Security and Resilience in AI Systems

“Powerful models demand equally strong protections.”

Why AI security is different

AI environments face unique risks that go beyond traditional application security:

• Leakage of sensitive data (PII, intellectual property, customer data)

• Model misuse, such as prompt injection and jailbreaks

• Attacks on inference APIs and quota exploitation

• Unexpected costs from GPU abuse or token consumption

• Dependency on critical infrastructure, where failures can halt business decisions

AI doesn’t run in isolation. It depends on secure, resilient, and auditable infrastructure. That’s your domain as an infrastructure professional.

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Security fundamentals for AI environments

Security pillar	Application in AI
Identity and Access	Who can access the model, data, and GPU
Data Protection	Encryption, DLP, classification, segregation
API Security	Authentication, rate limiting, WAF, monitoring
Secret Management	Keys, connections, and tokens stored securely
Governance and Auditability	Compliance, logging, traceability

💡 Security in AI isn’t just about firewalls. It’s about trust, traceability, and ethical use.

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Identity and access control

• Use Azure RBAC to control access to resources (VMs, AKS, Azure ML, Storage).

• Apply Managed Identities (UAMI) in pipelines and automated services.

• Adopt Microsoft Entra ID + Conditional Access + MFA for human authentication.

• Avoid static keys. Prefer federated identities (OIDC) and temporary tokens.

az ad sp create-for-rbac --name "ai-aks-service" --role contributor   --scopes /subscriptions/{id}/resourceGroups/rg-ai

Note: The command above creates a Service Principal. Prefer Managed Identity where supported to avoid long-lived credentials and reduce secret exposure.

Tip: Temporary and federated identities drastically reduce credential exposure risks.

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Secrets and key protection

Resource	Function	Best practice
Azure Key Vault	Secure storage for keys and secrets	Use RBAC and restrictive access
Managed Identity	Avoids credential exposure	Replaces static passwords in pipelines
API Tokens	Fine-grained control over usage and billing	Combine with rate limiting
Azure Policy	Governance for diagnostics and logging	Ensures active logging and compliance

az keyvault set-policy --name kv-ai --object-id <principalId> --secret-permissions get list

Note: Access policies are shown for illustration. Prefer RBAC-based access control where supported.

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Data and model protection

Action	Azure tool / service	Note
Encryption at rest	Storage Service Encryption (SSE)	Use customer-managed keys (CMK)
Encryption in transit	TLS 1.2+ and HTTPS	Enforce certificates on gateways
Data classification	Microsoft Purview	Identify PII and sensitive data
Environment segregation	VNets, NSGs, isolated workspaces	Separate dev, test, and prod
Model backups	Azure Backup, snapshots, Git	Include metadata and versioning

Never expose inference endpoints publicly without authentication. Use Private Endpoints and API Management for control, logging, and throttling.

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Model and inference security

Risk	Recommended mitigation
Prompt injection / jailbreaks	Input sanitization, filters, validation, and layered controls
Model misuse	Authentication and rate limiting
Model stealing	Limit requests per identity or token
GPU access abuse	RBAC plus taints and tolerations in AKS
Data leakage	Audit logs and anonymization of prompts and responses

💡 Conduct internal red teaming to test prompt and response vulnerabilities.

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Network protections

Resource	Recommended use
Private Endpoints	Private access to Azure OpenAI, Azure ML, and Storage
NSG + UDR	Restrict traffic in GPU subnets
Azure Firewall / WAF	Block injection and abuse patterns
API Management	Authentication, quotas, logging, centralized auditing
Front Door / Application Gateway	TLS termination, load balancing, health probes

az ml online-endpoint update   --name my-endpoint   --resource-group rg-ai   --set public_network_access=disabled

Allow access only via VNet with Private Link and properly configured firewalls.

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Resilience. Designing for high availability

Strategies for inference workloads and critical pipelines:

• Availability zones: Deploy across zones and regions.

• Load balancing: Use Front Door or Application Gateway.

• Intelligent autoscaling: Based on GPU usage, latency, or queues.

• Health probes and auto-restart: For AKS pods and critical services.

• Retry and fallback: Use alternate models or cached responses.

• Disaster recovery: Replicate data and models to secondary regions.

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Production lessons (real cases)

❌ Pod froze after 200 requests without readiness probe → ✅ Fix: Add health checks and auto-restart ❌ Key Vault token expired and blocked pipeline → ✅ Fix: Use Managed Identity with automatic rotation ❌ Logs captured customer prompts → ✅ Fix: Mask and anonymize logs

Test your incidents before they happen. Resilience is built before failure.

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Security and resilience checklist

Item	Status
Managed identities, no static keys	✅
Models and data encrypted	✅
API rate limiting and authentication	✅
Centralized logging and auditing	✅
Private VNet deployment with NSGs	✅
Prompt injection and abuse testing	✅
Model backup and versioning	✅
Disaster recovery strategy defined	✅

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Conclusion

Security and resilience are what sustain AI in production. Without them, even the most advanced model can become a liability.

You don’t need to understand every layer of the model to be essential in AI. You must ensure it operates securely, efficiently, and continuously.