Jena Zangs, chief data and AI officer at the University of St. Thomas in St. Paul, spoke with TechTarget about the growing need for stronger governance as organizations adopt agentic AI. Zangs emphasized the importance of structured data architecture, including centralized data systems and metadata tagging, to ensure agents operate within defined domains.
From the article:
Enterprises are going all in on agentic AI, accelerating initiatives even as they outpace the controls required to govern them.
The gap between agentic AI ambition and readiness is growing wider as organizations move from experimentation to production. The key issue is no longer whether AI agents can automate work, but whether the necessary data governance, observability and identity foundations are in place to handle these autonomous systems at scale. ...
Siloed data stores and static data warehouse models won’t support secure, governable and resilient agents, said Pablo Ballarin, co-founder and virtual chief information security officer at cybersecurity services firm Balusian S.L. and a member of the Emerging Trends Working Group at ISACA, an association for governance professionals.
Ballarin said that’s why it’s essential that organizations move to dynamic, entity-centric and governed data fabric architectures.
That’s the strategy at the University of St. Thomas in St. Paul, Minn. Jena Zangs, the university’s chief data and AI officer, said it uses a centralized data lakehouse, data mesh architecture and metadata tagging to support agentic AI use.
“That gives us governability,” she said. “And it keeps data and business close to create a data product, so when we talk about agentic AI, we can keep agents to a specific domain and they don’t have to have access to the entire database.”
