Implement hardware-based confidential computing to meet data-in-use mandates under GDPR, HIPAA, and the EU AI Act.
Services
AI Model Confidentiality for Regulatory Compliance
Hardware-based confidential computing implementation to meet data-in-use protection mandates under GDPR, HIPAA, and the EU AI Act for AI systems processing personal data.
AI MODEL CONFIDENTIALITY FOR REGULATORY COMPLIANCE
The Compliance Gap in AI: Data-in-Use is Your Biggest Risk
Regulations like the EU AI Act and GDPR Article 32 explicitly mandate the protection of personal data during processing. Traditional encryption secures data at rest and in transit, but leaves it exposed in memory during AI inference—creating your largest compliance liability.
We architect hardware-based Trusted Execution Environments (TEEs) like Intel SGX and AMD SEV to create cryptographically isolated enclaves where your AI models run. Sensitive data is decrypted, processed, and re-encrypted solely within this secured memory, invisible to the host OS, cloud provider, or any other process.
Direct Outcomes for Compliance Teams:
- Demonstrate technical compliance with data-in-use requirements under GDPR, HIPAA, and emerging AI regulations.
- Pass rigorous audits with cryptographic attestation reports proving data was processed only within certified enclaves.
- Eliminate breach reporting triggers for AI systems processing personal data, as plaintext data is never exposed.
This is not a theoretical layer. We integrate TEEs directly into your production AI pipelines. Explore our foundational service on Confidential Computing for AI Workloads or see a specific implementation for Confidential AI Inference Enclave Development.
DELIVERING TANGIBLE VALUE
Business Outcomes: Beyond Checking a Compliance Box
Our confidential AI engineering delivers measurable business advantages, turning regulatory mandates into competitive differentiators.
01
Accelerated Market Entry
Deploy compliant AI systems in weeks, not months. Our pre-architected frameworks for GDPR, HIPAA, and EU AI Act compliance eliminate lengthy security reviews, enabling faster product launches and time-to-value.
< 4 weeks
Average Deployment
60%
Faster Compliance Review
02
Reduced Operational Risk
Mitigate multi-million dollar fines and reputational damage. Hardware-based TEEs provide provable data-in-use protection, creating an auditable chain of custody that satisfies regulators and builds stakeholder trust.
Zero
Data Breach Incidents
100%
Audit Readiness
03
Unlock Sensitive Data Assets
Safely utilize previously restricted datasets (PII, PHI, financial records) for AI training and inference. Secure multi-party computation enables collaborative analytics without data sharing, creating new revenue streams from siloed information.
04
Protect Core IP
Safeguard proprietary AI models and algorithms as competitive assets. Encrypted model deployment within Intel SGX or AMD SEV enclaves prevents reverse-engineering and theft, even by privileged cloud insiders.
05
Future-Proof Architecture
Build on a foundation that adapts to evolving global regulations. Our confidential computing architecture is designed for extensibility, simplifying compliance with emerging mandates like the EU AI Act's high-risk system requirements.
ISO/IEC 27001
Aligned Framework
NIST AI RMF
Integrated Controls
06
Enhanced Partner & Customer Trust
Win contracts in regulated industries by demonstrating superior data stewardship. Provable confidential computing controls become a key differentiator in RFPs for healthcare, finance, and government sectors, directly impacting deal velocity.
Compliance Architecture
Mapping Technical Controls to Regulatory Mandates
How Inference Systems' confidential computing controls directly satisfy data-in-use protection requirements under major regulations.
| Regulatory Mandate | Technical Control | Inference Systems Implementation |
|---|---|---|
GDPR Article 32 (Security of Processing) | Data Protection by Design & Default | End-to-end encryption within Intel SGX/AMD SEV enclaves; data never decrypted outside TEE |
HIPAA Security Rule §164.312 (Technical Safeguards) | Access Control & Integrity Controls | Hardware-rooted attestation for authorized code; memory encryption prevents unauthorized access to PHI during AI inference |
EU AI Act (High-Risk Systems) Annex III | Data & Model Governance for High-Risk AI | Tamper-evident logging of all enclave activity; verifiable audit trails for model weights and inference data |
PCI DSS Requirement 3.4 | Render PAN unreadable anywhere stored | Credit card data processed in-memory within attested enclaves; no plaintext persistence in logs or storage |
SEC Rule 17a-4(f) / FINRA 4511(c) | Preservation & Integrity of Electronic Records | WORM-compliant logging of attestation reports and model inference events for financial AI audits |
NIST AI RMF (Govern) - MAP Category | Measurable AI System Performance & Monitoring | Real-time monitoring of TEE health and attestation status integrated into enterprise AI governance dashboards |
CCPA/CPRA (Consumer Rights Requests) | Limited Data Retention & Deletion | Ephemeral enclave sessions; automated cryptographic shredding of all session data post-inference |
FedRAMP Moderate / High Baseline | System & Communications Protection (SC) Family | Architecture patterns for air-gapped, sovereign AI deployments meeting FedRAMP controls for government data |
COMPLIANCE-FOCUSED AI
Regulated Industries We Serve
Our confidential computing implementations are engineered to meet the stringent data-in-use protection mandates of highly regulated sectors, enabling secure AI innovation without compliance risk.
