Advanced

Multi-Cloud AI Security

Many organizations use AI services across multiple cloud providers. Managing identity, policy, data residency, and compliance across clouds requires careful architectural planning.

Cross-Cloud Identity Federation

  1. Centralized Identity Provider

    Use a single identity provider (Okta, Azure AD, Google Workspace) as the source of truth for all cloud identities. Federate this IdP with AWS IAM, GCP IAM, and Azure AD to provide consistent identity across all AI workloads.

  2. Workload Identity Across Clouds

    Use OIDC federation to enable workloads in one cloud to access resources in another without long-lived credentials. AWS roles can trust GCP service accounts, and vice versa, using workload identity pools.

  3. Consistent RBAC Mapping

    Define a common role taxonomy (ML Engineer, Data Scientist, ML Ops, Auditor) and map it consistently to IAM roles on each cloud. This ensures permissions are equivalent regardless of which cloud hosts the AI workload.

  4. Session Management

    Enforce short-lived sessions (1 hour maximum) for cross-cloud access. Require re-authentication for sensitive operations like model deployment or training data access across cloud boundaries.

Unified Policy Management

Tool Capability Multi-Cloud Support
Open Policy Agent Policy-as-code for authorization decisions across services Cloud-agnostic, runs anywhere
HashiCorp Sentinel Policy enforcement for Terraform-managed infrastructure AWS, GCP, Azure via Terraform
Cloud Custodian Rules engine for cloud resource compliance and governance AWS, GCP, Azure native support
Prisma Cloud CSPM, CWPP, and compliance across clouds Comprehensive multi-cloud coverage

Data Residency and Sovereignty

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Regulatory Complexity: AI workloads processing personal data must comply with data residency requirements (GDPR, data localization laws). When using AI services across clouds and regions, ensure training data, model artifacts, and inference requests stay within permitted jurisdictions.
  • Data classification: Tag all ML datasets with data residency requirements. Enforce region restrictions through IAM policies and service configurations on each cloud
  • Cross-border transfers: When training requires data from multiple regions, use approved transfer mechanisms (Standard Contractual Clauses, adequacy decisions) and document the legal basis
  • Model as data: Trained models may contain representations of personal data. Apply the same residency controls to model artifacts as you would to training data
  • Inference routing: Route inference requests to endpoints in the same region as the data subject to avoid cross-border data transfers for real-time predictions

Multi-Cloud Governance

Centralized Logging

Aggregate audit logs from all clouds into a single SIEM (Splunk, Elastic, Sentinel). Normalize log formats to enable cross-cloud correlation and unified alerting.

Cost Governance

Implement cross-cloud cost monitoring with alerts for GPU spending anomalies. A compromised credential on any cloud can lead to massive compute bills within hours.

Compliance Mapping

Maintain a unified compliance matrix that maps regulatory requirements to controls on each cloud. Automate compliance checking with tools like Cloud Custodian or Prisma Cloud.

Incident Response

Develop cloud-specific playbooks within a unified incident response framework. Ensure your team can respond to security incidents on any cloud with the same level of proficiency.

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Next Up: In the final lesson, we bring together all cloud AI security concepts into a comprehensive best practices checklist and production hardening guide.