What Security Controls to Audit in an AI Infrastructure Platform

Quick Answer: Auditing security in an AI infrastructure platform requires verifying five control domains: encryption (at rest, in transit, and in use), identity and access management, network isolation, compliance certifications, and incident response capabilities. Each domain has specific artifacts you can request and tests you can run before entrusting a provider with sensitive training data and model IP.

AI infrastructure platforms handle data that traditional cloud security frameworks were not designed to protect: multi-gigabyte training datasets, proprietary model weights, checkpoint files containing learned parameters, and inference logs that may reveal query patterns. A provider that passes a generic cloud security audit may still leave these AI-specific assets exposed.

This article walks through the five security control domains that matter most for AI infrastructure, what to verify in each, and how to evaluate the provider's shared responsibility model so you know which controls you own and which the provider manages.

Why AI Infrastructure Security Requires a Dedicated Audit

AI infrastructure security is the set of controls that protect training data, model artifacts, and inference workloads throughout their lifecycle on a compute platform managed by a third-party provider. It extends beyond traditional cloud security because AI workloads create unique risk surfaces: large data transfers that stress encryption pipelines, long-running training sessions that expand the attack window, and model artifacts that represent months of proprietary research condensed into portable files.

A standard SOC 2 or ISO 27001 certification tells you that the provider follows recognized security practices. It does not tell you whether those practices adequately cover AI-specific threats like model extraction attacks, training data poisoning, or checkpoint file exfiltration. The audit framework below bridges that gap by focusing on controls that directly protect AI assets.

For teams evaluating a private AI infrastructure provider, the security audit should confirm that isolation boundaries, access controls, and monitoring systems are designed for the scale and sensitivity of AI workloads, not just general-purpose cloud computing.

Domain 1: Encryption Across the Data Lifecycle

AI infrastructure handles data in three states: at rest (training datasets and checkpoints in storage), in transit (data moving between storage and GPU nodes), and in use (data loaded into GPU memory during training and inference). Each state requires distinct encryption controls.

What to Verify

Request the provider's encryption architecture document. It should specify the encryption algorithms used for each data state, key management procedures, and key rotation policies. For data at rest, verify that storage volumes use AES-256 or equivalent. For data in transit, verify that inter-node communication uses TLS or IPsec, and that InfiniBand fabric traffic is protected through IPoIB encryption or equivalent.

For data in use, ask whether the provider supports confidential computing technologies like NVIDIA Confidential Computing (using H100 Confidential Computing mode) or AMD SEV-SNP. These technologies encrypt data in GPU memory, protecting model weights and training data from hypervisor-level access. Not all AI infrastructure providers support this, and its absence is not necessarily a disqualifier, but it should factor into your risk assessment for highly sensitive workloads.

Key Management

Verify who controls the encryption keys. A provider-managed key model is simpler but gives the provider access to your decrypted data. A customer-managed key model (BYOK or HYOK) gives you control but adds operational complexity. For regulated workloads, customer-managed keys are often a requirement rather than a preference.

Domain 2: Identity and Access Management

AI infrastructure platforms typically serve multiple teams within an enterprise: data scientists, ML engineers, platform engineers, and compliance officers. Each role needs different access levels. A security audit should verify that the provider's IAM system supports granular, role-based access control with audit logging.

What to Verify

Check whether the provider supports the following IAM capabilities:

• Role-based access control: Can you define roles that restrict access to specific GPU pools, storage volumes, and model registries? This prevents a data scientist on one project from accessing another project's training data.
• MFA enforcement: Is multi-factor authentication required for all administrative access? AI infrastructure with single-factor admin access is a significant risk for credential theft.
• Audit logging: Does the platform log all access events, including who accessed which model artifacts and when? Audit logs are essential for post-incident forensics and compliance evidence.
• Service account management: Can you create service accounts for automated training pipelines with scoped permissions? Shared credentials for automation are a common security gap in AI platforms.

Request sample audit logs and verify that they capture the level of detail your security team needs. Logs that show "user accessed storage" without specifying which files or volumes are insufficient for AI workload forensics.

Domain 3: Network Isolation and Traffic Controls

AI training workloads generate significant network traffic between GPU nodes, storage systems, and external data sources. Network isolation controls ensure that this traffic stays within approved paths and cannot be intercepted or redirected by other tenants or external actors.

What to Verify

Request the network security architecture diagram. It should show how tenant traffic is isolated, how GPU cluster traffic is separated from management traffic, and how external access (APIs, data ingress) is controlled.

For teams with strict data residency requirements, verify that network traffic does not transit regions outside your compliance scope. A provider with U.S.-based AI infrastructure should confirm that all traffic, including management and backup traffic, stays within U.S. data centers.

Domain 4: Compliance Certifications and Scope

Compliance certifications provide third-party validation of a provider's security controls. However, the scope of certification matters as much as the certification itself. A provider may hold SOC 2 Type II certification for its general cloud platform but exclude its AI-specific services from the audit scope.

