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BigID: Privacy by Design for AI: Automation & Governance

BigID
07/14/2026
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which is going to be how we manage and bake in privacy by design into AI, it is ensuring that all of the controls and the structures and the policies that we put in place the first time around, when we were standing up our operational privacy programs, that they're now bolstered with language that enables them to be strong enough to withstand the scope creep potential for AI. We can no longer lean on manual processes. They have to be automated. They have to be standardized. We have to rely cross-functionally on, and this is different for every company. And I just wanna be clear, I'm speaking as myself, I'm not a representative of the Boeing company, but we have to make sure that those, that we're working with our data team. So if we don't have the data discovery tools or these data maps or whatever it may be, we need to be working with the people who do own them because the time for the spreadsheet that we all remember when we started this game, that ship has sailed. We have to be using cross-functional assessments. And so leaning heavily into technology controls, this isn't a gate process, it's an infrastructure. And so how do we do that? We do it through technology.

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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