AI Output Tracking, Governance, and Traceability: A Guide
- Large synthetic data sets built with AI used to train models, wich are than pushed into production.
- Continually re-creating data sets thru AI causes inconsistencies in the data, because each pull might be different.
- Before insights are generated, enterprises need best practices in place to govern how AI data is used.
Unlike customary, legacy data sets, AI-generated content and insights tend to live inside a vacuum, created, used and taken for granted without proper governance. unfortunately, for companies that don’t provide proper oversight — and proactively govern AI data – they’re susceptible to unseen risks.In other words, ungoverned AI data can poison the well. It makes companies vulnerable to legal or compliance issues,intellectual property concerns,holes in data sourcing and accountability,and inconsistent data results.
At the same time, leaders of data management who understand the importance of governing AI-generated insights and data face the challenge to do so proactively, rather than continually working backward to fix or react to data issues.
Ungoverned AI: What can go wrong
Rather than simply taking AI-synthesized data at face value and pulling it out of a system,companies need to ensure that all synthetic data and generative AI (GenAI)-powered insights are tagged,tracked,traced,stored and properly governed.
Large synthetic data sets built with AI used to train models, wich are than pushed into production. Companies that don’t track who created that data, when and where might lose that foundational knowledge going forward, causing teams to re-create the data set repeatedly.