The framework will develop uniform formats and functions for APIs and other software interfaces, consumer data and decision-making algorithms and a secure enclave for data providers/consumers.

Data governance is the process of managing the availability, usability, integrity, and security of an organization’s data. Effective data governance requires a structured approach that covers data ...

Components of a data governance framework. There are four primary components: ... This pillar includes encryption, access control, and anonymization techniques. 4. Data architecture.

To support a constructive debate towards a balanced artificial intelligence (AI) approach, the BRICS leaders have agreed on a set of guidelines to foster responsible development, deployment and use of ...

Today, the rise of AI presents a similarly complex policy challenge. Much like Christopher Columbus’s “discovery” of the New ...

By systematically categorizing generative AI applications—evaluating the provider, hosting environment, data flow, and industry specificity—organizations can build a tailored governance ...

This prevents unauthorized access during data transfer and when it is stored. Ensure that the AI software uses strong encryption standards, such as AES-256 for data at rest and TLS 1.2 or higher ...

AI models often handle sensitive data, increasing the risk of data breaches, unauthorised access, and misuse. “Implementing a detailed security framework in this regard is essential,” Keyter ...

Time to data. Time to data is a common metric used in dataops to measure any data processing and access delays. It’s an important metric for organizations that still have batch processing jobs ...