Top 10 Enterprise Confidential Computing Solutions in 2026

Key Takeaways

1. Enterprise data security requires a comprehensive strategy that involves data discovery, access controls, confidential computing, real-time detection, and backups.
2. Traditional encryption only protects data at rest and in transit. The moment data is decrypted for processing, it's exposed — and that's the gap confidential computing closes.
3. Without strong protection for data in every state, enterprises risk legal trouble, financial losses, and loss of customer trust.
4. Anjuna helps organizations in highly regulated industries run any workload inside hardware-enforced Trusted Execution Environments (TEEs), control access, govern AI, and continuously monitor for risks without rewriting applications.
5. Other top vendors include Fortanix, Edgeless Systems, Opaque Systems, Decentriq, Microsoft Azure Confidential Computing, Google Cloud Confidential Computing, AWS Nitro Enclaves, IBM Confidential Computing, and Intel Trust Authority.
6. When choosing a confidential computing solution, consider where your data and workloads live, which hardware TEEs you need to support, how attestation is verified, the Total Cost of Ownership, and your team's capacity.

From customer records and financial data to intellectual property, data is the most valuable asset most enterprises own, but it's also the one thing hackers want most. For years, "data security" meant encrypting data at rest and in transit and hoping the perimeter held. That's no longer enough. The moment sensitive data is decrypted for processing, it becomes visible to anyone with sufficient access to the underlying infrastructure: a compromised hypervisor, a malicious cloud admin, or an attacker who's already inside. Confidential computing closes that gap by keeping data encrypted even while it's in use.

In this guide, we'll cover everything you need to know about enterprise data security in the confidential computing era, including how it works, why it matters, and how to choose the right solution for your organization.

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Key Enterprise Security Components

Enterprise security is a layered strategy that involves more than just a single tool. It requires processes, standards, procedures, and technology that work across the entire organization. Antivirus software and a firewall are no longer sufficient. Data breaches can mean millions in ransom payments, regulatory fines, and lost customer trust.

So how does enterprise security work? These are some key components of a comprehensive data security strategy.

1. Data discovery and classification

You can't protect what you can't see. Data discovery and classification lays the foundation for data protection, identifying where sensitive data lives across your entire organization. This includes cloud storage, on-premises servers, databases, SaaS applications, and endpoints. Automated discovery tools scan for patterns (credit card numbers, social security numbers, health records, etc.) and classify data by sensitivity level. Without this layer, every other security control is operating blind.

2. Access controls and identity management

Once you know what data you have and where it lives, the next question is, "Who should be able to access it?" Access controls and identity management enforce the principle of least privilege. Every user, service, and device only gets the permissions they need to do their job and nothing more.

Components of access control and identity management include multi-factor authentication (MFA), single sign-on (SSO), role-based access control (RBAC), and privileged access management (PAM) for admin accounts. Zero-trust models, based on the principle of "never trust, always verify," verify access on every request, not just at login. Strong access controls dramatically reduce how devastating a compromised account is. For example, if an attacker accesses an intern's credentials, strong access controls prevent them from reaching your financial database.

3. Confidential computing: protecting data in every state

This is where most data security strategies fall short. Encryption for data-at-rest and in-transit protects data on disk and over the network, but the instant that data is loaded into memory for processing, it has to be decrypted — and it stays exposed for as long as it's being used. That's the "data-in-use" gap, and it's the one attackers, malicious insiders, and even cloud providers themselves can exploit.

Confidential computing closes it by running workloads inside hardware-enforced Trusted Execution Environments (TEEs), also called secure enclaves. Data, code, and encryption keys are isolated at the CPU level, so users with root access to the host, hypervisor, or cloud infrastructure cannot see what's inside. Cryptographic attestation verifies that the enclave and the code running in it are authentic and untampered before any sensitive data or secret is ever released. Traditional encryption and tokenization still matter for data at rest and in transit, and confidential computing is what extends that protection to the moment data is actually being computed on.

4. Real-time detection and response

Real-time detection and response limit how devastating an attack can be. Whenever a security event happens — whether it's a login or suspicious activity — an alert pops up to notify you immediately. Automated responses kick in to isolate the endpoint, revoke a session, or block an IP within seconds. This real-time reaction can be the difference between a contained incident and a full-blown breach. Most organizations use a combination of EDR, XDR, SIEM, and SOAR platforms to handle continuous monitoring and automated response.

