What happens when the need for rapid AI innovation runs up against the growing pressure for trust, accountability, and compliance? In this episode of Tech Talks Daily, I sit down with Mrinal Manohar, CEO of Prove AI, to explore how risk management can accelerate rather than hinder AI deployment.
Mrinal shares how Prove AI is helping organizations build trust into their AI systems from the start. At a time when businesses are moving AI models into production, yet often lack visibility or safeguards, Prove AI offers a solution grounded in transparency and automation. Their approach uses distributed ledger technology to create tamper-proof audit trails for AI models. This allows teams to focus on innovation while having the infrastructure in place to meet evolving standards and regulatory demands.
We discuss why traditional monitoring techniques fall short in an AI context, especially as models become more complex and decisions happen in real time. Prove AI's infrastructure is designed to support continuous risk mitigation. By recording every event and decision with cryptographic certainty, they make it possible to prove safety, compliance, and responsible use without relying on labor-intensive manual audits.
Mrinal also explains how Prove AI's upcoming GRC product aligns with ISO 42001 and helps companies stay ahead of regulatory expectations. Whether you're deploying AI in customer service, manufacturing, or high-risk environments, the platform ensures clear oversight without disrupting speed or agility.
This conversation covers practical examples of AI risk in action, from automated railway inspections to drive-through ordering systems. We also explore how distributed ledger technology is helping redefine AI governance, offering companies a way to move fast with confidence.
If you're scaling AI and wrestling with risk, compliance, or trust, this episode will give you a fresh perspective on how to build guardrails that support growth—not slow it down.
