PUBLISHER: AnalystView Market Insights | PRODUCT CODE: 1927688
PUBLISHER: AnalystView Market Insights | PRODUCT CODE: 1927688
Healthcare Confidential Computing Market size was valued at US$ 3,402 Million in 2024, expanding at a CAGR of 55.90% from 2025 to 2032.
The healthcare confidential computing market covers security technologies designed to protect sensitive healthcare data not only when stored or transmitted, but also while it is actively being processed in system memory. This is typically done through trusted execution environments (TEEs) or secure enclaves built into modern server processors, combined with software controls and attestation features that help confirm workloads are running in a protected environment. In healthcare, where data sets often include protected health information, medical images, genomics, clinical trial data, and insurance claims, confidential computing is becoming relevant because data-intensive analytics and AI projects increasingly require cloud infrastructure and cross-organization collaboration, which raises concerns about privacy, compliance, and exposure during computation.
Market interest is rising as hospitals, payers, and life-sciences organizations push further into cloud migration, automation, and AI/ML development. Many projects depend on sharing or combining data across multiple parties, such as multi-hospital research studies, federated learning programs, secure clinical trial data exchange, and fraud detection using claims data. Confidential computing helps these use cases move forward by reducing the risk that raw data can be accessed by unauthorized users, including threats from compromised infrastructure or overly broad administrative access. Adoption also depends on practical factors such as ease of integration with existing identity and access management, key management, and security monitoring tools, along with performance impact and the ability to support real healthcare workloads like imaging pipelines and clinical data platforms.
Healthcare Confidential Computing Market- Market Dynamics
Healthcare breach frequency and ransomware risk are pushing demand for stronger protection of data during processing
A major driver for healthcare confidential computing is the continuing surge in cyberattacks and data breaches, which is making it harder for healthcare organizations to rely only on traditional security methods like encryption at rest and in transit, especially when more analytics and AI workloads move into cloud environments. According to the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR) breach portal reporting, the United States has seen 600+ large healthcare breaches (500+ records) reported per year in multiple recent years from 2021 to 2024, showing that breach exposure is persistent rather than occasional. The business impact is also severe: According to the Ponemon Institute Cost of a Data Breach Report 2024 (published by IBM Security), healthcare recorded the highest average breach cost of USD 9.77 million in 2024, which increases urgency for stronger controls around sensitive workloads. Ransomware adds another layer of pressure because it disrupts hospital operations and increases the chance of data exposure. According to the Federal Bureau of Investigation (FBI) Internet Crime Report 2023, there were 2,825 ransomware complaints in 2023, reinforcing how widespread the threat has become. These trends make confidential computing more relevant in healthcare because it focuses on protecting "data in use" by isolating workloads in secure enclaves and supporting attestation, which helps reduce risk when processing protected health information for research, collaboration, and AI-driven clinical analytics.
Healthcare confidential computing adoption is being influenced strongly by the shift of sensitive healthcare workloads into cloud environments and by the growing use of privacy-sensitive analytics and AI. Cloud infrastructure is attractive for scaling compute for imaging, genomics, and large EHR datasets, but it also raises concerns about protecting protected health information during processing, not just during storage or transmission. Breach reporting shows why security teams are tightening requirements: According to the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR) breach portal reporting, the U.S. recorded 600+ large healthcare breaches (500+ records) per year in multiple recent years from 2021 to 2024, which keeps pressure high to reduce exposure in shared environments. Digitization also expands the available data pool that feeds analytics projects. According to the Office of the National Coordinator for Health Information Technology (ONC), nearly all non-federal acute care hospitals had adopted certified EHRs by 2021, increasing the volume of digital clinical data that can be analyzed and shared.
At the same time, the business case for stronger "data-in-use" controls is reinforced by breach costs: According to the Ponemon Institute Cost of a Data Breach Report 2024 (published by IBM Security), healthcare had the highest average breach cost at USD 9.77 million in 2024, supporting investment in confidential computing features such as secure enclaves and attestation to enable analytics and AI while reducing privacy and compliance risk.
