In this moment, excerpted from his discussion, CJ Combs, Senior Artificial Intelligence Business Consultant, Columbus Global, explained that addressing enterprise data risk requires a cross-functional governance framework that exposes hidden data practices, creates accountability, and establishes processes for remediation.
Key Takeaways
Data Governance Must Be an Enterprise Discipline: Effective governance cannot belong solely to IT, leadership, or the business. Combs emphasized bringing teams across the organization together to examine data practices, ask difficult questions, and collectively determine how problems should be addressed.
AI Raises the Stakes for Hidden Data Risk: Organizations may have data exposures they do not even know exist, creating additional risk as AI gains greater access to enterprise information. Combs pointed to developers creating multiple copies of databases as an example of how unmanaged data can proliferate without broader organizational visibility.
Governance Turns Discovery Into Remediation: A governance framework creates a structured way to uncover these issues and decide what happens next — from cleaning up unnecessary database copies to establishing new processes, adopting additional frameworks, and bringing in outside expertise where needed.
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