Michael Mylrea

Recent advancements in artificial intelligence (AI) technology have raised concerns about the ethical, moral, and legal safeguards. There is a pressing need to improve metrics for assessing security and privacy of AI systems and to manage AI technology in a more ethical manner. To address these challenges, an AI Trust Framework and Maturity Model is proposed to enhance trust in the design and management of AI systems. Trust in AI involves an agreed-upon understanding between humans and machines about system performance. The framework utilizes an “entropy lens” to root the study in information theory and enhance transparency and trust in “black box” AI systems, which lack ethical guardrails. High entropy in AI systems can decrease human trust, particularly in uncertain and competitive environments. The research draws inspiration from entropy studies to improve trust and performance in autonomous human–machine teams and systems, including interconnected elements in hierarchical systems. Applying this lens to improve trust in AI also highlights new opportunities to optimize performance in teams. Two use cases are described to validate the AI framework’s ability to measure trust in the design and management of AI systems.

Cybersecurity vulnerability assessment tools, frameworks, methodologies, and processes are commonly used to understand the cybersecurity maturity and posture of a system or a facility. Although those tools are strictly developed based on standards defined by organizations such as the National Institute of Standards and Technology (NIST) and the U.S. Department of Energy, the majority of these tools and frameworks do not provide a platform to prioritize the requirements to reach a desired cybersecurity maturity. To address that challenge, we have been developing a framework and a software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). The efficacy of CyFEr was evaluated by implementing it on the NIST cybersecurity framework (CSF). This paper provides a detailed architecture of CyFEr and demonstrates its application to CSF by testing against a real-world cyberattack that targeted industrial control systems in a critical infrastructure facility.