AI & Machine LearningResearch & Ethics

Agentic Data Flows: Privacy, Data Minimization, and PETs in Agentic Deployment

A Virtual Roundtable Series October 19, 2026 @ 9:30am - 11:00am ET
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Agenda
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Overview

What are the governance challenges when AI agents share data, memory, and inferences with each other? We’ll explore that question in Agentic Data Flows: Privacy, Data Minimization, and PETs in Agentic Deployment, the first session of a new series on agentic data minimization from FPF’s Center for AI on Monday, October 19, 2026, from 9:30 to 11:00 am ET.

As AI agents move from pilots into real deployments, they cross system boundaries, carry what they learn from one session to the next, and hand data and inferences off to other agents. For privacy teams, regulators, and policymakers, that raises a pressing question: what happens to personal information between agents, and how do we keep it in bounds?

This series is part of FPF’s Research Coordination Network on Privacy-Enhancing Technologies, supported by the National Science Foundation and the U.S. Department of Energy, and is presented in collaboration with Working Paper.

In this virtual roundtable, we’ll cover:

  • Agents, privacy risk, and PETs. A level-set on what makes an agent different from earlier AI systems, and what current research and real-world deployments tell us about new privacy risks. With Andrew Gruen and Bennett Hillenbrand, Working Paper.
  • A map of agentic privacy risks. A walkthrough of 24 risks across five areas: data ingestion and processing; data aggregation, use, and sharing; inconsistent privacy practices across agents; the failure of legacy consent systems; and accountability and governance.
  • Deep dive: state sharing, cascading inferences, and boundary collapse. A presentation and fireside chat on inference contagion, memory persistence, and failures in deletion and propagation.

This discussion will be recorded, and the recording and a summary report will be published after the event.

Who should attend: Privacy and data protection professionals, AI governance and product teams, regulators and policymakers, researchers, and anyone working on responsible deployment of AI agents. No technical experience required.

Click here to see the full program agenda and speaker announcements.

This project is part of the Research Coordination Network (RCN) for Privacy-Preserving Data Sharing and Analytics, which is supported by the U.S. National Science Foundation (Award #2413978) and the Department of Energy (Award #DE-SC0024884).

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Agenda

Monday, October 19, 2026

Time

Event

Speakers

9:30 am –
9:50 am ET

Welcome and Opening Remarks
Agents, Privacy Risk, and PETs
  • Introducing the series. This is the first of four sessions on where PETs and AI meet. In the later sessions we will get into the technical primitives like confidential compute, secure enclaves, and cryptographic privacy protections.
  • Agents vs what came before. A quick level-set on what separates an agent from ordinary software. We will describe how it crosses system boundaries, it carries learning across sessions, and it hands data off to other agents.
  • Introducing the landscape. Before we get into standards or tools, we introduce a tour of what current research and actual deployments tell us about novel agentic privacy risk.
  • Bennett Hillenbrand, Adjunct Professor, Data Science and Public Policy, Georgetown University, McCourt School of Public Policy

9:50 am –
10:05 am ET

The Taxonomy

There are 24 agentic specific risks identified in the taxonomy, grouped into five parts:
  1. Data ingestion and processing
  2. Data aggregation, use, and sharing
  3. Inconsistent privacy practices across agents
  4. Failure of legacy consent systems
  5. Accountability and governance
Parts 1 and 2 are well understood risks, but have a new agentic spin. We’ll spend extra time on part 3 because that’s where privacy teams can actively develop learnings on agentic deployments, where some of the biggest novel risks are, and where they can make progress right now. This is the most immediate issue we’re seeing today. Parts 4 and 5 will come later, as they involve interactions between, and understanding of, parts 1-3.
  • Andrew Gruen, Senior Fellow, Future of Privacy Forum
  • Bennett Hillenbrand, Adjunct Professor, Data Science and Public Policy, Georgetown University, McCourt School of Public Policy

10:05 am –
10:30 am ET

A Deep Dive on State Sharing, Cascading Inferences, and Boundary Collapse

We’ll start from the chain-of-inference setup in part 3 and how it can lead to arbitrarily aggressive information sharing between agents (when it shouldn’t) and boundary collapse. We will go through a few use cases of multi-agent state sharing.

