About

Dr. Annie I. Antón is a Professor in the School of Interactive Computing at the Georgia Institute of Technology in Atlanta, where she has also served as chair. Her research focuses on the specification of complete, correct behavior of software systems that must comply with federal privacy and security regulations. She has served the national defense and intelligence communities in various roles since her selection for the IDA/DARPA Defense Science Study Group in 2005-2006. In 2016, she was appointed by President Barack Obama to the 12-person bi-partisan Commission on Enhancing Cybersecurity for the Nation. Dr. Antón currently serves on several boards, including the NIST Information Security & Privacy Advisory Board and the Future of Privacy Forum Advisory Board. She has been involved with numerous other advisory committees and boards related to privacy, security, and computing policy.

Research topics

• Computer Security
• Computer Science
• Artificial Intelligence
• Human–computer interaction
• Software engineering
• Telecommunications
• Engineering
• Physics

Selected publications

• Know Your Scientist: KYC as Biosecurity Infrastructure

  ArXiv.org · 2026-02-05

  articleOpen accessSenior author

  Biological AI tools for protein design and structure prediction are advancing rapidly, creating dual-use risks that existing safeguards cannot adequately address. Current model-level restrictions, including keyword filtering, output screening, and content-based access denials, are fundamentally ill-suited to biology, where reliable function prediction remains beyond reach and novel threats evade detection by design. We propose a three-tier Know Your Customer (KYC) framework, inspired by anti-money laundering (AML) practices in the financial sector, that shifts governance from content inspection to user verification and monitoring. Tier I leverages research institutions as trust anchors to vouch for affiliated researchers and assume responsibility for vetting. Tier II applies output screening through sequence homology searches and functional annotation. Tier III monitors behavioral patterns to detect anomalies inconsistent with declared research purposes. This layered approach preserves access for legitimate researchers while raising the cost of misuse through institutional accountability and traceability. The framework can be implemented immediately using existing institutional infrastructure, requiring no new legislation or regulatory mandates.

  PublisherOA PDF

• Know Your Scientist: KYC as Biosecurity Infrastructure

  Open MIND · 2026-02-05

  preprintSenior author

  Biological AI tools for protein design and structure prediction are advancing rapidly, creating dual-use risks that existing safeguards cannot adequately address. Current model-level restrictions, including keyword filtering, output screening, and content-based access denials, are fundamentally ill-suited to biology, where reliable function prediction remains beyond reach and novel threats evade detection by design. We propose a three-tier Know Your Customer (KYC) framework, inspired by anti-money laundering (AML) practices in the financial sector, that shifts governance from content inspection to user verification and monitoring. Tier I leverages research institutions as trust anchors to vouch for affiliated researchers and assume responsibility for vetting. Tier II applies output screening through sequence homology searches and functional annotation. Tier III monitors behavioral patterns to detect anomalies inconsistent with declared research purposes. This layered approach preserves access for legitimate researchers while raising the cost of misuse through institutional accountability and traceability. The framework can be implemented immediately using existing institutional infrastructure, requiring no new legislation or regulatory mandates.

  DOI

• Know your scientist: KYC as biosecurity infrastructure

  Frontiers in Microbiology · 2026-04-29

  articleOpen accessSenior author

  Biological AI tools for protein design and structure prediction are advancing rapidly, creating dual-use risks that existing safeguards cannot adequately address. Current model-level restrictions, including keyword filtering, output screening, and content-based access denials, are fundamentally ill-suited to biology, where reliable function prediction remains beyond reach and novel threats evade detection by design. Because the full spectrum of risks cannot be managed by any single actor, effective oversight requires shared responsibility between research institutions and model hosts. Hence, we propose a three-tier Know Your Customer (KYC) framework, inspired by anti-money laundering (AML) practices in the financial sector, that augments existing approaches, supplementing content inspection with complementary layers of user verification and monitoring. Tier I leverages research institutions as trust anchors to vouch for affiliated researchers and assume responsibility for vetting. Tier II applies output screening through sequence homology searches and functional annotation. Tier III monitors behavioral patterns to detect anomalies inconsistent with declared research purposes. This layered approach preserves access for legitimate researchers while raising the cost of misuse through institutional accountability and traceability. The framework can be implemented immediately using existing institutional infrastructure, requiring no new legislation or regulatory mandates.

