Protecting corporate data from leakage and misuse, and preventing unwanted, erroneous results of AI are top-of-mind for executives today. A lack of agreed-upon standards, guidelines, and regulations in North America is making the task more difficult. IT leaders can start by using data management technology for visibility on all their unstructured data across storage. This visibility is the starting point to better understanding this growing volume of data so that it can be governed and managed properly for AI. Data classification is another key step in AI data governance and involves enriching file metadata with tags for sensitive data identification that cannot be used in AI programs. Metadata enrichment is also available to help researchers and data scientists quickly curate datasets for their taiwan rcs data projects by searching keywords that identify file contents. With automated processes for data classification, IT can create workflows to continually send protected datasets to secure locations and, separately, send AI-ready datasets to object storage where they are ingested by AI tools. Automated data workflow orchestration tools will be important for efficiently managing these tasks across petabyte-scale data estates. AI-ready unstructured data management solutions will also deliver a means to monitor workflows in progress and audit outcomes for risk.
Role of storage administrator evolves to embrace security and AI data governance
Pressing demands on both the data security and AI fronts are changing the roles of storage IT professionals. The job of managing storage has evolved, with technologies now more automated and self-healing, cloud-based, and easier to manage. At the same time, there is increasing overlap and interdependency between cybersecurity, data privacy, storage, and AI. Storage pros will need to make data easily accessible and classified for AI while working across functions to create data governance programs that combat ransomware and prevent the misuse of corporate data in AI. Storage teams will need to know where sensitive data lurks and have tools to develop auditable data workflows that prevent sensitive data leakage.