As GenAI models become increasingly popular, software developers must focus efforts on maintaining data integrity to ensure their AI-driven solutions are precise, effective and efficient.

Data integrity begins with awareness. Many organisations do not fully understand what data they have, when it was added or what was updated over time, making it challenging to conduct data audits or ...

Data integrity is crucial for achieving reliable outcomes and regulatory compliance. Data integrity centers on quality, ...

In fast-evolving research and manufacturing landscapes, maintaining data integrity is more important than ever. Regulatory compliance, product quality and patient safety depend on the reliability of ...

This innovative method, designed to guarantee data integrity and secure provenance in environments where the Internet of Things is used, is called ZIRCON (Zero-watermarkIng-based data pRovenanCe ...