Prove AI CTO Greg Whalen spoke at the NY AI Summit, one of the world’s largest annual gatherings of AI professionals, earlier this week. His presentation highlighted a new paradigm that has emerged alongside AI: AI development does not follow the same rules as traditional software development. Unlike previous technologies, the data, events and software generated (and consumed) by AI are intertwined and operate at a significantly larger and more dynamic scale.
This shift is particularly relevant as organizations increasingly prioritize clear and comprehensive AI governance strategies. For governance to be truly effective, organizations need the ability to observe all inputs that train a given AI system. This represents a stark departure from existing norms. As AI standards gain traction – the most notable example being NIST’s ISO 42001 standard, which was officially achieved by AWS last month – it has become increasingly clear that there’s a direct correlation between AI-related risk levels and a lack of observability into AI systems’ underlying datasets and models.
Prove AI was purpose-built for this new AI development paradigm. Using distributed ledger technology as a highly secure and tamper-proof infrastructure layer, it enables organizations to capture all key AI data and automate the management.This ensures a fully observable and ISO 42001-compliant AI governance process.
1. Always Compliant and Observable Development
Greg emphasized the importance of maintaining compliance and observability throughout AI development, no matter the tools or teams involved. For instance, a company with 50 AI teams using diverse MLOps tools can implement a system that logs every action in real time, avoiding reliance on periodic reviews or single-tool mandates. This approach ensures compliance is seamless, transparent, and integrated into the development process without adding manual work or delays.
2. Industrial AI and Public Safety
Greg addressed the risks industries face when embedding AI into physical products like robotic devices or x-ray machines. Automating safety and maintenance reviews can enhance accountability while minimizing risks and manual effort. Transparent, tamper-proof records of incremental updates help manufacturers share responsibility with stakeholders and prevent liability issues or legal disputes.
3. Third-Party Compliance and Verifiability
For software vendors, traditional audits and certifications can be time-consuming and complex, especially when working with LLMs or third-party tools. Greg proposed a secure, real-time “show your work” system that eliminates the need for manual verifications, reducing friction and ensuring continuous compliance. This approach provides stakeholders with instant access to tamper-proof compliance records, fostering trust and streamlining the process.
While this is far from an exhaustive list of Prove AI’s applications, it does provide a powerful testament to the efficiencies driven by good AI governance.
If you’re beginning your AI governance journey, we’d love to hear from you and explore how we can help set you up for success. Get in touch with us here.