Data Provenance: The Trust Layer For Agentic AI

InnovationData Provenance: The Trust Layer For Agentic AIByGaurav Aggarwal,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)Jun 10, 2026, 07:15am EDTGaurav Aggarwal, Senior Vice President at Onix, Global Head Presales & Solutions Engineering. getty​For the last two years, most AI conversations in the enterprise have started with the same question: What can the model do? It is a natural question. Generative AI helped teams write faster, search faster, summarize faster and automate pieces of work that once consumed hours.But as AI moves from generating responses to taking action, I believe the more important question is changing. It is no longer only "What can the model do?" It is now "What data made the system act?"That question brings us to data provenance. Data provenance is the story behind the data. Where did it come from? Who changed it? Which system touched it? Was it allowed to be used for this purpose? Which decision did it influence?In a reporting world, poor provenance may lead to a wrong dashboard. In an agentic AI world, poor provenance can lead to a wrong action. That is where the risk becomes far more serious.The New Risk Behind Autonomous AI Generative AI gives an answer. Agentic AI can take a step.An AI agent may retrieve information, call an API, interact with another agent, update a system or complete a workflow. In many enterprise use cases, multiple agents may work together: One gathers data, one interprets it and one acts.That chain is only as trustworthy as the data moving through it. If the first agent pulls stale, incomplete or unauthorized data, the issue does not remain at the first step. It travels into the reasoning layer. Then it travels into the action layer. By the time someone notices, the enterprise may not be dealing with one bad output. It may be dealing with a bad...