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What if you could collaborate on AI projects, run complex models, fine-tune them, and even monetize both your models and data – all while retaining full control and ensuring confidentiality? It might sound impossible, especially when involving multiple participants you don’t need to trust – or even know.

In his article, “Web3 plus Confidential Computing,” Mike Bursell, Executive Director of the Confidential Computing Consortium,  delves into this challenge: “It turns out that allowing the creation of trust relationships between mutually un-trusting parties is extremely complex, but one way that this can be done is what we will now address.“

Mike explores the synergy of Confidential Computing, blockchain, and smart contracts, showcasing Super Protocol as a real-world implementation of this vision. He explains: “Central to Super Protocol’s approach are two aspects: that it is open source, and that remote attestation is required to allow the client to have sufficient assurance of the system’s security. Smart contracts – themselves open source – enable resources from various actors to be combined into an offer placed on the blockchain, ready for execution by anyone with access and sufficient resources. What makes this approach Web3 is that none of these actors needs to be connected contractually.”

This approach enables “the network effect… building huge numbers of interconnected Web3 agents and applications, operating with the benefits of integrity and confidentiality offered by Confidential Computing, and backed up by remote attestation.“ Unlike Web2 ecosystems, often criticized for “their fragility and lack of flexibility (not to mention the problems of securing them in the first place),” here is an opportunity to create “complex, flexible, and robust ecosystems where decentralized agents and applications can collaborate, with privacy controls designed in and clearly defined security assurances and policies.“

As Mike aptly puts it: “Technologies, when combined, sometimes yield fascinating – and commercially exciting – results.“

Explore the full article to dive into the basics, the synergy of these technologies, and the technical details of how Super Protocol is turning this vision into reality.

Read the Full Article

Author: Sal Kimmich

Introduction:

As enterprises increasingly adopt multi-cloud architectures, managing data governance across distributed systems has become more complex. With data privacy regulations like GDPR and CCPA requiring organizations to maintain strict control over sensitive information, ensuring compliance while leveraging the flexibility of multi-cloud systems presents a significant challenge.

Enter Confidential Computing: by using trusted execution environments (TEEs) and remote attestation across cloud platforms, organizations can ensure that sensitive data is processed in a secure and compliant manner. This blog will explore how decentralized data governance can be achieved in multi-cloud environments using confidential computing technologies.

Why Is Confidential Computing Essential for Multi-Cloud Data Security?

In a multi-cloud setup, organizations often distribute workloads across multiple cloud providers to meet their operational needs. However, this also increases the potential attack surface, as data flows through various infrastructures. Ensuring that data remains secure and compliant with regulations across these disparate environments is critical.

Confidential computing provides a solution by ensuring that sensitive data is processed in secure enclaves within TEEs, which isolate the data from unauthorized access. Using remote attestation, these TEEs can be verified, ensuring that the code executing within the enclave is trustworthy.

This ability to isolate and verify processing environments makes confidential computing essential for ensuring data security and governance across multi-cloud deployments.

What Is Decentralized Data Governance and Why Does It Matter in the Cloud?

Decentralized data governance refers to the practice of managing data policies, access controls, and compliance requirements across multiple locations or platforms without relying on a single centralized authority. In a multi-cloud environment, this is particularly challenging, as each cloud provider may have different security standards, policies, and regulatory requirements.

By decentralizing data governance, organizations can ensure that each cloud provider adheres to specific security and compliance rules. Confidential computing enables this by allowing organizations to enforce strict access controls and data policies at the TEE level, ensuring that data governance is maintained consistently, regardless of where the data is processed.

This approach to governance is crucial for businesses that need to operate in multiple jurisdictions or across cloud infrastructures, ensuring that they meet all relevant regulatory requirements.

