Why enterprises blame GDPR for an engineering problem they built themselves, and how privacy-ready data architecture actually accelerates AI Key Takeaways: The primary blocker to enterprise AI adoption is poor data architecture, not GDPR or the EU AI Act. Organisations with mature data governance report significantly fewer compliance-related delays in AI projects. Privacy-ready data architecture requires three capabilities: discovery, lineage, and automated classification. Pr

Enterprises store most of their sensitive data in unstructured documents. Without automated anonymization, that data is a compliance liability. Learn how Maya FileSafe solves this at scale.

Maya Data Privacy was present at the India AI Impact Summit 2026 in New Delhi, one of the largest AI gatherings ever held. Here is what we saw, what we demonstrated, and why it matters for the future of responsible AI.

Enterprises are sitting on a goldmine of data — but 80% of it never reaches an AI model. The reason is privacy. The fix is anonymization. Here is how leading organizations are unlocking their most sensitive data for AI, safely and compliantly.

Two approaches promise privacy-safe AI training data. Only one reliably handles enterprise complexity, regulatory scrutiny, and real-world data fidelity. Here's the honest comparison.

AI thrives on data — but most enterprise data is locked away in unstructured files, legacy systems, and compliance risk. Here's how to unlock it without compromising security or trust.