After reflecting on the discussions at the International Association of Privacy Professionals Data Intensive 2026, it is clear that artificial intelligence continues to dominate the data privacy sector, and trust in AI platforms remains the foremost concern. Across the sessions, three themes consistently emerged in both presentations and audience questions: trust, explainability, and transparency.

• Trust.  From the opening session, one question dominated the conference: How can AI providers ensure users can trust their platforms? In the opening session, J. Trevor Hughes, President and CEO of the International Association of Privacy Professionals, compared the current AI landscape to the early adoption of electricity. Public apprehension ran high—even at the White House, which employed an electrician solely to operate light switches throughout the residence. This story illustrates that the public will not touch (literally) what they do not trust.  Developing and operationalizing an AI governance program is essential to establishing trust in an AI platform and the organization that provides it. Such a program should include a public-facing privacy policy as well as internal IT and AI governance policies. One notable insight from the opening session was that trust and safety enable faster innovation.
• Explainability. At first glance, navigating AI, cybersecurity, and privacy regulation can seem daunting. However, after hearing TikTok’s Lead Counsel for Privacy Regulation and Governance and Google’s Director of Regulatory Affairs discuss how best to prepare for regulatory inquiries and investigations, I was reminded of the adage, “Keep it simple.”  When developing AI governance, there is no need to overcomplicate the associated procedures. As I often tell clients, AI policies and privacy policies should be indicative of actual practice and refrain from aspirational statements. As the regulatory landscape and potential customers continue to demand explainability, it is important to have a simple governance system in place that allows a company to explain how its model works and how it collects and uses data. Explainability may be one of the most effective sales tools for AI platforms.
• Transparency.  Transparency remains a foundational pillar of both data privacy and AI governance. Transparency is among the most effective ways for a company to build trust in its products and activities. Outgoing UK Information Commissioner John Edwards emphasized that there is always a person behind the data. Demonstrating how individuals’ data is collected, used, stored, and secured is the most effective way to earn their trust. Public-facing privacy policies are among the most accessible mechanisms for companies to build that trust. In the age of AI, data quality is the strongest determinant of output quality, and sustaining access to high-quality data requires that users trust their data is used responsibly.

The conversations that emerged from IAPP’s Intensive 26 highlighted that the future of AI and data privacy governance will be defined not by the sophistication of technology, but by the integrity of the frameworks we build around it. Trust, explainability, and transparency are the practical pillars upon which responsible AI governance must rest. If that sounds daunting, remember to always keep it simple!

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