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AI Output Tracking, Governance, and Traceability: A Guide

by Lisa Park - Tech Editor

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.

Continually re-creating data sets thru AI causes inconsistencies⁤ in the data, because​ each pull might be different. Constantly remaking large synthetic data sets — only to have them‌ disappear — is like building‌ and melting icebergs. AI-driven insights are incredibly ‌helpful and convenient ⁤for ⁣business teams to‌ leverage,but the⁤ process doesn’t need ⁢to be reckless and⁣ wasteful.

AI output governance

Before insights⁣ are generated, ‌enterprises need ​best practices‌ in place to govern how AI data is used. This includes foundational steps such ‌as tagging, tracing, storing and ⁢establishing accountability around AI data. Other key tactics include:

Related:The great digital buy-in: What retail CIOs hope ⁢AI‍ transformation‌ delivers in ‌2026

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    As artificial intelligence becomes more integrated into daily ‌workflows, ‌establishing clear governance protocols is crucial. here are key considerations for businesses ⁤navigating this new landscape:

    • Establish a​ single source of truth. Teams need to work together from a single source of truth.

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