June 4, 2025
274 words, 1 minute read time.
Everyone is trying to figure out how to apply AI to federal problems—essentially, building large language models and trying to wring value from them. Inevitably, many are jumping into creating LLMs to use with various data stores.
We are right at the point where consideration is given to managing enormous data sets in the federal government, emphasizing the need for operational efficiency and security.
Today, we will sit down with Dr. Ellison Anne Williams to explore the potential of privacy-enhancing technologies to enable secure and efficient data use across classification boundaries and data silos.
Dr. Ellison Anne Williams suggests a solution called Privacy Enhancing Technologies (PETs). It is applied to organizations who want to utilize data that sits in a silo, a data lake, or whatever nomenclature is used to describe large data sets these days.
PETs allow the secure and private use of data across boundaries and classifications. She explains how PETs enable AI and machine learning models to be trained and used without compromising sensitive data.
The conversation also touches on the cost savings from avoiding data replication and the potential for significant operational efficiencies.
Explore the potential of privacy-enhancing technologies to enable secure and efficient data use across classification boundaries and data silos.
Here are some episodes that are related:
EP 243: From Bottlenecks to Breakthroughs – Boosting Federal Efficiency with Automation
Covers similar themes of improving operational efficiency through technology, complements the LLM discussion with broader automation insights.
EP 244: Women in Federal Tech – Recognizing Leadership at the Women in Technology Awards
Adds a leadership and talent perspective—highlighting diversity in federal tech which supports long-term LLM adoption and governance.