Part A: Technical mechanisms and agentic privacy 

  • Inference contagion. What happens if Agent A makes an uncertified, probabilistic inference (financial stress, an illness, etc.), passes it downstream, and Agent B treats it as a hard constraint?
  • Memory persistence. What happens if a persistent agent memory caches intermediate reasoning outside file-level permission domains, getting around the access controls that traditional databases rely on?
  • Deletion and propagation failures. What happens when a user revokes consent or asks for deletion, but downstream agents still hold cached memory or or derived inferences?
Part B: Privacy and enterprise realities 
  • Applying theory to enterprise risk. Connecting the risk model to real deployments in regulated workflows.
  • Auditability vs. minimization. How to capture audit trails and inference lineage across multi-agent loops without building a second, invasive surveillance log.
  • Vendor questions. Non-promotional. The technical questions privacy teams should be asking vendors about agent memory, tool-chain edge cases, and inference propagation to make sure their procurement efforts are privacy-aware.
  • Bennett Hillenbrand, Adjunct Professor, Data Science and Public Policy, Georgetown University, McCourt School of Public Policy
  • Antigoni Polychroniadou,
    Executive Director, J.P. Morgan AI Research; AlgoCRYPT Center of Excellence

10:30 am –
11:00 am ET

Moderated Discussion and Q&A

  • Discussion (15 min) Negotiating privacy limits between autonomous agents vs. setting policy at the protocol layer, and how to ask third-party vendors to include boundaries and inference controls.
  • Audience Q&A (10 min)
  • Wrap-up and next steps (5 min) What’s coming in the next PETs and AI sessions (including confidential compute) and how to get the working draft of the taxonomy.

 

Stacey Gray, Senior Director for Artificial Intelligence, Future of Privacy Forum

Speakers

Jaspreet Bhatia

Staff Privacy Engineer, Google

Jaspreet helps teams across Alphabet build AI responsibly by bringing privacy into every stage of AI development. Jaspreet’s work combines expert guidance, research into emerging AI Privacy risks, and automated, scalable AI governance, enabling teams to innovate quickly while privacy stays the default.

Stacey Gray

Senior Director for Artificial Intelligence , FPF

Stacey Gray is the Senior Director for Future of Privacy Forum’s Center for Artificial Intelligence and supports FPF’s U.S. engagement for consumer privacy research, analysis, and policymaker education. At FPF, she has spent many years focusing on the privacy implications of data collection in online and mobile advertising, platform regulation, cross-device tracking, Smart Homes, and the Internet of Things, including publishing extensive work and providing Congressional testimony on the intersection of emerging technologies and federal privacy regulation and enforcement. Stacey graduated from the University of Florida in 2010, and cum laude from Georgetown University Law Center in 2015, during which she worked in privacy-related civil rights litigation as a law clerk for Victor M. Glasberg & Associates, and as a member of the civil rights division of the Institute for Public Representation.

Selected Publications and Events:

Senate Testimony (April 9, 2020), Senate Committee on Commerce, Science, and Transportation, “Enlisting Big Data in the Fight Against Coronavirus” Infographic (May 22, 2020), “Understanding the World of Location Data“ FPF Blog (Mar. 25, 2020), “A Closer Look at Location Data: Privacy and Pandemics“ Public Comments (Mar. 11, 2020) to the California Attorney General on the California Consumer Privacy Act (CCPA) FPF Blog (Feb. 25, 2020), “Close to the Finish Line: Observations on the Washington Privacy Act“ Guest Expert (November 27, 2018, Brussels), “Digital Data Flows Masterclass: Location Data: GPS, Wi-Fi, and Spatial Analytics“ IAPP Privacy Perspectives (Dec. 1, 2016) “How industry can protect privacy in the age of connected toys“ White Paper (April 28, 2016) “Always On: Privacy Implications of Microphone-Enabled Devices” White Paper (Oct. 27, 2015), “Cross-Device: Understanding the State of State Management”
Andrew Gruen

