Detection of Deployment Operational Deviations for Safety and Security of AI-Enabled Human-Centric Cyber Physical Systems

TL;DR

This paper addresses the detection of operational deviations in AI-enabled human-centric cyber-physical systems, proposing a framework to ensure safety and security, exemplified by a novel method for blood glucose control in diabetics.

Contribution

It introduces a framework for evaluating strategies to maintain safety and security in AI-driven human-centric systems during deployment, with a novel personalized detection technique.

Findings

Framework effectively detects operational deviations.

Personalized image-based method identifies unannounced meals.

Enhances safety and security in critical AI-enabled applications.

Abstract

In recent years, Human-centric cyber-physical systems have increasingly involved artificial intelligence to enable knowledge extraction from sensor-collected data. Examples include medical monitoring and control systems, as well as autonomous cars. Such systems are intended to operate according to the protocols and guidelines for regular system operations. However, in many scenarios, such as closed-loop blood glucose control for Type 1 diabetics, self-driving cars, and monitoring systems for stroke diagnosis. The operations of such AI-enabled human-centric applications can expose them to cases for which their operational mode may be uncertain, for instance, resulting from the interactions with a human with the system. Such cases, in which the system is in uncertain conditions, can violate the system's safety and security requirements. This paper will discuss operational deviations that can lead these systems to operate in unknown conditions. We will then create a framework to evaluate different strategies for ensuring the safety and security of AI-enabled human-centric cyber-physical systems in operation deployment. Then, as an example, we show a personalized image-based novel technique for detecting the non-announcement of meals in closed-loop blood glucose control for Type 1 diabetics.