of SBOMs and AIBOMs to greatly reduce the risks inherent in today’s software solutions. 

Benefits of Blockchain: 

All transactions are recorded, including the creation, modification, and distribution of AI models and their components, guaranteeing that data once written cannot be altered. 

Authorized parties can trace the provenance and evolution of AI models and their associated SBOMs.  

Less reliance on central authorities reduces potential bottlenecks and points of failure. Trust is established through the consensus mechanisms inherent to Hyperledger Firefly, ensuring the integrity of every piece of data. 

Benefits of Smart Contracts: 

A range of templates designed for SBOM management can be customized to meet diverse industry standards and compliance requirements.  

Upon the upload of an SBOM, the smart contracts activate, validating the data against the latest compliance standards and regulatory frameworks. This automation significantly reduces the need for manual intervention, increasing efficiency and accuracy. 

Our smart contracts embed compliance within the blockchain ledger, ensuring that each compliance check is recorded and verifiable. This not only provides an immutable history of compliance but also ensures that any attempt to bypass regulatory requirements is detectable and traceable. 

Smart contracts act on predefined triggers, such as the release of a new software version or the update of an AI model. When triggered, they facilitate the automatic update of SBOMs and AIBOMs, ensuring all materials are current and accurate. 

The evolution of AI models and their components are managed with robust version control. Each update is timestamped and versioned, allowing a historical view of changes and the ability to rollback to previous states if necessary. 

Benefits of Large Language Models: 

LLMs can assist in document generation for software components and ML models. This includes descriptions, versioning information, and usage details. 

LLMs can help identify which libraries and packages are being used, aiding in the creation of a comprehensive SBOM. 

 LLMs can be trained to recognize patterns that might indicate security vulnerabilities in both software and ML models, which is an essential part of the SBOM for risk analysis. 

LLMs can assist in tracking dependencies of ML models, including datasets, preprocessing steps, and hyperparameters, similarly to how dependencies are tracked in software. 

An LLM can analyze the licenses of software components and ML models to ensure compliance with legal and company policies.

Using OWASP CycloneDX standard for SBOMs, our approach extends its foundational principles to effectively address the unique complexities of LLMs, enhancing the transparency and security of AI-driven software. 

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