Confidential Computing

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,...

In 2026, secure data sharing goes beyond encrypting files and setting access permissions. Organizations in regulated fields like finance, healthcare, and AI face a deeper challenge: enabling collaboration while maintaining control over sensitive data. The difficulty lies in managing what happens after access is granted, how data is queried, combined, and reused. Most legacy security...

In the world of finance, the “trusted third party” has always been a necessary go-between. If you needed to optimize a client’s portfolio across different banks, you went through a custodian. If you wanted to check for fraud signals against industry peers, you uploaded your data to a central utility. This reliance on intermediaries wasn’t...

How do enterprises use federated learning? 7 real-world use cases for secure AI without exposing sensitive data.

Most Privacy-Enhancing Technologies (PETs) projects don’t fail because the math is flawed. They fail because they get stuck answering a deceptively simple question from the business: “So what?” When the pitch for PETs begins and ends with “privacy,” it often fails to gain traction. This isn’t because privacy is unimportant, but because most organizations already...

Federated learning has grown into a vibrant ecosystem with frameworks that excel at different stages of the journey from experimentation and prototyping to large-scale deployment across real organizations. As this ecosystem matures, interoperability has become mission-critical. Teams want the freedom to innovate with the tools they love while ensuring they can deploy confidently in governed,...