Confidential computing protects data while software is actively processing it. IDC defines it as computation inside a hardware-based, cryptographically attested trusted execution environment (TEE) that isolates code and data from the host operating system and hypervisor. It adds protection for “data in use” to encryption at rest and in transit; it does not replace either control.
IDC’s definition of confidential computing
In its November 2025 white paper Unlocking the Future of Data Security: Confidential Computing (IDC #US53866125), IDC defines the technology as “the protection of data that is actively in use by performing computation in a hardware-based, attested trusted execution environment (TEE).”
A TEE is designed to keep sensitive instructions and memory contents confidential and unaltered even from privileged software outside the protected environment, including the host operating system or hypervisor. Hardware-backed measurements and cryptographic attestation provide evidence of the environment’s security state before a workload or its keys are trusted.
How it completes the encryption model
| Protection stage | What is protected | What confidential computing adds |
|---|---|---|
| Encryption at rest | Stored files, databases and backups | Does not protect plaintext after authorized software loads it for processing. |
| Encryption in motion | Data moving across networks | Does not by itself protect data once it reaches a running application. |
| Encryption in use | Data while it is processed in CPU and memory | A hardware-based, attested TEE isolates the workload and supports verification before sensitive data or keys are released. |
The three stages are additive. A confidential-computing design still needs sound storage encryption, transport security, identity controls and application security.
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What TEEs and attestation do
Isolation from the host
The workload runs in a protected hardware boundary intended to prevent the host operating system, hypervisor or other privileged infrastructure software from reading or changing its code and data.
Measurement and evidence
The platform records a cryptographic description of the TEE’s relevant security state. Attestation presents that evidence to a verifier, allowing a policy to distinguish an approved environment from one that is misconfigured or running unexpected software.
Policy-controlled key release
Applications can make access to encryption keys or confidential inputs conditional on a valid attestation result. This creates a verifiable trust decision rather than relying only on a cloud or data-center operator’s administrative assurances.
Protected execution
After the trust decision, the TEE processes the data and returns an authorized result. Attestation does not make the application automatically secure: flawed code, excessive permissions, compromised identities and unsafe outputs remain application and operational risks.
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IDC’s July 2025 survey indicates substantial adoption, while also showing that many deployments were still being tested. The survey covered 600 manager-level-or-higher IT leaders in 15 industries. Respondents worked at organizations with 500 to 10,000 employees and were involved weekly in specifying or developing systems that process confidential or regulated data.
| IDC finding | Reported share |
|---|---|
| Organizations already using confidential computing | 75% |
| In production | 18% |
| Actively piloting | 57% |
| Familiar with the concept | 73%, including 31% very familiar |
These are survey responses, not an independent census of the market or a performance test. The study was sponsored by the Confidential Computing Consortium, so the sponsorship and sample limits matter when interpreting the percentages. The figures support a practical conclusion: production use exists, but piloting remains the dominant stage among the surveyed organizations.
Where confidential computing is most useful
Secure AI training and inference
During training, a TEE can help protect proprietary models, sensitive training sets and intermediate results. During inference, it can keep an organization’s model and another party’s input data protected from the external execution environment. This is especially relevant when a model owner and data owner do not fully trust the infrastructure operator or each other.
Multiparty data collaboration
Organizations can collaborate on analytics while limiting exposure of each party’s raw records. The TEE approach is one option alongside secure multiparty computation and homomorphic encryption; the best choice depends on the threat model, workload and performance requirements.
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Healthcare, financial and public-sector teams can process regulated information in a controlled execution boundary and retain evidence about the environment that handled it.
Hybrid, on-premises and edge workloads
Confidential-computing capabilities can be considered for public cloud, private infrastructure, hybrid systems and edge deployments. The operational model changes by location, so key custody, attestation services, patching and physical control must be evaluated for each environment.
Intellectual-property protection
Organizations can use TEEs to reduce exposure of proprietary algorithms, models, formulas and high-value datasets while they are being processed.
