Security and Safety Aspects of AI in Industry Applications

TL;DR

This paper discusses safety and security challenges in AI applications within industry, focusing on neural network vulnerabilities like adversarial attacks and the importance of risk assessment for real-world deployment.

Contribution

It provides an overview of safety and security issues in industrial AI, highlighting vulnerabilities and the need for risk assessment in deploying neural network-based systems.

Findings

Neural networks in industry face security threats like adversarial attacks.

Safety concerns impact the adoption of AI in real-world applications.

Risk assessment is crucial for safe and secure AI deployment.

Abstract

In this relatively informal discussion-paper we summarise issues in the domains of safety and security in machine learning that will affect industry sectors in the next five to ten years. Various products using neural network classification, most often in vision related applications but also in predictive maintenance, have been researched and applied in real-world applications in recent years. Nevertheless, reports of underlying problems in both safety and security related domains, for instance adversarial attacks have unsettled early adopters and are threatening to hinder wider scale adoption of this technology. The problem for real-world applicability lies in being able to assess the risk of applying these technologies. In this discussion-paper we describe the process of arriving at a machine-learnt neural network classifier pointing out safety and security vulnerabilities in that workflow, citing relevant research where appropriate.