Secure AI Infrastructure • HIPAA & CMMC Compliant GPU Hosting

AI Infrastructure Built for
Regulated Industries.

Cloud AI means cloud risk. Petronella builds hardened AI infrastructure on your premises — GPU servers, inference engines, and AI platforms deployed inside your security boundary with encryption, access controls, and audit logging that satisfy HIPAA, CMMC, SOX, and PCI DSS from day one. Air-gapped options for classified environments.

HIPAA • CMMC • SOX • PCI DSS • NIST 800-53 • Air-Gapped Options

AES-256
Encryption at Rest
& In Transit
0
Data Breaches Among
Compliant Clients
100%
On-Premise
Data Sovereignty
23+
Years Cybersecurity
Experience
The Problem

Cloud AI Is a Compliance Minefield

Every major cloud AI platform processes your data on shared infrastructure you don’t control, in regions you can’t verify, with retention policies you can’t enforce.

Shared Infrastructure

Cloud GPU instances process workloads from thousands of tenants on shared hardware. Side-channel attacks, memory residual risks, and multi-tenancy vulnerabilities are well-documented. For organizations handling CUI, PHI, or financial data, shared infrastructure is a non-starter under most compliance frameworks.

Data Residency Uncertainty

Cloud providers route workloads across global regions for efficiency. Your data may be processed in Virginia, Ireland, or Singapore without your knowledge. CMMC requires CUI to remain within US boundaries. HIPAA requires demonstrable control over PHI processing locations. Cloud AI makes these guarantees difficult or impossible to verify.

No Air-Gap Option

CMMC Level 3, classified environments, and certain ITAR workloads require air-gapped processing with zero internet connectivity. Cloud AI is fundamentally incompatible with air-gapped requirements. If your compliance mandate requires physical network isolation, on-premise is the only path.

Our Solution

Hardened AI Infrastructure — Security From the Ground Up

Security Architecture — Defense in Depth for AI

We build AI infrastructure with the same security rigor we apply to every system we protect. Every layer — hardware, OS, network, application, and data — is hardened per NIST 800-53 controls and CIS benchmarks.

Encryption Everywhere
AES-256 encryption at rest (LUKS2 full-disk encryption), TLS 1.3 in transit, and encrypted model weights. Keyfiles stored in HSM or secure enclave, never on the AI server itself.
Access Control
Role-based access control (RBAC) with MFA enforcement. Least-privilege access to GPU resources, model APIs, and training data. Integration with your existing identity provider (AD, Azure AD, Okta).
Comprehensive Audit Logging
Every API call, model inference, data access, and administrative action is logged with timestamps, user identity, and full request/response metadata. Immutable log storage for forensic readiness.
Air-Gapped Deployment
Complete network isolation for classified and high-security environments. Model weights transferred via secure physical media. No internet connectivity required for inference or training operations.
Network Segmentation
AI infrastructure isolated in dedicated VLANs with firewall rules restricting traffic to authorized applications. No lateral movement paths from the AI environment to production systems.
Vulnerability Management
Continuous vulnerability scanning of AI infrastructure with automated patching workflows. OS, driver, and framework updates tested in staging before production deployment.
Compliance Matrix — Framework Coverage

Our secure AI infrastructure is designed to satisfy the technical requirements of every major regulatory framework out of the box.

Security Control HIPAA CMMC SOX PCI DSS NIST
Encryption at Rest (AES-256) ✓ ✓ ✓ ✓ ✓
Encryption in Transit (TLS 1.3) ✓ ✓ ✓ ✓ ✓
Role-Based Access Control ✓ ✓ ✓ ✓ ✓
Multi-Factor Authentication ✓ ✓ ✓ ✓ ✓
Comprehensive Audit Logging ✓ ✓ ✓ ✓ ✓
Network Segmentation ✓ ✓ — ✓ ✓
Air-Gapped Option — ✓ — — ✓
Vulnerability Management ✓ ✓ ✓ ✓ ✓
Data Sovereignty / Residency ✓ ✓ ✓ ✓ ✓
GPU Hardware — Right-Sized for Your Workload

We deploy NVIDIA enterprise GPU infrastructure sized to your actual requirements — not oversized to maximize a cloud vendor’s bill.

  • NVIDIA RTX 5090 (32 GB) — ideal for small teams (10–25 users), single-model inference, and fine-tuning workloads under 70B parameters
  • NVIDIA A100 (40/80 GB) — enterprise-grade for medium deployments (25–100 users), multi-model serving, and large-scale fine-tuning
  • NVIDIA H100 (80 GB) — maximum performance for large deployments (100+ users), real-time inference at scale, and training workloads exceeding 70B parameters
  • Multi-GPU clusters — scale horizontally across 2, 4, or 8 GPUs for enterprise workloads requiring massive throughput or concurrent model serving

We benchmark your actual workload on candidate hardware before purchase, ensuring you invest in the right configuration from day one.

FAQ

Frequently Asked Questions

What does a secure AI infrastructure deployment include?
A complete deployment includes GPU server hardware (procured to your specs), operating system installation and hardening, NVIDIA driver and CUDA configuration, inference engine deployment (vLLM, Ollama, or llama.cpp), full-disk encryption with LUKS2, RBAC configuration, audit logging, network segmentation, and integration with your identity provider. We handle everything from hardware procurement to production readiness.
Can the infrastructure be deployed in our existing data center?
Yes. We deploy into your existing data center, server room, or colocation facility. We work with your facilities team to verify power (GPU servers typically require 1,500–3,000W per unit), cooling, rack space, and network connectivity. For organizations without a data center, we can recommend and configure colocation options with appropriate security controls.
How much does secure AI infrastructure cost?
Entry-level deployments with a single RTX 5090 server start around $15,000–$25,000 for hardware plus setup and hardening. Enterprise deployments with A100 or H100 GPUs range from $50,000–$200,000+ depending on scale. Ongoing managed services are billed monthly. The total cost typically breaks even with cloud GPU pricing within 6–12 months while providing superior security and compliance.
Who manages the infrastructure after deployment?
We offer fully managed AI infrastructure as part of our managed IT and cybersecurity services. This includes hardware monitoring, OS and driver updates, security patching, model updates, performance optimization, and 24/7 support. Alternatively, we can train your internal team and provide consulting support on an as-needed basis.
Can the infrastructure be air-gapped?
Yes. We design and deploy fully air-gapped AI infrastructure for CMMC Level 3, classified environments, and organizations requiring zero internet exposure. Model weights are transferred via secure physical media. All software dependencies are pre-loaded. Updates and model refreshes are handled through controlled media transfer processes that maintain the air gap.

Related Services

Ready to Build AI Infrastructure You Can Trust?

Get a free AI infrastructure assessment. We’ll evaluate your compliance requirements, workload demands, and existing data center capacity — and deliver a deployment plan with hardware specs, costs, and a timeline.

No obligation • No data leaves your environment • Results in one week