How Open Enclave SDK Powers Secure Data Governance in Multi-Cloud Environments

One of the key tools that enables secure data governance in a multi-cloud environment is the Open Enclave SDK. Developed under the Confidential Computing Consortium, the Open Enclave SDK provides a consistent abstraction for creating TEEs across different platforms, including Azure, AWS, and Google Cloud.

By using the Open Enclave SDK, developers can build applications that securely process data in TEEs across multiple cloud environments without having to rewrite code for each cloud provider. This ensures that data remains secure and compliant with governance policies, regardless of the cloud infrastructure being used.

Additionally, the Open Enclave SDK supports remote attestation, allowing organizations to verify that data is being processed in trusted environments across all cloud platforms.

How Remote Attestation Ensures Compliance Across Multi-Cloud Systems

As organizations move workloads across different cloud providers, ensuring that each platform complies with relevant data privacy laws is a key concern. Remote attestation provides a mechanism to verify the security and integrity of TEEs, ensuring that sensitive data is processed only within approved environments.

In the context of GDPR, for example, remote attestation can help ensure that personal data is processed only within TEEs that meet the necessary security and privacy requirements. This ability to verify compliance on the fly allows businesses to confidently use multi-cloud infrastructures while maintaining adherence to data protection regulations.

Remote attestation helps organizations remain agile in the cloud while still upholding strict data sovereignty requirements, ensuring compliance with the CCPA, GDPR, and other global regulations.

Case Study: Confidential Computing in Real-World Data Sovereignty Challenges

A real-world example of decentralized data governance using confidential computing is the case of Italy’s Sovereign Private Cloud initiative. Italy’s government aimed to ensure that critical public sector workloads were processed within secure and private environments, adhering to the country’s strict data sovereignty laws.

By adopting confidential computing and remote attestation, Italy’s sovereign cloud enabled secure processing of sensitive public data across distributed environments. This approach ensured that even when data was processed outside of government infrastructure, it was handled securely in trusted execution environments, and compliance with Italian data protection laws was maintained.

To dive deeper into this solution, you can watch the session titled Sovereign Private Cloud: A Confidential Computing Solution for the Italian Public Administration from the Confidential Computing Summit 2024, where the implementation of the Sovereign Cloud is discussed in detail. The recording is available here.

This use case highlights how confidential computing can help address data sovereignty concerns, enabling organizations to operate securely across multiple cloud infrastructures without compromising compliance.

Achieving Decentralized Data Governance with Confidential Computing

As organizations continue to embrace multi-cloud strategies, managing data governance across distributed environments becomes more complex. Confidential computing offers a powerful solution by securing data in trusted execution environments and enabling remote attestation to verify compliance.

By leveraging tools like the Open Enclave SDK, businesses can maintain control over their data policies and ensure that sensitive information is processed in secure, compliant environments across all cloud platforms. As data sovereignty concerns grow, particularly in industries like healthcare and finance, confidential computing will play an increasingly important role in ensuring data governance and regulatory compliance across the multi-cloud landscape.

Hyperlinks Summary:

• Multi-Cloud Architectures: What Is Multi-Cloud?
• Confidential Computing: Confidential Computing Overview
• Trusted Execution Environments (TEEs): What Are TEEs?
• Remote Attestation: Remote Attestation Explained
• Open Enclave SDK: Open Enclave SDK GitHub
• Azure Confidential Computing: Azure Confidential Computing
• Google Cloud Confidential VMs: Google Cloud Confidential Computing
• GDPR: General Data Protection Regulation
• CCPA: California Consumer Privacy Act
• Sovereign Private Cloud Talk: Watch the Full Session on Sovereign Private Cloud

Author:  Sal Kimmich

Introduction

Imagine you’re working for a large healthcare provider. You have patient data that needs to be processed in the cloud, but you also want to make sure that this data isn’t accessed or tampered with by anyone, including the cloud provider itself. How can you trust that the cloud server is secure before sending sensitive information to it? That’s where remote attestation comes in. It’s like a virtual “security checkpoint” that ensures the environment where your data will be processed is trustworthy.