Healthcare Confidential Computing Market- Geographical Insights
Healthcare confidential computing demand is building in regions where healthcare data is already highly digital, cloud migration is active, and privacy expectations are strict enough that advanced security controls become part of procurement. North America is a major market because breach reporting is frequent and healthcare organizations face strong compliance pressure, which increases interest in protecting data even during processing. According to the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR) breach portal reporting, the U.S. recorded 600+ large healthcare breaches (500+ records) per year in multiple recent years from 2021 to 2024, keeping security investment high. Europe also supports adoption because privacy regulation and multi-country research collaboration create ongoing demand for privacy-preserving analytics, while Asia Pacific is expanding as national digital health programs and hospital modernization increase the volume of electronic health data.
United States Healthcare Confidential Computing Market- Country Insights
The United States is the strongest single-country market because large-scale digitization and a high breach environment make confidentiality during computation a practical need, not just a nice-to-have. High availability of electronic clinical data supports more analytics and AI use, which increases exposure if controls are weak. According to the Office of the National Coordinator for Health Information Technology (ONC), nearly all non-federal acute care hospitals had adopted certified EHRs by 2021, which means data volumes are large and widely distributed across systems. Breach pressure remains persistent: According to HHS OCR, 600+ large breaches per year were reported in multiple recent years (2021-2024). The financial case is also strong: According to the Ponemon Institute Cost of a Data Breach Report 2024 (published by IBM Security), healthcare had the highest average breach cost at USD 9.77 million in 2024, which supports spending on enclaves, attestation, and workload isolation for cloud-based processing of protected health information.
The competitive landscape is led by hyperscale cloud companies and processor vendors, with support from specialist security software firms and large consulting and integration partners. Microsoft Corporation, Google LLC, and Amazon Web Services, Inc. are usually associated with strengths in delivering confidential computing as managed cloud services and integrating it with broader cloud security and compliance tooling. Intel Corporation and Advanced Micro Devices, Inc. are typically linked to the hardware foundation through trusted execution environments in widely deployed server platforms, while NVIDIA Corporation is often referenced where confidential computing is paired with AI acceleration and secure model training. International Business Machines Corporation and Oracle Corporation are commonly positioned around enterprise-grade security and hybrid deployments in regulated industries. Specialist vendors such as Fortanix, Inc. and Anjuna Security, Inc. are often tied to strengths in enclave management, runtime protection, and attestation/policy tooling that helps operationalize confidential computing across cloud environments. Accenture plc and Deloitte Touche Tohmatsu Limited are typically mapped to strengths in healthcare cybersecurity programs, compliance alignment, and integration work, which is important because real deployments depend on connecting confidential computing with IAM, key management, and monitoring.
In December 2025, U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR) (HIPAA enforcement and oversight authority) was cited in an update discussing potential HIPAA changes, including a proposed update to the HIPAA Security Rule issued in January 2025 that could require major cybersecurity investments if finalized, and a separate final rule aligning 42 CFR Part 2 substance use disorder record protections more closely with HIPAA that took effect on April 16, 2024 with required compliance by February 16, 2026, while broader HIPAA Privacy Rule changes proposed during 2020-2021 remained under consideration.
In August 2025, Google LLC (cloud provider) announced broader general availability for Intel TDX-based confidential computing services, including Confidential VMs, Confidential GKE Nodes, Confidential Space, and Confidential GPU options, expanding Intel TDX support from 3 regions (9 zones) to 10 regions (21 zones) on the C3 machine series, and adding Confidential VM and Confidential GKE Nodes with NVIDIA H100 GPUs on A3 instances to secure data-in-use for AI and machine learning training and inference.
In May 2022, BeeKeeperAI, Inc. (a healthcare AI security platform company) outlined how confidential computing can make multi-institution AI development more practical by running encrypted AI models on encrypted patient data inside secure enclaves, using Microsoft Azure confidential computing with Intel Software Guard Extensions (Intel SGX), and cited common adoption barriers such as contracting cycles of 9-18 months and model development costs of $750,000 to $2.5 million, while also claiming project timelines for dataset access and approvals can shrink from around two years to roughly 3-4 months when secure compute reduces data-sharing friction.