How to compare confidential-computing options
| Decision area | Questions to answer |
|---|---|
| Deployment environment | Will the workload run in a public cloud, private data center, hybrid estate or at the edge? |
| TEE and attestation model | Which hardware boundary is used, who signs measurements, how are trust chains validated, and how are revocations handled? |
| Workload and data sensitivity | Which code, inputs, memory regions, model artifacts and outputs require protection? |
| Cloud interoperability | Can the same policy and attestation workflow operate across providers, or does it depend on one provider’s implementation? |
| Vendor lock-in | What would have to change if the TEE platform, cloud region or key-management service changed? |
| Performance impact | What latency, memory, accelerator and throughput effects appear for this specific workload? |
| Key lifecycle | Where are keys generated, stored, rotated, revoked and recovered, and what attestation state is required for release? |
| Regulation and residency | Do processing locations, audit evidence and subcontractor arrangements satisfy the applicable jurisdiction and sector rules? |
| Operating skills | Can the team build, verify, monitor and troubleshoot attestation and secure key-release policies? |
Open standards and vendor-agnostic frameworks can reduce migration risk, but interoperability must be demonstrated for the actual platforms and workload rather than assumed from a product label.
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| Challenge reported by IDC respondents | Share citing it |
|---|---|
| Validating attestation chains of trust | 84.5% |
| Perception that the technology is niche with limited proof points | 77.7% |
| Lack of skilled personnel | 74.7% |
| Inconsistent public-cloud approaches and vendor lock-in | 62.2% |
| Compute-performance deterioration | 21.3% |
The performance figure is a reported concern, not a published benchmark. Actual overhead depends on the TEE implementation, memory behavior, accelerators, I/O pattern and workload design. Attestation is often the harder operational problem: teams must maintain trusted measurements, certificate paths, verifier policy and responses to platform or software changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How DORA relates to confidential computing
IDC reports that 77% of surveyed organizations were more likely to consider confidential computing because of the European Union’s Digital Operational Resilience Act (DORA). DORA addresses the resilience and protection of financial entities and their information and communications technology providers, including availability, authenticity, integrity and confidentiality across data at rest, in transit and in use.
Confidential computing aligns most directly with the processing stage by adding controls and evidence around data in use. It is not, by itself, proof of DORA compliance. Organizations still need governance, incident response, access management, continuity measures, supplier oversight, testing and the other controls required by their regulatory obligations.
What organizations say they gain
The Confidential Computing Consortium’s 2025 announcement of the IDC study reported these named benefits:
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- 88% identified improved data integrity as the primary benefit.
- 73% cited confidentiality with proven technical assurances.
- 68% cited better regulatory compliance.
The same announcement reported full-production deployment rates of 37% in financial services, 29% in healthcare and 21% in government. Those sector figures come from the Consortium’s announcement and should not be treated as a universal industry adoption rate or combined with IDC’s overall 18% production figure without the underlying denominators.
A practical path to adoption
- Choose a bounded pilot. Select a workload with a clear confidentiality or integrity problem, measurable business value and a manageable data flow, such as a sensitive inference service or a cross-organization analytics job.
- Define the threat model. State whether the concern is a cloud operator, host administrator, hypervisor, co-tenant, compromised platform component or another party. Confidential computing protects specific boundaries; it does not address every threat.
- Map the data lifecycle. Identify where inputs, code, memory, temporary files, logs, model artifacts, outputs and backups exist, then retain encryption at rest and in transit around the TEE.
- Design attestation and key policy. Specify approved measurements, signer trust, revocation behavior, software-update handling and the conditions under which keys or data may be released.
- Test interoperability. Validate attestation chains, key-management integration, cloud or on-premises portability and failure recovery with the exact platforms that will run in production.
- Measure the real workload. Record latency, throughput, memory, accelerator use, operational effort and cost against the non-TEE design. Do not substitute vendor claims for workload-specific testing.
- Expand with governance. Document ownership, monitoring, incident response, audit evidence, residency decisions and skills requirements before moving additional regulated workloads.
IDC recommends measurable pilots, open standards, vendor-agnostic frameworks, third-party attestation and interoperability testing, along with participation in industry initiatives such as the Confidential Computing Consortium. Cloud providers, managed-service providers and consultants may help with access controls, secure data management and regulatory work, but responsibilities and independent verification should be defined contractually.
Quick Recap
What confidential computing cannot guarantee
- It does not replace encryption at rest or in transit.
- It does not repair vulnerable application code or prevent an authorized application from misusing data.
- It does not eliminate identity, key-management, patching, logging, availability or incident-response requirements.
- It does not make every cloud implementation interoperable; attestation formats, trust anchors and operational tooling can differ.
- It is not a “cure-all.” Its value depends on a correctly defined trust boundary, validated attestation and disciplined operations.
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