Now imagine you’re managing thousands of IoT devices in a smart city, such as street lights or traffic sensors, which are constantly sending data back to central systems. You need to know that these devices haven’t been compromised by hackers. Remote attestation (specifically, RATestation)  helps verify that these devices are secure and haven’t been tampered with, ensuring reliable and secure communication.

Remote attestation is a core component of Confidential Computing that helps verify the integrity of a processing environment in both cloud and IoT setups, building trust across these systems. 

As organizations increasingly adopt cloud and distributed systems, securing sensitive data has become more critical than ever. Remote attestation, a core component of Confidential Computing, verifies the integrity of a data processing environment before sensitive workloads are accessed. This technology builds trust across multi-cloud environments by ensuring workloads run securely within Trusted Execution Environments (TEEs). However, this need for security also extends to the rapidly growing Internet of Things (IoT), where secure real-time operations are crucial.

Remote Attestation in Cloud and IoT: The Key to Secure Data Processing

Remote attestation operates differently in cloud and IoT environments, but its core function remains the same: verifying that a piece of code or application is running inside a secure, trusted environment (TEE).

In cloud computing, remote attestation assures that sensitive workloads, such as financial transactions or healthcare data, are processed securely within TEEs. In IoT, where devices operate in often uncontrolled environments, remote attestation ensures that each device remains trustworthy and untampered with, allowing it to communicate securely with cloud services or other devices.

Confidential Computing for Cloud: In cloud computing, multi-cloud architectures require trust across several infrastructures. Remote attestation ensures that sensitive workloads run in verified TEEs, providing a secure way to meet strict compliance requirements such as GDPR and HIPAA.

Confidential Computing for IoT: Real-Time Security: In IoT environments, remote attestation ensures the continuous integrity of distributed devices. For example, connected medical devices or autonomous vehicles must maintain their trustworthiness during real-time operations. Remote attestation allows organizations to verify these devices dynamically, preventing compromised systems from accessing sensitive networks.

Several CCC projects actively contribute to remote attestation in cloud and IoT:

• Gramine: Primarily focused on Intel® SGX, Gramine supports secure workload execution across multi-cloud infrastructures, providing compatibility for legacy applications that require trusted execution environments.

• Veraison: This flexible framework verifies attestation evidence from TEEs across multiple architectures, validating the integrity of both cloud and IoT devices.

• Keylime: Particularly useful in IoT environments, Keylime offers remote boot attestation and real-time integrity monitoring, ensuring that IoT devices maintain a secure status during operations.

• SPDM Tools: Developed to secure TEE-I/O in both cloud and IoT, SPDM Tools verify that communications between devices remain secure within trusted execution environments.

• Open Enclave SDK: This project abstracts hardware differences and provides a unified API for building secure enclave applications, supporting both cloud-based and IoT use cases.

For more information on all of these projects, see the links below and visit our CCC Project Portfolio. 

How Remote Attestation Ensures Compliance with Global Data Privacy Laws

In industries governed by stringent data privacy laws such as GDPR (General Data Protection Regulation) in Europe and HIPAA (Health Insurance Portability and Accountability Act) in the US, compliance is a top priority. Remote attestation plays a pivotal role in ensuring that sensitive data is processed securely, in compliance with global privacy regulations.

1. GDPR Compliance: Remote attestation ensures that personal data is processed in verified, secure TEEs, preventing unauthorized access or tampering. This is particularly critical for organizations in Europe, where GDPR mandates stringent data protection and privacy standards. The ability to verify the integrity of the cloud infrastructure before processing data allows organizations to prove compliance during audits.
2. HIPAA Compliance: In the healthcare sector, remote attestation is essential for ensuring that sensitive patient data is processed securely in environments that comply with HIPAA. By confirming the integrity of the TEE, healthcare providers can securely manage electronic health records (EHRs), ensuring that patient data remains protected during transmission and processing.

