Innovation comes in many areas and compliance professionals need to not only be ready for it but embrace it. Join Tom Fox, the Voice of Compliance as he visits with top innovative minds, thinkers and creators in the award-winning Innovation in Compliance podcast. In this episode, host Tom visits with Nikunj Bajaj, co-founder and CEO of TrueFoundry, about enterprise agentic AI infrastructure, governance, and hidden costs most organizations are not accounting for.
Bajaj describes TrueFoundry’s platform as a single control plane where enterprises can build, ship, and govern agentic AI applications, inspired by Meta’s internal ML stack, which he describes as about a decade ahead of the rest of the industry. He argues enterprises over-focus on model and tool selection when problem definition and effective use are the real constraints. On governance, he identifies two failure modes: avoiding meaningful use cases entirely to sidestep governance risk, or trying to solve all governance problems upfront and never reaching ROI. Successful teams implement application-specific controls iteratively, starting with a few high-value use cases rather than hundreds of low-value ones. He highlights that model inference accounts for only about 20% of total generative AI spend, with the majority concentrated in infrastructure, engineering, and debugging, creating cost allocation and budget control challenges for compliance teams. For auditability, he argues that an agent without full decision traces is “a liability with an API key,” and walks through how end-to-end tracing enables audit readiness, faster debugging, and proactive attack detection. He closes by advocating centralized control via a unified AI gateway while enabling federated development, and tailoring guardrails to whether your exposure surface is external or internal.
Key Highlights
- Stop Chasing Tools
- Governance vs Speed
- Hidden AI Costs
- Agent Auditability
- Board Level Priorities
Resources
Connect with Nikunj Bajaj
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