5. Automated backup and recovery

Strong data protection should also include backup and incident response. Automatic backups provide assurance for any number of events that can cause data loss, from natural disasters to ransomware in your primary systems. The best data protection tools should provide rapid recovery options in case of data loss so you can get back to work ASAP.

Why Confidential Computing Is Essential for Enterprises

With so many organizations moving operations — and now AI workloads — to hybrid environments including multi-cloud infrastructure, protecting data while it's in use has become as important as protecting it at rest. Attackers, and the surface they can exploit, are constantly evolving, and the only way to stay ahead is a proactive strategy that doesn't stop at the edge of memory.

Here are the biggest reasons enterprises need confidential computing as part of their data protection strategy:

• The stakes are higher than ever. According to IBM, the average cost of a data breach in 2025 was $4.4 million. For a large enterprise, a single incident can wipe out a quarter's profit or even put the company out of business.
• Regulatory pressure is intensifying. GDPR fines can reach up to €20 million, or 4% of global revenue. CCPA, HIPAA, PCI-DSS, SOX, and a growing list of state privacy laws all carry significant penalties — and regulators increasingly expect verifiable proof of how data was protected, not just a policy document.
• Multi-tenant and shared cloud infrastructure raises the stakes. When workloads run on infrastructure you don't own or fully control, "trust the provider" isn't a security boundary. Confidential computing gives you a hardware-verified guarantee that even the infrastructure owner can't see your data.
• AI and ML pipelines are a new, high-value attack surface. Proprietary models, training data, and inference requests are some of the most sensitive assets an enterprise has, and they typically run fully decrypted in memory. Without confidential computing, that's a wide-open target.
• Ransomware attacks are extremely disruptive. Modern ransomware groups do more than just encrypt files — they exfiltrate data and threaten to leak it. Without proper backups, segmentation, and detection, recovery can take weeks or months.
• New technologies increase risk. Remote work, SaaS apps, APIs, IoT devices, and AI/ML pipelines add potential entry points for bad actors. Enterprise security has to evolve as fast as the business does.

Top 10 Solutions

While there are a number of vendors on the market, these are the top 10 enterprise confidential computing solutions to consider.

1. Anjuna

Anjuna is a leader in confidential computing, a security approach that protects data at all times — at rest, in transit, and in use. Traditional encryption stops working the moment data is loaded into memory for processing. Anjuna Seaglass closes that gap by running workloads inside hardware-enforced Trusted Execution Environments (TEEs), or secure enclaves, where data, code, and secrets are isolated from the rest of the infrastructure.

Core features include:

• Confidential containers: Isolate existing applications in secure enclaves with no re-coding necessary.
• Always-on encryption: Data is encrypted in all three states: at rest, in transit, and in use. Even if an attacker gains access, they cannot read the data being processed.
• Hardware-rooted attestation: Before an enclave boots, Anjuna cryptographically verifies that the infrastructure and application code are authentic and untampered. Secrets are only released to verified, trusted code.
• Multi-cloud and on-prem support: Anjuna works across AWS, Azure, GCP, and on-premises environments, on top of Intel SGX/TDX and AMD SEV-SNP hardware, without specialized skills or application refactoring.
• AI clean rooms: Anjuna Northstar provides the protection that Anjuna Seaglass provides - secure, isolated environments where multiple organizations can collaborate on sensitive data and workloads without exposing raw data to each other.

Who is Anjuna best for?

• Enterprises in highly regulated industries that need to meet strict compliance requirements like HIPAA, GDPR, or PCI-DSS. Confidential computing provides a verifiable security boundary that auditors love.
• Organizations running proprietary LLM AI models, algorithms, or trade secrets in cloud environments who want no-code-change enclave protection.
• Teams that want a single, consistent confidential computing layer across multi-cloud and on-prem environments, rather than being locked into one cloud's native TEE tooling.

2. Fortanix

Fortanix is one of the earliest pure-play confidential computing vendors, built by engineers who helped pioneer the category. It combines confidential computing with encryption, key management, and cryptographic posture management in a single platform, so enterprises can run sensitive workloads and AI inference inside TEEs while managing the keys that protect them from the same console.