Remote attestation provides organizations with the assurance that sensitive data is handled within secure environments that comply with privacy laws. As multi-cloud and IoT networks grow, ensuring compliance with these laws through verified environments will become even more critical.

Real-Time Trust in IoT: The Importance of Continuous Attestation

The challenge in IoT environments lies in ensuring that every device continuously adheres to security standards. For example, Keylime enables real-time integrity monitoring, ensuring that compromised IoT devices can be detected and isolated immediately. This is especially crucial in industries like healthcare, where real-time decision-making is directly influenced by the security status of devices.

The Future of Remote Attestation: From Cloud to Edge

Remote attestation is evolving to meet the demands of both cloud and IoT environments. As organizations adopt more complex multi-cloud infrastructures and IoT networks, the role of remote attestation will expand. Post-quantum cryptography and enhanced security measures such as multi-party attestation will improve the scalability of remote attestation in the future, making it more robust against emerging threats.

Conclusion: Building Trust with Remote Attestation

Remote attestation is a crucial tool for building trust in both cloud and IoT environments. Whether securing sensitive workloads in multi-cloud infrastructures or maintaining the integrity of millions of IoT devices, remote attestation ensures that data is processed in trusted, verified environments. CCC open-source projects such as Veraison, Gramine, Keylime, and SPDM Tools are leading the way in making remote attestation scalable and secure. As Confidential Computing continues to evolve, remote attestation will remain a cornerstone for ensuring security and trust across distributed systems.

Origin and Motivations Behind ATtestation

The RATtestation documentation emerged from the need to standardize remote attestation protocols across diverse Confidential Computing environments. The document addresses the challenge of securely verifying the integrity of systems in distributed and multi-cloud architectures. ATtestation defines best practices for trust establishment, data protection, and secure communication, ensuring the integrity of Trusted Execution Environments (TEEs). It emphasizes the role of remote attestation in enabling secure collaboration while maintaining compliance with privacy regulations such as GDPR and HIPAA.

RATtestation Documentation 

Resources Summary:

• • Trusted Execution Environments (TEEs): What are TEEs?
  • Confidential Computing: Confidential Computing Overview
  • Remote Attestation: Remote Attestation Explained
  • GDPR: General Data Protection Regulation
  • HIPAA: Health Insurance Portability and Accountability Act
  • Veraison: Veraison GitHub Project
  • SPDM Tools: SPDM GitHub Project

• Keylime: Keylime: Scalable Remote Attestation

• Gramine: Gramine: Confidential Computing for Cloud

• Post-Quantum Cryptography: NIST Post-Quantum Cryptography

By Sal Kimmich

As AI and machine learning continue to evolve, securing the entire lifecycle of AI models—from training to deployment—has become a critical priority for organizations handling sensitive data. The need for privacy and security is especially crucial in industries like healthcare, finance, and government, where AI models are often trained on data subject to GDPR, HIPAA, or CCPA regulations.

In this blog, we’ll explore how confidential computing enhances security across the entire AI model lifecycle, ensuring that sensitive data, models, and computations are protected at every stage. We’ll also examine the role of technologies like Intel SGX, ARM TrustZone, and trusted execution environments (TEEs) in achieving end-to-end security for AI workflows.

The AI Model Lifecycle: From Training to Deployment

The AI model lifecycle consists of several stages where sensitive data is exposed to potential risks:

1. Data Collection and Preprocessing: This is the stage where data is gathered and prepared for model training. In regulated industries, this data often contains personally identifiable information (PII) or other sensitive details.
2. Model Training: During training, AI models are fed data to learn patterns. This process is compute-intensive and often requires distributed systems or multi-cloud environments.
3. Inference and Deployment: Once trained, AI models are deployed to make predictions on new data. At this stage, the model itself and the inference data need to remain secure.

