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.

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.

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.

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.

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.

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.

The advancement of artificial intelligence (AI), including machine learning (ML) and increasingly autonomous systems, has resulted in a push for technical standards that can assess the trustworthiness of these technologies. Developing standards have the potential to drive the design and development of trustworthy systems, and to more cost-effectively evaluate AI-enabled systems during technology acquisition or regulation.

Properly managed contractor/supplier management systems include a robust performance monitoring and improvement mechanism. The Department of Homeland Security (DHS) is beginning to invest artificial intelligence/machine learning (AIML) technology into the Contractor Performance Assessment Reporting System (CPARS).

The challenge of securing the safe, orderly, and humane processing of migrants who arrive at our Southwest border is renderedincreasingly complicated due to the growing volume and substantial variations of this influx of migrants.