Core features include:

• Confidential Computing Manager for deploying and orchestrating enclave-based workloads
• Confidential AI for protecting proprietary models and data during inference
• Integrated key management and hardware security module (HSM) capabilities
• Support for Intel SGX, Intel TDX, and AMD SEV-SNP
• Cryptographic posture management and discovery
• Multi-cloud and on-premises deployment

Who is Fortanix best for?

• Enterprises that want confidential computing bundled with encryption key management under one vendor
• Organizations running confidential AI inference workloads
• Security teams that already rely on Fortanix for HSM or key management and want to extend that relationship to TEEs

3. Edgeless Systems

Edgeless Systems, based in Germany, focuses on making confidential computing consumable for cloud-native and Kubernetes environments. Its flagship product, Constellation, is a CNCF-certified Kubernetes distribution that runs entire clusters including control plane and worker nodes inside hardware-enforced TEEs, encrypting data at rest, in transit, and in use by default.

Core features include:

• Constellation: a confidential Kubernetes distribution with cluster-wide encryption and isolation
• Contrast: a tool for adding confidential computing to existing container workloads
• Hardware-rooted remote attestation for the entire cluster
• Support for AMD SEV-SNP and Intel TDX
• Multi-cloud support across Azure, GCP, and AWS

Who is Edgeless Systems best for?

• Cloud-native and Kubernetes-first engineering teams that want confidential computing built into their container orchestration layer
• Organizations in Europe with strong data sovereignty requirements
• Teams that want an open-source-rooted approach to confidential computing

4. Opaque Systems

Opaque Systems focuses on confidential computing for data analytics and AI, letting enterprises run collaborative analytics, machine learning, and LLM workloads on encrypted data without exposing the underlying records to the platform operator or other parties.

Core features include:

• Confidential AI platform combining TEEs with cryptographic attestation
• Confidential data analytics and collaborative querying across parties
• Policy enforcement and audit trails for regulated AI workflows
• Integration with existing data science and ML tooling
• Hardware root-of-trust verification

Who is Opaque Systems best for?

• Organizations building AI and analytics pipelines on sensitive or regulated data
• Teams that need multiple parties to analyze shared data without any party seeing the others' raw records
• Data science teams that want confidential computing without changing their existing ML workflows

5. Decentriq

Decentriq is a confidential computing platform purpose-built for data clean rooms with secure environments where two or more organizations can jointly analyze data (for advertising, healthcare research, or banking consortia, for example) without any party, including Decentriq itself, ever seeing the other side's raw data. It's built on hardware confidential computing (AMD SEV-SNP) rather than software-only privacy techniques.

Core features include:

• Confidential data clean rooms for multi-party collaboration
• Support for large-scale industry consortia with dozens of participating organizations
• Compatibility with existing machine learning frameworks inside the clean room
• Hardware-backed attestation so even administrators cannot access underlying data
• Pre-built templates for media, healthcare, and financial services collaboration

Who is Decentriq best for?

• Organizations that need to collaborate on sensitive data with external partners, competitors, or industry consortia
• Media, healthcare, and financial services companies running audience or research collaborations
• Enterprises that want a clean-room-specific tool rather than a general-purpose confidential computing platform

6. Microsoft Azure Confidential Computing

Azure offers one of the broadest native confidential computing portfolios of any hyperscaler, spanning confidential VMs, confidential containers, and confidential AKS nodes, built on both Intel TDX and AMD SEV-SNP hardware, with Azure Attestation providing remote verification of TEE integrity.

Core features include:

• Confidential VMs (DCesv6/ECesv6 series) for VM-level isolation
• Confidential containers and confidential AKS nodes
• Azure Attestation for remote verification of enclave and platform integrity
• Confidential ledger and key management integrations
• Broad partner ecosystem (including vendors like Decentriq) built on top of the platform

Who is Azure Confidential Computing best for?

• Organizations already standardized on Azure that want native, first-party confidential computing without plans to move workloads to another cloud provider
• Teams comfortable managing TEE deployment and attestation themselves rather than through a third-party abstraction layer
• Enterprises that need confidential computing for a single-cloud environment

7. Google Cloud Confidential Computing

Google Cloud's Confidential Computing portfolio includes Confidential VMs and Confidential GKE Nodes, using Intel TDX and AMD SEV-SNP to encrypt memory with a dedicated key per VM and isolate workloads from the host and hypervisor.