Each stage presents unique security challenges. Data can be exposed during preprocessing, models can be stolen during training, and sensitive inputs or outputs can be compromised during inference. Securing all aspects of the AI pipeline is critical to maintaining data privacy and ensuring compliance with regulations like GDPR and HIPAA.

How Confidential Computing Protects AI at Each Stage

Confidential computing provides a solution to these challenges by using trusted execution environments (TEEs) to secure data, models, and computations throughout the AI pipeline.

• Data Collection and Preprocessing: In this stage, TEEs ensure that sensitive data can be preprocessed in a secure enclave. Technologies like Intel SGX and ARM TrustZone create isolated environments where data can be cleaned, transformed, and anonymized without exposing it to unauthorized access.
• Model Training: Confidential computing plays a critical role during AI model training, where TEEs are used to protect both the training data and the model itself. By running the training process within a secure enclave, organizations can ensure that no external party—whether malicious actors or cloud providers—can access or steal the model.
• Inference and Deployment: After training, confidential computing ensures that the model remains protected during inference. Remote attestation allows organizations to verify that the AI model is running in a secure environment before it is deployed. This prevents data leakage during inference and ensures that the model’s predictions are based on trusted data inputs.

Intel SGX and ARM TrustZone: Securing AI Workflows

Intel SGX and ARM TrustZone are two leading technologies that enable confidential computing in AI pipelines by securing sensitive workloads at every stage.

• Intel SGX: Intel SGX provides hardware-based security by creating secure enclaves that isolate data and code during processing. In AI workflows, Intel SGX is used to protect data during preprocessing and model training, ensuring that sensitive data and AI models remain secure even in multi-cloud environments.
• ARM TrustZone: ARM TrustZone enables secure computation on mobile and IoT devices, providing isolated execution environments for sensitive AI models. ARM TrustZone is particularly useful in edge computing, where AI models are deployed close to data sources, and confidentiality is critical.

Both Intel SGX and ARM TrustZone provide the infrastructure needed to implement confidential AI pipelines, from data collection and training to inference and deployment.

Real-World Use Case: Confidential AI in Healthcare

A prime example of how confidential computing secures AI pipelines is in the healthcare industry, where AI models are often used to analyze sensitive patient data. By using confidential computing, healthcare organizations can ensure that patient records are protected during model training, and predictions are made without exposing sensitive data to unauthorized access.

In this case, confidential computing helps healthcare providers comply with regulations like HIPAA, while still benefiting from the insights generated by AI models.

Confidential Computing and AI Regulations: Ensuring Compliance with GDPR and HIPAA

As AI becomes more embedded in regulated industries, maintaining compliance with data privacy laws like GDPR and HIPAA is essential. Confidential computing ensures that sensitive data and AI models are protected at every stage of the AI lifecycle, reducing the risk of data breaches or unauthorized access.

By securing both data and models, confidential computing helps organizations meet the requirements for data minimization, transparency, and consent, ensuring that AI workflows remain compliant with global regulations.

AI Pipelines with Confidential Computing

As AI workflows become more complex and data privacy concerns grow, confidential computing will play a central role in securing the AI model lifecycle. From data preprocessing to model inference, confidential computing ensures that data and AI models remain protected in trusted execution environments, enabling organizations to deploy AI securely and compliantly.

With technologies like Intel SGX and ARM TrustZone, organizations can now secure their AI pipelines at every stage, ensuring privacy, security, and regulatory compliance in industries like healthcare, finance, and national security.

Hyperlinks Summary:

• AI and Machine Learning: What is AI?
• Confidential Computing: Confidential Computing Overview
• Trusted Execution Environments (TEEs): What Are TEEs?
• Intel SGX: Intel SGX Overview
• ARM TrustZone: ARM TrustZone Overview
• GDPR: General Data Protection Regulation
• HIPAA: Health Insurance Portability and Accountability Act
• Multi-Cloud Environments: What is Multi-Cloud?
• Edge Computing: What is Edge Computing?