Anjuna Solutions for Technological Transformation

Organizations can overcome concerns about security and data privacy that prevent them from embarking on their journey from on premises operations to cloud services. 

‍Anjuna Confidential Computing Platform abstracts confidential computing hardware technologies, allowing you to easily deploy existing applications without re-coding across multiple service providers offerings, finally making hybrid cloud the safest place for sensitive workloads.

Applications use secrets, such as credentials, tokens, and API keys, which can be exposed in code, configuration files, and memory if hosts get compromised. 

Anjuna stores secrets securely inside the Anjuna Policy Manager and distributes them to trusted applications in a secure manner using attestation, significantly reducing the risk of security breaches.

Meeting geographic requirements is a key concern for many organizations when it comes to processing data in the cloud. Businesses subject to data residency, data localization, or data sovereignty regulations must show where their workloads are. 

Anjuna enables organizations to attest facts about their workloads, including details about the hardware on which they run and their location. This ensures that companies can prove their compliance with relevant regulations and maintain the security and privacy of their data.

Complying with data security and privacy regulations such as GDPR, PCI DSS, HIPAA, NHS laws, EU privacy regulations, and financial regulatory requirements is challenging for organizations. 

Anjuna can help address these challenges by running workloads and processing data in a way that is safe from viewing and tampering by unauthorized entities. Companies can ensure that sensitive data is processed confidentially and reduce the risk of penalties, legal action, and reputational damage.

By leveraging Confidential Computing and creating secure clean rooms, Anjuna allows organizations to share data for joint analysis, such as calculating credit risk scores or developing machine learning models, without exposing sensitive information. Each party can derive valuable insights from shared data analysis while ensuring the confidentiality of each other’s private data.