Confidential Computing

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,...

Protecting data today and decades into the future Quantum computing promises revolutionary advances. But it also poses one of the greatest threats modern cryptography has ever faced. When quantum computers reach sufficient scale, they’ll be able to break widely used public-key algorithms,  exposing data that was once considered secure. For governments and intelligence organizations, that...

Duality Technologies’ perspective on Gartner’s Top Strategic Technology Trends for 2026 A New Baseline for Data Trust In its newly released report, Gartner Top Strategic Technology Trends for 2026, Gartner places Confidential Computing among the three core “Architect” technologies shaping enterprise infrastructure over the next five years. According to Gartner: “By 2029, more than 75%...

Confidential computing refers to technologies that protect data while it is being processed, focusing on encryption in use, which complements traditional protections for data at rest and in transit. At the heart of this approach are Trusted Execution Environments (TEEs). These are isolated hardware-based environments within a CPU that securely process data, away from the...