Core features include:

• Confidential VMs across multiple hardware vendors (Intel and AMD)
• Confidential GKE Nodes for containerized and Kubernetes workloads
• Integration with Google Cloud's broader identity, key management, and compliance tooling
• Attestation support for verifying VM and node integrity

Who is Google Cloud Confidential Computing best for?

• Organizations running primarily on Google Cloud that want confidential computing native to the platform with no immediate plans to run workloads in other clouds
• Kubernetes-heavy teams already using GKE
• Enterprises that need confidential computing for a single-cloud footprint rather than a multi-cloud strategy

8. AWS Nitro Enclaves

AWS Nitro Enclaves let organizations create isolated compute environments within EC2 to process highly sensitive data and workloads, with no persistent storage, no interactive access, and no external networking into the enclave itself. It's a narrower, more infrastructure-level approach than platform vendors like Anjuna or Fortanix, built specifically for AWS environments.

Core features include:

• Isolated, hardened compute environments carved out of existing EC2 instances
• Cryptographic attestation to verify enclave code before releasing sensitive data
• No persistent storage or external networking inside the enclave
• Integration with AWS KMS for key release tied to attestation

Who is AWS Nitro Enclaves best for?

• Organizations fully committed to AWS that want to build custom enclave applications
• Teams with the engineering resources to design and maintain bespoke Nitro Enclave applications
• Use cases like isolated key management, tokenization, or PII processing within an existing AWS architecture

9. IBM Confidential Computing

IBM's confidential computing portfolio, anchored by Hyper Protect Services on IBM Cloud and LinuxONE, was one of the earliest enterprise confidential computing offerings on the market. It spans confidential containers, key management, and high-performance computing, and is often paired with Intel Trust Authority for independent attestation.

Core features include:

• Hyper Protect Virtual Servers and Containers
• Confidential computing across IBM Cloud and on-premises LinuxONE/Z systems
• Integrated key management with "keep your own key" guarantees
• Independent attestation options via Intel Trust Authority

Who is IBM Confidential Computing best for?

• Enterprises with existing IBM Z, LinuxONE, or IBM Cloud infrastructure
• Highly regulated industries such as banking and insurance with long IBM mainframe histories
• Organizations that want confidential computing paired with IBM's broader compliance and governance tooling

10. Intel Trust Authority

Intel Trust Authority is an independent, vendor-neutral attestation service rather than a full confidential computing platform. It verifies the trustworthiness of TEEs (Intel SGX and Intel TDX, in particular) across clouds, so enterprises don't have to rely solely on the cloud provider hosting the workload to vouch for its own hardware's integrity.

Core features include:

• Independent remote attestation for Intel SGX and Intel TDX environments
• Multi-cloud attestation that works across Azure, IBM Cloud, and other Intel-based infrastructure
• Policy-based verification before secrets or keys are released to an enclave
• Complements platform vendors and hyperscaler-native confidential computing offerings

Who is Intel Trust Authority best for?

• Organizations that want an attestation layer independent of the cloud provider hosting their workloads
• Enterprises running confidential computing across multiple clouds that want consistent, vendor-neutral verification
• Security and compliance teams that need to prove trust to auditors without relying on a single vendor's self-attestation

How to Choose the Right Confidential Computing Solution

With so many vendors and overlapping categories, picking the right solution can feel overwhelming. The key is to stop looking for the "best product” and start looking for the “best fit” for your specific environment and team capacity. Many organizations use a combination of tools to provide the right coverage for their needs.

Here are some tips to help you choose.

