Lilly Tech Systems
Security for Hybrid Cloud AI
Implement unified identity management, consistent encryption, compliance controls, and zero-trust security across on-premises and cloud AI environments.
Unified Identity and Access
The most critical security challenge in hybrid AI is managing identities consistently across environments. Data scientists need seamless access to resources in both on-premises and cloud, but with the same access controls enforced everywhere.
Identity Federation
Federate on-premises Active Directory with cloud IAM (AWS IAM Identity Center, Azure AD, GCP Workforce Identity). Single sign-on for all AI platforms.
Service Accounts
Use workload identity federation for service-to-service authentication. ML pipelines authenticate to cloud APIs without storing long-lived credentials on-premises.
RBAC Consistency
Define roles (data scientist, ML engineer, admin) once and map them consistently to both on-premises and cloud permissions.
Encryption Strategy
| Layer | On-Premises | In Transit | Cloud |
|---|---|---|---|
| Training Data | LUKS / BitLocker | TLS 1.3 | SSE-KMS |
| Model Artifacts | Application-level encryption | TLS 1.3 | SSE-KMS |
| Inference I/O | TLS termination | mTLS | TLS termination |
| Key Management | HSM (on-prem) | Key wrapping | Cloud KMS |
Zero-Trust for AI Workloads
Verify Every Request
Authenticate and authorize every API call between AI services, whether on-premises or in cloud. Never trust based on network location alone.
Least Privilege
Training jobs get read access to training data and write access to model output only. Inference services get read access to models only. No broad permissions.
Micro-Segmentation
Isolate AI workloads in dedicated network segments. Training, inference, and data processing each get their own security boundary.
Continuous Monitoring
Log all data access, model downloads, and API calls. Use SIEM tools to detect anomalous patterns like unusual data exfiltration or unauthorized model access.
Compliance Considerations
- Data residency: Ensure training data stays in approved jurisdictions. Use sync policies that filter by data classification before cross-border transfer.
- Model governance: Track model lineage across environments. Record which data trained which model and where it was deployed.
- Audit logging: Centralize audit logs from both environments. Ensure logs are tamper-proof and retained per regulatory requirements.