The Rise of Confidential Computing: Securing Data in Use

In an age where data has become the lifeblood of digital business, protecting sensitive information from unauthorized access has never been more critical. While many organizations have developed robust solutions for securing data at rest (stored on disk) and data in transit (moving across networks), there remains a gap in protecting ***data in use*** —the moment when data is being processed by applications and systems. ***Confidential computing*** is emerging as the answer to this problem, providing organizations with the ability to secure data even while it is actively being processed.

Confidential computing represents a paradigm shift in data privacy and security. It allows sensitive information to be processed within a secure, isolated environment that remains protected from external and internal threats, including unauthorized administrators or malicious software. This approach has important implications for industries like finance, healthcare, cloud services, and artificial intelligence (AI), where data privacy is essential to maintaining trust and complying with regulations.

In this blog post, we'll explore the rise of confidential computing, how it works, the challenges it addresses, and the potential impact it has on securing data in use across different industries.

What is Confidential Computing?

At its core, ***confidential computing*** is a technology that aims to protect sensitive data while it is being used or processed. Unlike traditional methods that primarily focus on encrypting data at rest or in transit, confidential computing secures data ***in use*** by utilizing hardware-based security features. These features isolate sensitive workloads within ***trusted execution environments (TEEs)*** , ensuring that the data cannot be accessed or tampered with by the operating system, hypervisor, or even insiders with administrative privileges.

A TEE is a secure area of a computer's processor that creates a protected execution environment for sensitive data. It shields data from external threats, including malicious attacks or unauthorized access. For example, in cloud environments where data is processed by third-party cloud providers, confidential computing helps ensure that even the cloud provider itself cannot view or alter the data during computation.

One of the most well-known implementations of confidential computing is Intel’s ***Software Guard Extensions (SGX)*** , which allows developers to create secure enclaves in which sensitive code and data can be isolated and processed safely. Other major technology providers, such as AMD and ARM, have also developed their own confidential computing technologies, making this a rapidly growing field.

The Need for Confidential Computing

The rise of confidential computing is driven by several factors, most notably the increasing need for enhanced data privacy, the growing adoption of cloud services, and the rising concerns over cybersecurity threats. As businesses process more sensitive information, the need for securing this data while it is in use becomes more urgent.

- ***Data Privacy and Compliance***
In today's regulatory landscape, data privacy is a top concern for organizations. Laws such as the ***General Data Protection Regulation (GDPR)*** in Europe and the ***California Consumer Privacy Act (CCPA)*** in the United States impose strict requirements on how organizations handle personal and sensitive data. These regulations mandate that data must be protected at every stage of its lifecycle—storage, transmission, and processing. Confidential computing provides a way to meet these regulatory requirements by ensuring that sensitive data remains protected even when it is being actively used. For example, a financial institution processing customer transactions in the cloud can use confidential computing to ensure that the data remains secure during the processing phase, reducing the risk of a data breach or unauthorized access.
• Cloud Adoption and Multi-Tenancy
  The shift toward cloud computing has introduced new challenges in terms of data security. While cloud providers offer strong encryption for data at rest and in transit, the risk of data exposure during processing remains a concern, especially in multi-tenant environments where multiple customers share the same infrastructure. With confidential computing, organizations can protect sensitive workloads from other tenants and even from cloud provider administrators, providing an additional layer of trust and security in the cloud. For example, in a multi-tenant cloud environment, confidential computing ensures that data belonging to one organization cannot be accessed by another tenant or even by the cloud service provider itself. This allows organizations to confidently move sensitive workloads to the cloud without worrying about potential data exposure.
• Cybersecurity Threats
  The rise of sophisticated cyberattacks, such as advanced persistent threats (APTs) and insider attacks , has highlighted the need for better protection of data in use. Cybercriminals and malicious insiders often target data during its most vulnerable state—when it is being actively processed. Traditional security measures, such as encryption, cannot protect data at this stage, leaving a significant gap in an organization’s security posture. Confidential computing helps mitigate these risks by isolating sensitive data within secure enclaves. Even if a system is compromised by malware or an attacker gains privileged access, the data within the TEE remains secure and inaccessible. This provides a powerful defense against internal and external threats.

