February 2026 Summaries

In a series of news updates spanning from late 2025 to early 2026, various developments highlight the role of Privacy-Enhancing Technologies (PETs) in transforming data governance and collaboration, particularly in healthcare and AI applications. Duality Technologies is prominently featured for its contributions to secure data analysis and collaboration, notably through its use of AWS Nitro Enclaves and support for federated learning frameworks like Flower. The company has partnered with entities like NHS England, the National Cancer Institute, and Oracle to facilitate privacy-preserving data handling in sensitive areas such as pediatric cancer research and government operations. Additionally, collaborations with NVIDIA and Google Cloud aim to advance GenAI workflows while maintaining data privacy, crucial for sectors like banking and healthcare. The updates also underscore the importance of preparing for quantum computing's impact on data security, urging managed service providers to develop quantum literacy to safeguard future operations.

Federated learning offers a novel approach to artificial intelligence by enabling model training across distributed data sources without the need to centralize sensitive information, thus preserving privacy and complying with regulatory constraints. This technique is particularly beneficial in sectors like healthcare, finance, government, insurance, manufacturing, marketing, and data services, where data cannot be easily pooled due to legal or operational barriers. By sending models to local data environments and only sharing model updates with a central orchestrator, federated learning facilitates secure collaboration and improves model performance across diverse datasets while maintaining data sovereignty. It is often paired with additional privacy and security measures such as secure aggregation and differential privacy to mitigate risks of information leakage. Duality Technologies provides infrastructure to support federated learning, ensuring ease of deployment, robust security, scalability, and governance, making it suitable for regulated and complex environments.

Privacy in healthcare has become foundational to patient trust and the lawful use of data, especially as AI becomes integral to diagnostics and health operations. The challenge lies in deploying AI without violating patient protection rules, a process often delayed by lengthy approval procedures involving data protection assessments and ethics reviews. However, privacy-enhancing technologies (PETs) such as federated learning and differential privacy are transforming these processes by embedding governance directly into AI systems, reducing risks traditionally associated with compliance. A case study involving NHS England and the National Cancer Institute demonstrates how PETs cut approval times significantly by keeping patient data local and secure, aligning with frameworks like the NIST AI Risk Management Framework. This shift from reactive to proactive compliance allows healthcare organizations to use sensitive data securely, promoting innovation without compromising on privacy standards. As healthcare data grows in volume and value, federated ecosystems supported by PETs offer a scalable and trustworthy model for AI development, positioning organizations that adopt these technologies as leaders in compliant AI.

Privacy-enhancing technologies (PETs) are revolutionizing the finance industry by enabling secure collaboration without the need to move or reveal sensitive data, thus reducing reliance on trusted third parties. Technologies such as secure multiparty computation (SMPC), federated learning (FL), and trusted execution environments (TEEs) allow institutions to perform joint analyses while maintaining data privacy, effectively replacing institutional trust with mathematical trust. PETs are transforming practices like portfolio optimization and fraud detection by allowing financial institutions to operate securely without exposing critical information, exemplified by JPMorgan's Prime Match platform, which conducts private auctions without revealing full order books. Duality Technologies plays a significant role by providing the necessary governance and infrastructure for implementing PETs in real-world environments, enabling institutions to analyze encrypted data, build AI models, and comply with regulations without compromising privacy. As a result, these technologies are poised to eliminate the traditional role of intermediaries in finance, promoting a future that is secure and private by design.

Data compliance is a critical framework for organizations to ensure that they are meeting legal, regulatory, contractual, and internal requirements related to data collection, storage, usage, sharing, retention, and auditing. This guide highlights the importance of data compliance, particularly in today's environment where data is dispersed across multiple cloud services and remote teams, and where regulations like GDPR, HIPAA, and FedRAMP are increasingly stringent. Data compliance involves maintaining privacy, security, and governance discipline throughout the data lifecycle, with an emphasis on accountability and proof of compliance through audit trails and evidence. The distinction between data compliance and data security compliance is noted, with the latter being a subset focused on technical safeguards. The text underscores the growing importance of data compliance due to expanding regulations, the rise of AI, and the demand for more comprehensive audit evidence. It also discusses the essential features of robust data compliance platforms, especially for sectors like healthcare and finance, and explores how privacy-preserving AI technologies can support compliance by reducing the need to move or centralize sensitive datasets.