01
Healthcare & Life Sciences
Deploy HIPAA-compliant AI for clinical decision support, medical imaging, and patient risk analytics. Sensitive PHI is processed within hardware-secured enclaves, ensuring data never leaves the protected memory environment during inference. Our solutions support ambient AI documentation and genomic analysis.
HIPAA
Compliance
TEE-Based
PHI Processing
02
Financial Services & FinTech
Secure algorithmic trading, real-time fraud detection, and credit risk modeling within attested enclaves. Protects proprietary models and sensitive PII/transaction data to meet GLBA, SOX, and PCI-DSS requirements. Enables secure multi-party computation for consortium fraud networks.
SOC 2 Type II
Audited
GLBA/SOX
Alignment
03
Defense & Government
Develop air-gapped, hardware-rooted AI systems for classified data processing and geospatial intelligence. Our TEE integrations ensure model integrity and prevent data exfiltration on compromised infrastructure, complying with ITAR, FedRAMP, and CMMC frameworks for national security applications.
FedRAMP
Ready
CMMC
Aligned
04
Legal & Compliance
Implement confidential AI for contract analysis, predictive litigation, and regulatory compliance auditing. Processes sensitive case files and client data within secure enclaves to uphold attorney-client privilege and meet data residency mandates under regulations like GDPR and the EU AI Act.
GDPR
Article 32
EU AI Act
High-Risk
05
Insurance
Enable privacy-preserving AI for claims processing, underwriting, and risk assessment. Our confidential computing architecture allows analysis of sensitive customer data (health, financial, telematics) without exposing it, ensuring compliance with state-level privacy laws and NAIC guidelines.
State-Level
Privacy Laws
NAIC
Aligned
06
Biometrics & Identity
Specialized TEE deployment for secure facial recognition, fingerprint verification, and voice authentication systems. Protects biometric templates and live match data end-to-end, critical for compliance with BIPA, GDPR biometric provisions, and emerging AI regulations governing remote identity proofing.
BIPA/GDPR
Compliant
Template
In-Enclave Match
STRATEGIC IMPLEMENTATION
Our Proven Engagement Process for Compliance
A structured, four-phase methodology to deploy compliant confidential AI systems that meet stringent regulatory audits.
We translate complex mandates like GDPR Article 32, HIPAA Security Rule, and the EU AI Act into actionable technical controls, delivering audit-ready systems in 6-8 weeks.
- Phase 1: Regulatory Mapping & Threat Modeling We conduct a gap analysis against your specific regulatory obligations, identifying high-risk data flows. Our team defines the Trusted Computing Base (TCB) and threat model using frameworks like MITRE ATLAS to scope the required TEE protections.
- Phase 2: Architecture & Control Design
We design the confidential computing architecture, selecting the appropriate hardware TEE (
Intel SGX,AMD SEV,AWS Nitro Enclaves). We implement policy-as-code for data lineage, access logging, and cryptographic attestation to demonstrate compliance.
- Phase 3: Secure Development & Integration Our engineers refactor your AI pipeline, ensuring sensitive data is decrypted, processed, and re-encrypted only within the secure enclave. We integrate with your existing MLOps stack (Kubeflow, MLflow) and establish continuous attestation.
- Phase 4: Validation & Operational Handoff We perform penetration testing and generate the audit evidence package, including attestation reports and access logs. We provide operational runbooks and can manage the environment via our Confidential AI Managed Services.
AI Model Confidentiality
Frequently Asked Questions on AI Compliance
Get clear, technical answers on implementing confidential computing to meet stringent data-in-use protection mandates under GDPR, HIPAA, and the EU AI Act.
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01
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02
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03
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30m
working session
Direct
team access