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Confidential Computing Solutions

Confidential Computing technologies are becoming the standard in private, public, and hybrid clouds.

Anjuna makes it simple for enterprises to implement Confidential Computing by allowing applications to operate in complete privacy and isolation, instantly and without modification. Anjuna Confidential Computing software supports custom and legacy applications—even packaged software such as databases and machine learning systems. Both on-site and in the cloud, Anjuna's broad support provides the strongest and most uniform data security across AWS Nitro, Azure, AMD SEV, Intel SGX, and other technologies.

Use Cases for Confidential Computing

Database Protection

Even secured databases need to store data unencrypted and exposed in memory. Anjuna assures that both the database itself and its data operate within the secure confines of a confidential cloud environment. Cryptographically and physically isolating data from malicious processes and bad actors virtually eliminate the chance of a data breach or exfiltration. Enterprises can protect their data in use, at rest, and in transit with a single approach. More importantly, Anjuna protection assures that IT insiders are never over-exposed to data they should not see—simplifying compliance efforts.

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Secure Cloud Migration

With Anjuna, migrating applications to the cloud means attaining a security posture that exceeds on-premises protection. Anjuna enables highly regulated industries to trust the public cloud by extending hardened security capabilities provided by Confidential Computing technologies. Now, any public cloud is the safest place for sensitive enterprise applications and data, eliminating the compromise between cloud economics and robust security. 

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PII Protection

Anjuna delivers the strongest and most complete data security and privacy control available. Sensitive data created, processed, stored, and networked is protected with hardware-rooted zero-trust protection. This protects personally identifiable information (PII) from insiders and bad actors throughout its lifecycle. These secure isolated environments deploy and scale invisibly with data, making deployment and operations simple and transparent to IT staff. And, unlike legacy layered security controls that must be active, data is protected by default, everywhere.

PII protection with confidential computing
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Powerful Threat, Vulnerability, and Risk Mitigation

As one of the most powerful security controls available, Anjuna effectively mitigates thousands of high-priority host, application, storage, and networking vulnerabilities present in today's enterprises. By creating an impenetrable contiguous data perimeter around data and running applications, operating system flaws and most zero-day exploits no longer present a threat to applications or data. Misconfigurations—even direct exposure to the Internet—are no longer a data security concern. Access to data is strictly controlled through policy originating from a default zero-trust security posture.

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Microsegmentation

Anjuna delivers simple, strong, and complete microsegmentationisolating workload compute, storage, and networking in the data center, over public clouds, and across the internet. This produces the strongest and most complete microsegmentation solution available for any cloud environment.

With Anjuna software, application microsegmentation is
intent-driven, automatically eliminating the deployment and management complexity associated with legacy network-only solutions. Enterprises deploy applications on any cloud or on-premises to achieve strong security without configuration changes or the need for network-based firewalls or complex access policies. Only those applications that are authorized to communicate with each other may do so.

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Multi-Party Computation (MPC)

Anjuna's Confidential Computing software allows two or more parties to share data, code, and algorithms without leaking their private data to one another. This is especially useful in such examples where the parties would like to perform computation together such as calculating credit risk scores or analyzing private health care data to develop machine learning and AI models. Data can be simply shared for targeted analysis and a shared output, but each party's sensitive data is never exposed or put at risk.

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