What to Verify

Request the provider's compliance attestation reports and check the scope carefully. Verify that the following are included in the audit scope:

• AI compute services: GPU cluster management, workload scheduling, and model deployment services should be in scope, not just general compute.
• Storage services: Object storage, file storage, and model registries should be in scope, since these hold training data and model artifacts.
• Networking services: The fabric connecting GPU nodes and the external-facing APIs should be in scope.
• Management plane: The admin interfaces and orchestration platforms should be in scope, since they control access to all other services.

For healthcare AI workloads, verify HIPAA readiness. The provider should offer a Business Associate Agreement (BAA) and demonstrate that its infrastructure supports HIPAA-required safeguards. Look for HIPAA-ready language rather than guaranteed compliance claims, since HIPAA compliance is a shared responsibility.

For financial services workloads, check for SOC 2 Type II and, where applicable, PCI DSS scope. Ask whether the provider has undergone penetration testing specifically targeting AI infrastructure components, not just general web applications.

Domain 5: Incident Response and Security Monitoring

Security incidents in AI infrastructure can have different characteristics than traditional cloud incidents. A model extraction attack may look like normal inference API usage until you analyze query patterns. A training data leak may occur through checkpoint file access rather than direct database exfiltration. The provider's incident response process should account for these AI-specific threat patterns.

What to Verify

Request the provider's security incident response plan and verify the following:

• Detection capabilities: Does the provider monitor for AI-specific threats like abnormal inference query volumes, unauthorized model artifact access, or unusual data egress patterns? Generic cloud monitoring may miss these signals.
• Response timelines: What are the defined response times for security incidents? Critical security incidents should be acknowledged within 15 minutes and have a defined containment procedure.
• Customer notification: How and when will the provider notify you of a security incident affecting your data or workloads? The notification timeline should be defined in the contract, not left to the provider's discretion.
• Forensic support: Does the provider preserve logs and system images for post-incident forensics? Without preserved evidence, determining the scope of a breach is difficult.

Ask for a sample post-incident report from a past security event. If the provider has never had a security incident, ask about their tabletop exercise results. A provider that has never tested its incident response process is a higher risk than one that has identified and addressed gaps through simulation.

Shared Responsibility Model for AI Security

Security in AI infrastructure is a shared responsibility. The provider secures the underlying infrastructure, but you remain responsible for how you use it. Understanding where the provider's responsibility ends and yours begins prevents gaps in your security posture.

Review the provider's shared responsibility documentation and confirm that it explicitly addresses AI workloads. Generic cloud shared responsibility models may not cover AI-specific scenarios like model artifact protection or training data isolation.

FAQ

What security controls should I audit in an AI infrastructure provider?

Audit five domains: encryption (at rest, in transit, and in use), identity and access management, network isolation, compliance certifications, and incident response. Each domain has specific artifacts to request, including encryption architecture documents, IAM capability lists, network security diagrams, compliance attestation reports, and incident response plans.

How is AI infrastructure security different from general cloud security?

AI infrastructure handles unique assets like multi-gigabyte training datasets, proprietary model weights, and checkpoint files that traditional cloud security frameworks do not specifically address. AI workloads also create longer attack windows due to multi-week training sessions and expose new threat surfaces like model extraction and training data poisoning.

What compliance certifications should an AI infrastructure provider hold?

At minimum, SOC 2 Type II and ISO 27001. For healthcare workloads, HIPAA readiness with a BAA. For financial services, SOC 2 and potentially PCI DSS scope. The critical step is verifying that AI-specific services (GPU compute, model storage, AI APIs) are included in the certification scope, not just general cloud services.

Does AI infrastructure require confidential computing?

Confidential computing (encrypting data in GPU memory) is not required for all workloads, but it adds protection for highly sensitive model weights and training data. Providers that support NVIDIA Confidential Computing or AMD SEV-SNP offer stronger guarantees against hypervisor-level access. Evaluate the need based on your data sensitivity and regulatory requirements.

Who is responsible for security in a managed AI infrastructure environment?

Responsibility is shared. The provider secures the infrastructure: physical datacenters, network isolation, storage encryption, and platform-level access control. You are responsible for workload-level security: role definitions, data classification, compliance execution, and workload monitoring. Review the provider's shared responsibility model to confirm it covers AI-specific scenarios.

How do I test an AI infrastructure provider's security before committing?

Request the security documentation for all five domains, review compliance attestation reports with attention to scope, ask for sample audit logs and post-incident reports, and conduct a security architecture review with the provider's team. If possible, run a controlled penetration test during a proof-of-concept period to validate the controls in practice.

Summary

Auditing security in an AI infrastructure platform requires evaluating five control domains: encryption across the data lifecycle, identity and access management, network isolation, compliance certifications with verified scope, and incident response capabilities. Each domain has specific artifacts to request and tests to run before committing sensitive AI workloads to a provider.

The shared responsibility model defines where the provider's obligations end and yours begin. Review this model carefully to ensure it covers AI-specific scenarios like model artifact protection, training data isolation, and AI workload monitoring. Providers that cannot produce security documentation tailored to AI workloads may not be ready to protect your most sensitive assets.

Next step: Explore OneSource Cloud's secure AI infrastructure architecture to see how encryption, access control, network isolation, and compliance controls are designed for enterprise AI workloads.