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Strengthening AI Governance with Prove AI

Matt Coolidge

SVP of Global Communications

Tamper-resistant, transparent, and traceable, blockchain will set a new standard of AI governance

AI’s ultimate efficacy integrity rests upon data integrity— after all, AI is only as trustworthy as the data it is trained on. This reality has been increasingly recognized by the organizations building, using and regulating AI – and it’s why governance has emerged as such a critical component of the broader AI ecosystem. This theme was front and center at IBM THINK last week – and Casper Labs played a key role within it, announcing our Prove AI governance solution during the event’s Wednesday keynote. 

With each passing week,  enterprises are developing and advancing their AI governance strategies, all while  new regulations proliferate. Joining IBM SVP of Products, Dinesh Nirmal, on stage, Casper Labs CEO Mrinal Manohar emphasized the indispensable role of AI governance in the technological landscape, underlining the inseparable link between AI and data security. Without robust data security measures, the proliferation of AI could result in a corresponding surge in risks. 

Nodding to the imperative need for AI trust, Manohar introduced Prove AI, an AI governance solution from Casper Labs that enhances transparency, access and auditability for managing AI training data. Prove AI will offer the most advanced solution for maintaining and sharing audit logs of AI data in a secure and tamper-proof manner. Additionally, it introduces features such as version control, multi-party access controls, and advanced auditing functionality for AI training datasets.

Developed with support from IBM Consulting, Prove AI will augment IBM watsonx.governance’s functionality—and become a critical component of AI governance stacks. This essential layer of data security is critical to AI innovation and responsibility, and enables watsonx users to create ironclad trust up and down the value chain. 

Powered by the Casper blockchain, Prove AI introduces three especially critical features for AI governance:

1. Transparency

The distributed ledger of blockchain requires it to be maintained by a highly decentralized network of individuals, independently validating each transaction and storing it in a publicly accessible database. This inbuilt system of checks and balances creates inherent transparency without compromising data: a critical foundation for responsible data usage. Because the output of LLMs and machine learning tools depends on the quality of their training data, AI governance begins with data security. A permanent and transparent record of transactions provides granular visibility into any changes, allowing organizations to see how such changes may affect AI operations overall. 

2. Tamper-resistance

The inherent visibility created by the distribution of blockchain makes it prohibitively difficult—if not impossible—for individuals and small groups to tamper with datasets. This traceability provides a “digital fingerprint” system that organizations using data can offer partners as a concrete assurance of data integrity, allowing them to verify training data quality and factuality.

3. High serialization

As a highly serialized format of storage, blockchain is optimal for logging enormous quantities of transactional data. This serialization occurs automatically, sans manual controls, resulting in enormous time and resource savings at scale. 

 

Hear first hand from Manohar about Prove AI at IBM Think 2024.

New technology for new challenges

In an environment characterized by stringent regulations and frequent legal changes, enterprises leveraging AI must prioritize risk reduction. Given that AI’s efficiency relies on the quality of its training data, minimizing risks associated with such data is fundamental to effective AI governance. As the amount of data continues to grow exponentially alongside technological advancements,  current data security tactics won’t suffice. To address these evolving challenges, a forward-thinking approach is essential, necessitating the development of innovative security tools that harness emerging technologies. 

Prove AI harnesses blockchain technology to benefit AI users working with massive datasets. For instance, the highly serialized and tamper-resistant nature of Casper blockchain allows organizations to efficiently store comprehensive and fully auditable logs of AI training data, with a clear record of what was accessed when, and by whom. That means organizations can conduct and facilitate audits of large datasets in a secure and cost-efficient manner, seamlessly porting all or some data between public and private infrastructures. 

Prove AI can also harness the power of blockchain to create uniquely powerful controls. Using the Casper protocol, Prove AI enables users to securely grant and revoke access to entire or partial datasets. This feature is essential for audits and customer trust, while the clear record of changes created by blockchain ensures a safeguard against bad actors. By allowing companies to perform versioning on AI training data, including reverting to prior versions if faulty outputs arise, Prove AI will make it much easier to train models, too. 

The potential of AI is most promising if we find efficient ways to make it safe. New governance tools won’t just move enterprise AI innovation forward, they’ll set the foundation for a new era of value delivery. 

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