Data Management and Security in Hyper Automation

To accelerate technological transformation in businesses, hyperautomation is emerging rapidly as an effective approach to upgrade processes, reduce the involvement of humans, and develop organizational efficiency. The term hyperautomation refers to the integration of current technology, such as Artificial Intelligence (AI) and machine learning, to organize tasks that have historically required human collaboration. It also includes scheduling, understanding, knowledge, examining, adjusting, and even predicting results. To construct entirely autonomous systems that can adapt and acquire from new data, it syndicates several types of robotics knowledge, including Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), and data analytics. It relies extensively on information to drive intelligent growth and equips computers for decision-making. To tolerate operational efficiency, maintain data integrity, and comply with regulatory compliance, organizations must address the critical challenges related to data management, security, and governance that arise from handling vast volumes of information within hyperautomated systems. This chapter examines the strategies, frameworks, best practices, and challenges associated with data management and security in hyperautomation.