  PublisherDOI

• Educating the Next Generation of Ethical AI Practitioners

  Journal of The Colloquium for Information Systems Security Education · 2025-04-19

  articleOpen accessSenior author

  Artificial intelligence (AI) technologies are rapidly advancing, increasing concerns about data privacy harms in AI models. We discuss how ethical AI can be incorporated into computer science curricula. This paper describes the design process for the first ‘AI Privacy Engineering’ course, to the best of our knowledge, taught in the United States. The course is designed for both undergraduate and graduate students at Georgia Tech. Throughout this course, students examine ethical implications of AI system design, development, deployment, and utilization, using the ACM’s General Ethical Principles as an ethical framework. Recognizing that data privacy represents only one possible form of harm, the course blends theoretical and conceptual lectures with hands-on projects that require students to address ethical issues, including bias, fairness, and accountability in AI systems. Herein, we discuss the course design process, including selecting the appropriate body of knowledge, establishing learning objectives, creating assignments, and considering pedagogical methodologies we employed. We explain the empirical methods used to inform our design, including a systematic review of courses teaching AI development at over 40 universities. Our structured curriculum can be used to effectively teach ethical and safe AI, and we propose how these topics may be incorporated more broadly into computer science courses. Finally, we discuss early successes and the challenges faced while teaching the course, particularly in maintaining relevance despite fast-paced changes in the field of AI, an evolving legislative landscape, accessing computational systems to run AI models, and varying levels of student preparedness.

  PublisherOA PDFDOI

• Computer Security. ESORICS 2023 International Workshops

  Lecture notes in computer science · 2024 · 1 citations

  • Computer Science
  • Computer Science
  • Computer Security
  PublisherDOI

• Enhancing Privacy in Robotics via Judicious Sensor Selection

  2020 · 17 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Roboticists are grappling with how to address privacy in robot design at a time when regulatory frameworks around the world increasingly require systems to be engineered to preserve and protect privacy. This paper surveys the top robotics journals and conferences over the past four decades to identify contributions with respect to privacy in robot design. Our survey revealed that less than half of one percent of the ~89,120 papers in our study even mention the word privacy. Herein, we propose privacy preserving approaches for roboticists to employ in robot design, including, assessing a robot's purpose and environment; ensuring privacy by design by selecting sensors that do not collect information that is not essential to the core objectives of that robot; embracing both privacy and performance as fundamental design challenges to be addressed early in the robot lifecycle; and performing privacy impact assessments.

  PublisherDOI

• Computer Security

  Lecture notes in computer science · 2020 · 2 citations

  • Computer Security
  • Computer Science
  • Computer Security
  PublisherDOI

• Computer Security

  Lecture notes in computer science · 2019-01-01 · 10 citations

  book
  PublisherDOI

• A Prototype System for Transnational Information Sharing and Process Coordination

  Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) · 2018-01-01 · 6 citations

  articleOpen access

  Global problems such as disease detection and control, terrorism, immigration and border control, illicit drug trafficking, etc. require information sharing, coordination and collaboration among government agencies within a country and across national boundaries. This paper presents a prototype of a transnational information system which aims at achieving information sharing, process coordination and enforcement of policies, constraints, regulations, and security and privacy rules by integrating a distributed query processor with form-based and conversational user interfaces, a language translation system, an event server for event filtering and notification, and an event-trigger-rule server. The Web-services infrastructure is used to achieve the interoperation of these heterogeneous component systems.

  PublisherOA PDFDOI

• Discovering undocumented knowledge through visualization of agile software development activities

  Requirements Engineering · 2018-04-04 · 9 citations

  articleSenior author
  PublisherDOI

Frequent coauthors

• Julia B. Earp

  North Carolina State University

  24 shared

• Travis D. Breaux

  Carnegie Mellon University

  18 shared

• Aaron K. Massey

  Google (United States)

  16 shared

• Paul N. Otto

  Association for Computing Machinery

  14 shared

• Colin Potts

  Arizona State University

  10 shared

• Qingfeng He

  10 shared

• Thomas A. Alspaugh

  University of California, Irvine

  9 shared

• Laurie Williams

  8 shared

Education

• Ph.D., Computer Science

  University of California, Santa Barbara

  1991

• M.S., Computer Science

  University of California, Santa Barbara

  1987

• B.S., Computer Science

  University of California, Santa Barbara

  1985