In the rush to build out AI applications, a full understanding of the dynamics of personal data management can be difficult to achieve. As we kick off Pride Month, the complexities of personal data handling deserve more attention and Emily Jasper and Alan Moore join host Eric Hanselman to discuss the concerns and approaches to address privacy issues. Enterprises accumulate both operational and self-reported personal data, some with regulatory requirements for collection and reporting and some in support of employee development. Is that data the new oil that can fuel their efforts, the new water that can leak or the new plutonium that can be powerful, but also dangerous?  With many systems accumulating data, it can be difficult to ensure that right data is in the right places. Data migration is hard, but can be necessary in technology transitions. Data is the raw material that builds AI value, but personal data increases the risks of not only expose, but of creating presumptions by AI models of association and affiliation. There are additional risks in inadequate datasets for training. As we've pointed out in previous episodes, organizations need to be aware of how well their training data reflects the populations they intend to serve. More S&P Global Content: Webinar: Winning the Ad Dollar: Data-Driven Sales Enablement Next in Tech | Ep. 213: AI and Privacy Next in Tech | Ep. 204: Ethical AI Data For S&P Global subscribers: Safeguarding privacy in the AI era – Highlights from VotE: Data & Analytics 2025 Trends in Workforce Productivity & Collaboration Ask the Analyst: Retail tech — personalization without friction 2025 Trends in Data, AI & Analytics Credits: Host/Author: Eric Hanselman Guests: Emily Jasper, Alan Moore Producer/Editor: Adam Kovalsky Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

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