Enterprise Security Framework

AI Shared Responsibility Model

Comprehensive guide to understanding security responsibilities between cloud providers and customers in AI deployments across Azure, AWS, Google Cloud, and Oracle Cloud platforms.

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Microsoft Azure

AWS

Google Cloud

Oracle Cloud

Understanding the AI Shared Responsibility Model

The AI shared responsibility model extends traditional cloud security frameworks to address the unique challenges of AI system security, compliance, and governance across different service models.

Infrastructure as a Service (IaaS)

Customer manages AI applications, runtime, middleware, and OS. Provider secures physical infrastructure, hypervisor, and network controls.

Maximum control and flexibility

Highest customer responsibility

Platform as a Service (PaaS)

Provider manages runtime, middleware, and OS. Customer responsible for AI applications, data, and access management.

Balanced responsibility model

Focus on AI development

Software as a Service (SaaS)

Provider manages entire stack including AI applications. Customer responsible for data, user access, and usage policies.

Minimal customer responsibility

Ready-to-use AI services

Responsibility Distribution Matrix

Component	IaaS	PaaS	SaaS
Data & Content	Customer	Customer	Customer
Identity & Access	Customer	Customer	Shared
AI Applications	Customer	Customer	Provider
Runtime & Middleware	Customer	Provider	Provider
Operating System	Customer	Provider	Provider
Physical Infrastructure	Provider	Provider	Provider

Provider-Specific Implementations

Each cloud provider implements the AI shared responsibility model differently, with unique services, security controls, and compliance frameworks.

Microsoft Azure AI

Azure's AI shared responsibility model covers three layers: AI platform, AI application, and AI usage, with built-in safety systems for services like Azure OpenAI Service.

Provider Responsibilities:

• Infrastructure security and compliance
• Built-in safety systems for PaaS/SaaS
• Model security and updates
• Platform-level monitoring

Customer Responsibilities:

• Data governance and privacy
• Access control and authentication
• Application-level security
• Responsible AI practices

Learn More About Azure AI Security

Amazon Web Services

AWS implements shared responsibility through services like Amazon Bedrock and SageMaker, with comprehensive security controls and compliance frameworks.

Provider Responsibilities:

• Foundation model security
• Infrastructure and network security
• Service-level encryption
• Compliance certifications

Customer Responsibilities:

• IAM policies and access control
• Data classification and protection
• Application security
• Guardrails and content filtering

Learn More About AWS AI Security

Google Cloud AI

Google Cloud's Vertex AI implements shared responsibility with focus on infrastructure security, platform compliance, and customer control over data and applications.

Provider Responsibilities:

• Infrastructure and platform security
• Compliance maintenance
• Service availability and updates
• Network security controls

Customer Responsibilities:

• Container and VM image security
• Access control management
• Data security and privacy
• Incident monitoring and response

Learn More About Google Cloud AI Security

Oracle Cloud Infrastructure

Oracle Cloud's AI services follow enterprise-grade shared responsibility model with emphasis ondatabase security and autonomous operations.

Provider Responsibilities:

• Autonomous security patching
• Infrastructure hardening
• Service-level security
• Compliance frameworks

Customer Responsibilities:

• Database and application security
• User access management
• Data encryption and privacy
• Security monitoring

Learn More About Oracle AI Security

Implementation Best Practices

Essential practices for organizations to effectively navigate the AI shared responsibility model and ensure comprehensive security coverage.

Governance & Oversight

Establish clear governance structures and oversight mechanisms for AI system deployment and management.

Define AI governance policies and procedures
Implement AI governance frameworks
Establish accountability structures
Regular compliance assessments

Security Controls

Implement comprehensive security controls across all layers of the AI technology stack.

Multi-layered security architecture
Zero-trust security model
Continuous security monitoring
Incident response procedures

Compliance Management

Ensure adherence to regulatory requirements and industry standards for AI systems.

Regulatory compliance mapping
Data protection and privacy controls
Audit trail maintenance
Regular compliance reviews

Real-World Implementation Cases

Learn from practical examples of organizations successfully implementing the AI shared responsibility model across different industries and use cases.

Financial Services

Enterprise AI Risk Management

Large financial institution implements comprehensive AI shared responsibility framework across multiple cloud providers for regulatory compliance.