As enterprises move more sensitive workloads to the cloud, “confidential computing” and advanced cloud-security models are emerging as critical enablers of trust and compliance.

The growing imperative for stronger cloud security

As organisations continue migrating mission-critical workloads to the cloud — whether public, private or hybrid — the security stakes are rising. Threat surfaces expand, regulatory demands intensify, and cloud complexity increases. According to recent research, nearly 61 % of organisations cite security and compliance as one of the biggest barriers to further cloud adoption. SAMAA TV
Compounding the issue, the introduction of AI workloads, non-human identities (service accounts, bots), distributed infrastructure and multi-cloud models are creating new vectors of risk. For example, one report found that 84 % of organisations use AI in the cloud, and 62 % of them have at least one vulnerable AI package. SAMAA TV

What is confidential computing?

Confidential computing is a relatively new paradigm — one that protects data not only at rest and in transit, but also in use. In other words, data is encrypted while being processed, isolating it from the host OS, hypervisors and even administrators. Semiconductor Engineering
This is enabled via Trusted Execution Environments (TEEs), secure enclaves in hardware, memory encryption, and rigorous attestation processes. One example: currently hardware from major chip vendors enables confidential computing, enabling workloads to run on encrypted memory and protected processors. SiliconANGLE

Why confidential computing matters for cloud

There are several reasons why this model is increasingly relevant for cloud environments:

• Sensitive workloads & data sovereignty – Industries like healthcare, finance, defense, and governments require high assurance that data is protected even while being processed in public or shared environments.

• Shared cloud infrastructure – In multi-tenant public clouds or hybrid clouds, isolating workloads is more complex; confidential computing adds a hardware layer of separation.

• Regulatory and trust demands – With evolving regulation (e.g., data-privacy laws) and heightened scrutiny over cloud service providers, enterprises need stronger proof of isolation and attestation.

• Enabling new workload types – AI workloads, large-scale compute, and collaboration across enterprises (including partnerships and supply-chains) increasingly require trust models that extend beyond traditional encryption.

Recent advancements and market momentum

The momentum behind confidential computing is accelerating. For instance:

• One recent article examines how confidential computing can end SaaS data breaches and raise the bar for secure workloads. Forbes

• A new collaboration between a major security company and a chip provider announced a platform enabling enterprises to deploy “agentic AI” in regulated environments — using confidential computing as a foundation. Venturebeat+1

• Cloud providers continue to roll out new hardware-level security options. For instance, one cloud provider announced general availability of new confidential computing options and updates to attestation services. Google Cloud

Key architectural and operational considerations

While confidential computing offers a strong promise, enterprises must approach it thoughtfully:

• Define the workload boundary – Which workloads truly require confidential computing? Not all cloud workloads need hardware-enforced confidentiality, but data-sensitive or regulated ones do.

• Integration with identity, key management, attestation – Secure enclaves still require strong identity, key-release, certificate/attestation flows, and governance around them.

• Cost and performance trade-offs – Hardware-based TEEs may impose overhead or limit choice of platforms; enterprises should evaluate cost-benefit.

• Vendor ecosystem maturity – Confidential computing is still evolving; check provider roadmaps, standards (e.g., Confidential Computing Consortium), and enterprise support.

• Monitoring and compliance – Even with hardware isolation, monitoring for misuse, configuring security controls, and aligning with compliance frameworks remain crucial.

• Hybrid and multi-cloud support – For enterprises with hybrid/multi-cloud architectures, ensuring that confidential computing works across clouds, private data centres and edge environments is important.

Strategic implications for enterprise IT

For enterprises, the adoption path might look like this:

1. Identify high-value, high-risk workloads (e.g., regulated data processing, third-party collaborations) and assess whether confidential computing is appropriate.

2. Map out hybrid/multi-cloud architecture: Determine where data resides, how it flows, and whether a hardware-trusted environment is required.

3. Choose cloud or infrastructure providers that support TEEs and attestation services, and evaluate inter-cloud portability or vendor lock-in.

4. Develop governance around enclave lifecycle, key management, attestation reporting, compliance auditing, and monitoring.

5. Pilot confidential computing with a defined workload to validate cost, performance and operational readiness — then scale as appropriate.

Closing Thoughts

Enhanced security and confidential computing mark a turning point in how enterprises think about cloud trust. As cloud consumption expands, data becomes more distributed and regulated, and workloads more complex (AI, multi-tenant, federated), the need for protected execution environments is real. Organisations that adopt confidential computing strategically — not just as a checkbox, but as part of the core architecture — will be better positioned to innovate securely and maintain competitive advantage.

Author: Serge Boudreaux – AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck – Miami, Florida

References

1. “Confidential Computing To Secure AI Workloads”, SemiEngineering. https://semiengineering.com/confidential-computing-to-secure-ai-workloads/

2. “New physical attacks are quickly diluting secure enclave defenses from Nvidia/AMD/Intel”, Ars Technica. https://arstechnica.com/security/2025/10/new-physical-attacks-are-quickly-diluting-secure-enclave-defenses-from-nvidia-amd-and-intel/

3. “New Confidential Computing updates for more hardware security options”, Google Cloud blog. https://cloud.google.com/blog/products/identity-security/new-confidential-computing-updates-for-more-hardware-security-options

4. “Confidential computing’s role in ending SaaS data breaches”, Forbes. https://www.forbes.com/councils/forbestechcouncil/2025/01/30/confidential-computings-role-in-ending-saas-data-breaches/

5. “Strong Adoption of AI-Powered Experience Orchestration …” Genesys newsroom. https://www.genesys.com/company/newsroom/announcements/strong-adoption-of-ai-powered-experience-orchestration-capabilities-fuels-genesys-cloud-momentum