How Confidential Computing Works

Confidential computing relies on a combination of hardware and software technologies to create secure, isolated environments where data can be processed without exposure to potential threats. The key components of confidential computing include:

- ***Trusted Execution Environment (TEE)***
The TEE is the foundation of confidential computing. It is a secure area of a processor where sensitive code and data can be executed and processed in isolation from the rest of the system. The TEE ensures that only authorized code can access the data, and it is protected from tampering or exposure by the operating system, hypervisor, or other software running on the system.
• Secure Enclaves
  A secure enclave is a type of TEE that provides an additional layer of isolation for sensitive workloads. Data is encrypted when it enters the enclave, and it can only be decrypted and processed within the enclave itself. Once the data has been processed, it is re-encrypted before it leaves the enclave, ensuring that it remains protected throughout its lifecycle. For example, in a cloud environment, a secure enclave could be used to process sensitive healthcare data for an AI model. The healthcare data would be encrypted before entering the enclave, processed securely within the enclave, and then re-encrypted before leaving the cloud server. This ensures that the data remains protected at all times.
• Attestation
  Attestation is a process that verifies the integrity of the TEE and ensures that the enclave has not been tampered with. This is critical for establishing trust between the organization and the computing environment, particularly in cloud scenarios where the organization may not have direct control over the infrastructure. Attestation typically involves generating cryptographic proof that the code running inside the TEE is legitimate and has not been altered. This proof can be shared with external systems or clients to provide assurance that the data is being processed in a secure environment.

Benefits of Confidential Computing

Confidential computing offers several key benefits that make it an attractive solution for organizations looking to enhance their data privacy and security:

- ***Enhanced Data Privacy***
Confidential computing ensures that sensitive data remains protected at all stages of its lifecycle, including during processing. This is particularly important for organizations that handle sensitive information, such as financial institutions, healthcare providers, and government agencies. By isolating data within a TEE, confidential computing reduces the risk of unauthorized access, data breaches, and compliance violations.
• Increased Trust in Cloud Computing
  One of the biggest barriers to cloud adoption is the concern over data privacy and security. Confidential computing addresses these concerns by providing organizations with the ability to process sensitive workloads in the cloud without exposing the data to the cloud provider or other tenants. This increased trust in cloud security can accelerate cloud adoption, particularly for organizations in highly regulated industries.
• Protection Against Insider Threats
  Insider threats—whether malicious or accidental—pose a significant risk to data security. Confidential computing mitigates this risk by ensuring that even users with administrative privileges cannot access data inside the TEE. This is particularly valuable in environments where privileged access is necessary for system maintenance but poses a security risk for sensitive data.
• Improved Collaboration and Data Sharing
  Confidential computing also enables secure collaboration between organizations, allowing them to share sensitive data and workloads without compromising privacy. For example, multiple healthcare providers could collaborate on a research project involving patient data, using confidential computing to ensure that the data remains secure during analysis and processing.

Use Cases for Confidential Computing

Confidential computing is being applied across a range of industries to address data privacy and security challenges. Some notable use cases include:

- ***Financial Services***
Financial institutions are using confidential computing to protect sensitive customer data, such as payment information, transaction histories, and account details. By isolating sensitive workloads within secure enclaves, financial organizations can process transactions and perform analytics without exposing customer data to potential threats.
• Healthcare and Genomic Data
  In the healthcare industry, confidential computing is being used to protect sensitive patient information and genomic data during analysis. This technology enables secure processing of healthcare data for AI-driven diagnostics, clinical trials, and personalized medicine without compromising patient privacy.
• AI and Machine Learning
  Confidential computing is also being adopted in the AI and machine learning space to protect the sensitive data used to train models. Organizations can use confidential computing to securely process large datasets—such as customer information or proprietary business data—without risking data exposure or intellectual property theft.
• Cloud Computing
  For organizations moving sensitive workloads to the cloud, confidential computing offers a way to secure data during processing in shared environments. This is especially important in multi-tenant cloud environments, where data must be protected from other tenants and the cloud provider itself.

Challenges and Future Outlook

While confidential computing is a promising technology, it is still relatively new and faces several challenges, including performance overhead, complexity of implementation, and limited support from some legacy systems. However, with growing support from major tech companies like Intel, AMD, Microsoft, and Google, the adoption of confidential computing is expected to increase in the coming years.

As the technology matures, we can expect confidential computing to become a standard feature of cloud environments, enabling

organizations to process sensitive data securely and with greater confidence. In an era of increasing cybersecurity threats and data privacy regulations, confidential computing offers a powerful solution for securing data in use and ensuring the privacy of sensitive information.

Conclusion

Confidential computing is a game-changing technology that addresses a critical gap in data security by protecting sensitive information while it is being processed. By leveraging hardware-based security features such as TEEs and secure enclaves, confidential computing provides organizations with a way to secure data in use, reduce the risk of insider threats, and comply with data privacy regulations.

As businesses continue to move more of their workloads to the cloud and face evolving cybersecurity threats, confidential computing is poised to play an essential role in protecting data privacy and enabling secure collaboration in the digital world.