BigID: Privacy by Design for AI: Automation & Governance

Name: BigID: Privacy by Design for AI: Automation & Governance
Uploaded: 2026-07-14T09:55:19-04:00
Duration: 1 min 49 s
Description: Privacy by Design for AI at Scale | Automation, Data Governance & AI Risk: https://bit.ly/3Oax9ex In this session from BigID’s AI Governance Leadership Forum, Mae-Beth Magno (Global Privacy and AI Governance, Boeing) explains why traditional privacy pr...

BigID

07/14/2026

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TL;DR

Traditional privacy programs built on manual processes cannot scale to meet the demands of AI systems and must evolve to automated, standardized controls.
Privacy by design for AI requires strengthening existing policies with language that addresses scope creep potential and embedding controls as infrastructure rather than gate processes.
Cross-functional collaboration with data, security, and governance teams is essential, leveraging technology tools like data discovery and mapping instead of spreadsheet-based approaches.

Summary

Mae-Beth Magno from Boeing's Global Privacy and AI Governance team addresses the fundamental shift required in privacy programs as organizations scale AI systems. Traditional privacy frameworks built on manual processes and one-time assessments cannot withstand the scope and complexity of generative AI deployment. The session emphasizes that privacy by design for AI requires automated, standardized controls embedded as infrastructure rather than gate processes. Organizations must strengthen existing privacy policies with language that accounts for AI's unique risks while establishing cross-functional collaboration between data, security, and governance teams. The core message is clear: the era of spreadsheet-based privacy management has ended, and technology-driven controls are now essential for managing AI risk at scale. This excerpt from BigID's AI Governance Leadership Forum provides practical guidance for privacy professionals transitioning from legacy approaches to infrastructure-driven frameworks capable of supporting AI innovation without increasing organizational risk.

Chapters

0:00 - Privacy by Design for AI
0:38 - Automation and Standardization Requirements
0:51 - Cross-Functional Collaboration
1:35 - Technology-Driven Infrastructure

Key Quotes

0:38 "We can no longer lean on manual processes. They have to be automated. They have to be standardized."
1:23 "The time for the spreadsheet that we all remember when we started this game, that ship has sailed."
1:41 "This isn't a gate process, it's an infrastructure. And so how do we do that? We do it through technology."

FAQ

Why do traditional privacy programs fail with AI systems?

Traditional privacy programs rely on manual processes and one-time controls that cannot scale to the complexity and scope of AI systems. AI introduces scope creep potential that requires automated, standardized controls embedded as infrastructure rather than gate processes.

What role does cross-functional collaboration play in AI privacy governance?

Cross-functional collaboration between data, security, and governance teams is critical because privacy professionals must leverage technology tools like data discovery and mapping owned by other teams. No single function can manage AI privacy risk in isolation using legacy spreadsheet-based approaches.

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