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Strengthening Trust in Large Language Models

The emergence of large language models (LLMs) such as ChatGPT has revolutionized information retrieval and interaction, particularly in intelligence analysis. However, concerns about their trustworthiness in security-critical applications persist due to security risks, factuality issues and biases. The project proposes a multifaceted approach to evaluating and enhancing the trustworthiness of large language models (LLMs), addressing key concerns related to security, factuality and biases.

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Enhancing AI for Homeland Security

The Multisource AI Scorecard Table (MAST) for Evaluating Generative AI in Worker-Automation Team Tasks (MEGAWATT) project aims to leverage MAST, a tool rooted in Intelligence Community Directive (ICD) 203, to evaluate and enhance the performance of large language models (LLMs) like OpenAI’s ChatGPT.

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Unlocking Software Safety with CHIAUS

The CHIAUS project aims to revolutionize risk communication in software development and consumption by integrating human-centered interactions with Software Bill of Materials (SBOM) data. Led by Principal Investigator L Jean Camp and her team at Indiana University, the project addresses the pressing need for actionable, understandable risk communication in the software ecosystem.

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Fortifying Security Screenings by examining human-AI interaction

In the realm of safety science, a paradigm shift towards resilience engineering offers new insights into addressing the challenges of complex systems, particularly in high-stakes environments like security screening. This project, Resilience Engineering for Visual Screening in Security Settings (REVS), aims to advance the understanding of resilience engineering principles within the context of security screening tasks.

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COVID-19 impacts on health, behavior, resilience and trade

The future economic effects of COVID-19 are highly uncertain. Early predictions showed COVID-19 may plunge the U.S. into an economic recession. However, current conditions make it difficult to predict the length and severity of the pandemic and identify any long-term economic repercussions.

Assessing threat landscapes through red teaming

The risk of chemical warfare (CW) has increased in recent years. The Homeland Security Enterprise (HSE) has a need to identify state and non-state actors likely to pursue the development of CW as well determining the factors that identify the use of CW. These factors include the states overall strategic orientation to CW, the types of CW agents likely to be pursued and the other activities that would accompany CW development.

Overhead view of people going through a TSA checkpoint.

Improving airport checkpoint efficiency

The combination of current airport space constraints and limited Transportation Security Officers coupled with projected increases in air travelers and rising number of security threats will put more pressure on TSA staff and resources.

Protecting privacy with deep-learning tools

In the era of big data, the unprecedented speed and volume of data collection poses benefits as well as risks. The large amount of collected data contains critical information for daily homeland security operations as well as potential usage to enhance analytical capabilities for better decision-making.

Creating new methods to preserve data privacy

Data sharing within DHS agencies and across components is challenging because of both privacy and security concerns. Many agencies remove sensitive but critical information from reports or documents to preserve privacy while others refrain from sharing data that could be useful to homeland security efforts due to the challenges of meeting and maintaining privacy requirements.

Guarding sensitive data with secure computations

The use of personal information is essential for homeland security efforts, but it is also a balancing act securing this information and protecting individual privacy concerns that exist in the sharing and use of sensitive information such as surveillance images/videos, biometrics, and other individual-identifying data that is collected and generated from multiple sources across the Homeland Security Enterprise.