09032026 - TechTalks Recap: Know Your Data and Trust Your AI 🤖
If you missed last week’s TechTalks, Ashish Gupta and I dug into a practical question many teams are working through right now: before you ask AI to make decisions with your data, do you really know what data you have, where it lives, and whether it can be trusted?
The short answer is that AI-ready data needs more than a model and a prompt. It needs context.
What you missed 👇
- 🔍 Start with visibility. Data is everywhere: business applications, file shares, email, SaaS platforms, cloud, and legacy systems. You cannot govern—or use—what you cannot see.
- 🏷️ Classify before you accelerate. Knowing which data is sensitive, stale, duplicated, or governed by a policy is foundational to making it safely available for AI use cases.
- 🧠Context makes AI more useful. A customer record, an invoice, an email, and a policy document can be related—but only when the data has the right semantic context. That is what helps AI produce relevant, explainable results.
- âś… Turn insight into action. The goal is not to lock down every dataset. It is to prioritize the right actions: reduce risk, meet governance requirements, and make the right data available for the right AI initiative.
One point that stood out for me: data intelligence is not just an AI conversation. It is a practical way to improve visibility, reduce exposure, and make better decisions across security, governance, and infrastructure teams.
Watch on demand 🎥
Want to see the examples and hear the full conversation? Watch “Data Intelligence Impact: Know Your Data and Trust Your AI” on demand.
The on-demand session is 58 minutes and covers how discovery, classification, context, and governance can help establish a trustworthy foundation for AI.
If you are starting an AI initiative, a good first step is simple: get a clearer picture of your data landscape. From there, you can decide what to protect, what to govern, and what is ready to put to work.
đź’¬ đź’¬ What is the first data challenge your team needs to solve before moving an AI use case forward? Let us know!
