Uploaded June 2026 | Updated September 2026, 3 weeks ago
Panel: The AI Architecture Debt—Refactoring Infrastructure for Sustainable Privacy
Moderator: Dylan Gilbert, IAPP; Panelists: Sri Pravallika Maddipati, Google; Nathalie Baracaldo, IBM Research; Gary Young, Google
As AI systems mature from experimental prototypes into long-lived production infrastructure, many organizations are discovering a new form of technical debt: AI architecture debt. This debt accumulates when privacy, governance, and data-minimization principles are "bolted onto" AI systems after deployment rather than "baked into" their foundations. The result is fragile compliance, opaque data provenance, and costly retrofits whenever regulations, models, or data flows change. This talk proposes a refactoring mindset for AI infrastructure and examines the AI architecture debt through a privacy engineering lens, focusing on how design decisions around data pipelines, model lifecycles, observability, and deployment patterns can either compound or reduce long-term privacy risk. We will explore practical strategies for re-architecting AI platforms to support sustainable privacy outcomes, including modular data boundaries, privacy-aware model interfaces, and infrastructure patterns that make privacy guarantees resilient to future change.
View the full PEPR '26 program at usenix.org/conference/pepr26/program
Panel: The AI Architecture Debt—Refactoring Infrastructure for Sustainable Privacy
Moderator: Dylan Gilbert, IAPP; Panelists: Sri Pravallika Maddipati, Google; Nathalie Baracaldo, IBM Research; Gary Young, Google
As AI systems mature from experimental prototypes into long-lived production infrastructure, many organizations are discovering a new form of technical debt: AI architecture debt. This debt accumulates when privacy, governance, and data-minimization principles are "bolted onto" AI systems after deployment rather than "baked into" their foundations. The result is fragile compliance, opaque data provenance, and costly retrofits whenever regulations, models, or data flows change. This talk proposes a refactoring mindset for AI infrastructure and examines the AI architecture debt through a privacy engineering lens, focusing on how design decisions around data pipelines, model lifecycles, observability, and deployment patterns can either compound or reduce long-term privacy risk. We will explore practical strategies for re-architecting AI platforms to support sustainable privacy outcomes, including modular data boundaries, privacy-aware model interfaces, and infrastructure patterns that make privacy guarantees resilient to future change.
View the full PEPR '26 program at usenix.org/conference/pepr26/program
