Garbage In, Liability Out: Why Ungoverned Data Is Your Biggest AI Risk

Every company racing to deploy artificial intelligence is quietly feeding it decades of ungoverned data: redundant, obsolete, misclassified and in many cases legally required to have been deleted years ago. The result is AI that confidently surfaces the PII you promised to minimize, the record you were obligated to dispose of and the sensitive content no one knew was there.

This session reframes AI governance as a data governance problem. Using real failure patterns from RAG pipelines, copilots and shadow AI, we will show why controls applied at the model layer cannot fix risk that lives in the data layer, and why "AI-ready" is really a statement about your data estate.

Attendees will leave with a practical way to assess whether their data is AI-ready, a map of where ungoverned data turns into AI risk and liability, and a sequence of steps (discovery, classification, minimization, defensible disposal) to close the gap so their AI is accurate, compliant and defensible.

What you will learn:

• Identify the specific points where ungoverned data creates AI risk, from training and RAG pipelines to shadow AI and copilots.
• Assess whether a data estate is "AI-ready" using data quality, classification accuracy and retention/minimization posture as the criteria.
• Apply a practical sequence — discovery, classification, minimization, defensible disposal — to reduce AI risk at the data layer.

Sponsored by RecordPoint

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