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The vision of an ecosystem of general and highly capable autonomous agents challenges accepted principles of secure and privacy-preserving system engineering. Inspired by the theory of Contextual Integrity, we argue that in order for agents to act appropriately, their behaviors should comply with societal norms and expectations. This manuscript explores contextual approaches to engineering agentic systems that embody an understanding of societal norms and uphold the appropriateness of actions by design. It presents a list of key open research problems in this area to create awareness among the broader research community across academia, government, and industry.View details
Google, , 1600 Amphitheatre Parkway, Mountain View, CA, 94043 (2025)
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AI products introduce new privacy challenges. Finding the right privacy solution is central to developing innovative products, especially as AI models increasingly handle user data. In this paper, we propose a framework to reason about privacy in AI, and discuss how Privacy Enhancing Technologies (PETs) enable novel user experiences by reducing privacy risks in the AI development lifecycle. We argue that privacy protections are not inherently at odds with utility; in contrast, we discuss how building privacy into products from the start can create better, more trustworthy experiences for everyone.View details