Senior Fellow, FPF

Andrew Gruen is a consultant and technology executive, supporting pro-social tech development. He is the principal consultant at Working Paper, where he helps clients develop evidence-based solutions to policy and governance issues. Andrew is also a Senior Fellow at the Future of Privacy Forum where he facilitates the National Science Foundation funded Research Coordination Network on Privacy Enhancing Technologies, and a Fellow at George Washington University’s Institute for Data, Democracy and Politics, where he focuses on the implementation of Europe’s new Digital Services Act.

Andrew previously served in multiple product and policy roles at Meta Platforms, including the head of joint planning for research and policy in the Policy Planning org, and head of governance for the company’s US 2020 Election Research Project (which published in Science and Nature). In addition he started the Academic Partnerships team, and was Meta’s representative to the European Digital Media Observatory‘s data sharing working groups. As Chief Communications Officer at Seven Bridges, a firm that develops tools to analyze genomic sequencing data at population scale, he led the company’s marketing, business development, and partnerships, including the company’s participation in the White House’s Cancer Moonshot program, with a particular focus on more creating more accurate, less invasive, genomics-based cancer diagnostics. As a Luce Scholar, Andrew worked in South Korea for the world’s largest citizen journalism organization, OhmyNews.

Learn more about Andrew here.

Bennett Hillenbrand

Adjunct Professor, Data Science and Public Policy, Georgetown University, McCourt School of Public Policy

Bennett is a product executive with a background in data science, policy development and implementation, and people management. He has led product and program teams in the data and AI/ML space supporting political, commercial, and research use cases. He is an expert in helping organizations develop and execute strategies that support their mission.

Bennett is a co-founder and President at Working Paper, where he helps clients establish product and engineering teams, supports established product teams that are evolving into mature, process-driven organizations, drives strategy and execution for 0-1 programs and products, and provides strategic guidance to executives when their programs and products interact with complex regulatory environments or critical policy objectives.

Bennett was previously Chief Product Officer at Yobi, where he worked with Fortune 500 companies to privately and safely monetize their data by leveraging privacy enhancing technologies and modern AI systems.

He also held multiple strategic roles at Meta, including as the Data and Tooling Lead for the Open Research and Transparency Team and as the Privacy Policy Lead for Research Data Sharing. There he launched the Meta Research Platform, product managed the privacy infrastructure and experimental interventions for the largest consented political science study ever conducted, and did policy and product work to launch other highly impactful datasets.

Prior to his work at Meta, Bennett held the roles of Director of Analytics Products and the Director of Data Science at Catalist, where he led the team in launching the “what happened” series, one of the most accurate and comprehensive assessments of population-level electoral behavior in the US.

In his early career, Bennett worked on hardware, firmware, and systems for signal processing applications on embedded systems.

He teaches a course on using product management skills to improve policy development, delivery, and iteration at the Georgetown McCourt School of Public Policy, near where he lives in Washington DC, with his wife, Cori, and their two children, Harper and Felix.

Antigoni Polychroniadou

Executive Director, J.P. Morgan AI Research; AlgoCRYPT Center of Excellence

Antigoni Polychroniadou heads the AlgoCRYPT Center of Excellence and is an Research Director at J.P. Morgan AI Research. She builds cryptographic tools and privacy-preserving algorithms, for time-series data and more, that change how financial institutions can use sensitive data. She earned her Ph.D. at Aarhus University and was a postdoctoral researcher at Cornell University. Her many honors include a Simons Fellowship and recognition as a Prolific J.P. Morgan Inventor.