Confidential Computing

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....

Sensitive data is one of the most valuable, and most vulnerable assets any organization holds. From customer information and financial records to proprietary algorithms and health data, mishandling it can result in regulatory fines, reputational damage, operational disruption, and even national security risks. The challenge is that not all sensitive data is the same. Different...

Federated learning has moved from academic curiosity to enterprise necessity. As organizations face tighter privacy regulations, fragmented data environments, and rising compute costs, the federated learning architecture they choose can determine whether a project succeeds or stalls. But here’s the challenge: there isn’t just one architecture. Centralized aggregation models, decentralized peer networks, hybrid orchestration layers,...