Noname Security Confidential Computing on AWS

Anjuna seaglass container enclaves include Remote Attestation for the ability to verify that the API gateway (e.g. Kong) is indeed running inside an AWS Nitro based Trusted Execution Environment (TEE) and is running the expected trustworthy version of the software. Thereby preventing a malicious insider or bot from running the application outside a TEE, or inside a TEE but with a malicious version of the code. With this ability, enterprise DevSecOps and CISO teams can verify that the AI model or API gateway application server is indeed trustworthy. As organizations look to minimize their cyber risk, confidential computing has come to the forefront as a revolutionary approach to data security. Confidential computing ensures that sensitive information remains encrypted and protected, even while in use by applications, processes, or services, using powerful hardware security features in modern CPUs for isolation of sensitive code and data in operation. Unlike traditional security methods that focus on securing data at rest or in transit, confidential computing extends protection to data while it's being processed, ensuring its confidentiality throughout its full lifecycle. When combined with the power of Noname Security artificial intelligence (AI) and machine learning (ML), confidential computing enhances API security by protecting the sensitive data being sent from the Noname Kong plugin to the Noname remote machine learning engine. By also running the Noname machine learning engine inside confidential computing enclaves the result is Trustworthy AI facilitating compliance with regulatory requirements. By leveraging confidential computing and AI, organizations can deploy their own private machine learning instances that are purpose-built for securing API traffic rather than utilizing a public cloud API service, drastically reducing their attack surface. Despite best-in-class security access controls, rogue system administrators or workloads running on untrusted infrastructure can increase the risk of sensitive data exposure. Confidential computing enhances protection from both internal and external threats by helping organizations maintain control over data, mitigate the risk of data breaches, and achieve compliance with stringent data protection regulations. Confidential computing addresses several key security concerns, including: Data Confidentiality: Organizations that handle high volumes of sensitive data such as healthcare, finance, and government, need confidential computing to ensure that their valuable data remains encrypted and inaccessible to unauthorized entities, even when processed or analyzed in untrusted environments. Secure Processing: By leveraging hardware-based security mechanisms such as secure enclaves, confidential computing enables organizations to perform computations on encrypted data without exposing it to the underlying infrastructure. This mitigates the risk of data breaches and insider threats, enhancing the overall security posture. Regulatory Compliance: Confidential computing solutions help organizations comply with stringent data protection regulations such as GDPR, HIPAA, and PCI DSS by safeguarding sensitive data throughout its lifecycle. This reduces the potential for regulatory fines and penalties associated with data breaches or non-compliance. Scalability and Performance: Despite stringent security measures, confidential computing solutions offer scalability and high-performance computing capabilities, allowing organizations to process large volumes of sensitive data efficiently without compromising on speed or reliability.