Why Proactive Governance Is A 'Continuous Pipeline' for Enterprise AI

AI is compelling financial institutions to rethink data governance. For decades, the rules were clear, built on pillars like data lineage, cataloging, and quality. But the introduction of AI is challenging that established playbook. Now, it's even raising new concerns about model reliability and ethical outcomes.

The transformation is one that Ashish Dibouliya (PhD), a Senior Enterprise Data Architect and Managing Director at Webster Bank, knows well. With over 19 years of experience in enterprise data warehousing for the financial services industry, Dr. Dibouliya has spent most of his career leading large-scale data projects at companies like GE Capital and ADP. Today, he's a certified Six Sigma Black Belt with domain expertise in consumer and commercial banking, risk management, and federal reporting. According to Dibouliya, the industry is already in the midst of rewriting its own governance rules.

"Financial institutions are redefining governance, looking beyond those four pillars and bringing the ethical and accuracy parts into the mix," Dibouliya said. "In the past, the accuracy of our predefined tools was well-accounted for. Now, that accuracy is becoming questionable." But the solution isn’t faster governance, he continued. Instead, it's building a disciplined, proactive quality assurance process that happens long before any AI model touches a live system.

• Speed through safety: With the help of a dedicated governance team, the approach vets every model to verify its reliability from the start. "The most important part is adopting the right and reliable AI models," Dibouliya said. "Once we ensure the reliability of these models, it does not really impact the speed. Once that part has been established, it's more or less a matter of tuning or grounding the model."