Ethical AI and QA-Driven Cybersecurity Risk Mitigation for Critical Infrastructure

Abstract

The critical infrastructure is becoming more dependent on artificial intelligence (AI) to monitor, detect and respond to cybersecurity threats, including energy grids, healthcare networks, and transportation systems. Nonetheless, with the implementation of AI, there are also other risks, such as the manipulation of algorithms, the manipulation of models, and the risk of privacy invasion. The study examines the application of ethical AI concepts and quality assurance (QA)-based models in enhancing cybersecurity risks mitigation of critical infrastructure. It highlights the importance of transparency, fairness, and accountability in AI driven threat detection, and strict QA testing of AI models to ensure ongoing testing, validation and monitoring. The research report focuses on an organized process of risk scoring, prioritization, and automated system response and maintaining ethical and regulatory standards. With the predictive capabilities of AI coupled with QA and supervision, such work is a roadmap on how to construct resilient, trustworthy, and ethically dedicated cybersecurity systems that will resist emerging threats to critical infrastructure.