• Get a clear picture of your data and workload landscape. What types of data do you handle? PII, PHI, financial records, intellectual property, trade secrets? Where does it live and run, and who has access? What regulations apply? If you don't know, start with a discovery and classification tool before layering on confidential computing.
• Decide between a platform and native cloud tooling. Hyperscaler-native offerings (Azure, Google Cloud, AWS) work well for single-cloud environments but require your team to manage TEE deployment and attestation directly. Platform vendors like Anjuna abstract that complexity and work consistently across multiple clouds and on-premises for deployment flexibility.
• Check hardware and re-coding requirements. Confirm which TEE technologies (Intel SGX, Intel TDX, AMD SEV-SNP) the solution supports, and whether you'll need to re-architect applications or can lift-and-shift existing workloads into enclaves unchanged.
• Evaluate your environment and integration needs. Are you all-in on one cloud, multi-cloud, or hybrid? Some tools are cloud-specific, while others cover on-premises environments too. Consider how well it integrates with your existing tech stack.
• Assess the team you have. If you have a mature security team with enough resources, you can handle complex, powerful platforms and custom enclave development. Small or overstretched teams should look for something that takes less effort to deploy and requires no code changes.
• Run a Proof of Concept to test the solution in your own environment. A proper POC should deploy in your actual infrastructure, test your top 3 use cases, include your team, and measure attestation and performance overhead.
• Compare the Total Cost of Ownership. Factor in deployment costs, ongoing staffing, hardware/instance costs for TEE-enabled compute, scaling costs, and renewal escalations.

Conclusion

As cyberattacks grow, the need for comprehensive security becomes more critical than ever, and protecting data at-rest and in-transit is no longer enough on its own. Enterprise data security requires ongoing vigilance, quick response to events, and evolving tactics to keep ahead of hackers, including closing the data-in-use gap that traditional encryption leaves open. Anjuna provides the confidential computing foundation you need to prevent data loss, stay compliant with industry regulations, and work across a multi-cloud environment.

If you're ready to upgrade your organization's security posture, let's chat! Request a demo [https://www.anjuna.io/demo-request] or talk with one of our experts.

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FAQs

Why is confidential computing critical for enterprises?

Because the cost of leaving data exposed while it's being processed is higher than ever. Traditional encryption protects data at-rest and in-transit, but decrypts it for computation, and that’s the exact moment attackers, malicious insiders, or compromised infrastructure can access it. Confidential computing closes that gap. Beyond the financial hit of a breach, exposure during processing can damage customer trust, trigger regulatory fines, and even put a company out of business entirely.

How is confidential computing different from encryption?

Traditional encryption protects data at-rest (on disk) and in-transit (over the network), but the data has to be decrypted to be processed, computed on, or analyzed. Confidential computing uses hardware-enforced Trusted Execution Environments (TEEs) to keep data encrypted and isolated even while it's in use, verified through cryptographic attestation before any secret is released. It's not a replacement for encryption — it's the missing third state of protection. 

What types of enterprises benefit the most from confidential computing?

Every organization handles sensitive data, but the need is most pressing for:

• Highly regulated industries like healthcare, finance, and government
• Cloud-first and multi-cloud organizations running workloads on infrastructure they don't fully control
• Companies running proprietary AI/ML models or valuable intellectual property
• Organizations collaborating on sensitive data with external partners or industry consortia
• Enterprises with remote or hybrid workforces and expanding AI pipelines

Even SMBs and startups handling customer PII benefit enormously from strong defenses, including confidential computing where their workloads run on shared cloud infrastructure.

What features should I look for in a confidential computing solution?

Look for hardware-rooted attestation, support for the TEE technologies your infrastructure already uses (Intel SGX/TDX, AMD SEV-SNP), no-code-change deployment options, multi-cloud and on-premises coverage, AI governance and clean-room capabilities if you run AI workloads, and integration with your existing access controls and monitoring.

Why choose Anjuna as your confidential computing solution?

Anjuna specializes in confidential computing, protecting data even when it's in use, not just at-rest or in-transit. This matters for organizations running workloads in shared cloud environments where traditional encryption stops short. Anjuna works across cloud and on-prem environments, requires no re-coding of applications, and provides hardware-verified, zero-trust protection to keep data and AI workloads secure at every stage.

Can confidential computing help with regulatory compliance?

Yes! In fact, regulatory compliance is one of the main drivers of confidential computing adoption. Confidential computing gives organizations a verifiable, hardware-rooted security boundary — not just a policy — that helps satisfy HIPAA, GDPR, PCI-DSS, and similar requirements, and provides auditors with cryptographic evidence of how data was protected at every stage, including while it was being processed.