Confidential Computing

If you’re comparing pseudonymization vs anonymization, the most important thing to know is that choosing the wrong approach can have serious compliance and security consequences. Under GDPR, pseudonymized data is still considered personal data, while properly anonymized data is not. That distinction affects everything from AI development and data sharing to regulatory compliance and breach...

Most security teams feel fairly confident about how they protect sensitive data. They encrypt databases. They mask data in test environments. They have policies in place. Job done. But here is the problem. Neither data masking nor encryption protects data while it is actually being used: while a query is running, an AI model is...

Data governance vs data architecture is one of the most misunderstood distinctions in enterprise data strategy. Most organizations have built one without the other. They invest in data architecture (storing, moving, and processing data efficiently) while governance remains a spreadsheet and a compliance checkbox. Or they enforce rigid governance rules that make their architecture painful...

For years, Fully Homomorphic Encryption (FHE) has been the “holy grail” of data privacy. The promise is incredible: the ability to compute on encrypted data without ever needing to decrypt it. In a world of constant data breaches and strict privacy regulations, it allows for a “privacy-by-design” economy where sensitive information like medical records or...

Most organizations have invested heavily in protecting data at rest and data in transit. Encryption standards are mature, compliance frameworks require them, and security teams understand how to implement them. However, the greatest exposure often occurs when data is actively being used. Data in use protection refers to the technologies and security controls that protect...

Data at rest vs data in transit is the comparison between the two classic states of data — stored data and data moving across a network — and each state requires a different type of encryption. There is also a third state, data in use, which covers data being actively processed. Most organizations think of...

Fully homomorphic encryption (FHE) lets you compute directly on encrypted data without ever decrypting it. For years, that capability came at a cost that made enterprise deployment impractical: operations that ran in milliseconds on plaintext took minutes or hours under encryption. Homomorphic encryption performance in 2026 tells a different story. Algorithm improvements, compiler optimization, batching...

Your legal team just approved a new AI project. The model is powerful, the use case is clear, and everyone is excited. Then someone asks the question nobody wants to answer: “What happens to the sensitive data we feed into this thing?” That moment of silence is familiar to almost every enterprise AI team right...

Most data breaches don’t happen because attackers broke an advanced algorithm. They happen because data was sitting somewhere, unencrypted, and the wrong person got to it. That’s the core problem with data at rest. It’s everywhere, databases, backup drives, file servers, cloud storage buckets, and it’s stationary, which makes it an easy target. Unlike data...

The Change Healthcare breach of 2024 exposed the records of 192 million individuals – the largest healthcare data breach in U.S. history, per HHS OCR. The organization had a DLP policy. A global bank with enterprise-grade DLP tooling watched sensitive client data flow quietly into employee personal cloud apps, week after week, undetected. The U.S....