AI Security Shared Responsibility Model
A comprehensive framework for understanding who's responsible for what when deploying AI systems. Maps security responsibilities across 8 deployment models and 16 security domains.
Why This Framework?
Your Day 1 framework for AI security - getting everyone on the same page before diving into technical specifications
Works across all cloud providers and deployment scenarios. Not tied to any specific vendor or platform.
Speaks business language before technical jargon. Perfect for getting organizational alignment.
Covers the full AI landscape from SaaS to on-premises, agentic systems to coding assistants.
Framework Comparison
This Framework
- • Day 1 organizational alignment
- • Vendor-agnostic approach
- • Business language first
- • Deployment-model focused
- • Covers emerging AI patterns
Other Frameworks
- • NIST AI RMF: Excellent for mature AI programs
- • CSA Models: Great for cloud-specific implementations
- • Microsoft's Approach: Comprehensive for Azure users
8 Deployment Models
From SaaS AI to on-premises, agentic systems to coding assistants - comprehensive coverage of all AI deployment scenarios
Examples:
Customer focuses on usage policies and data governance
Examples:
Shared responsibility for application and platform security
Examples:
Customer manages most security aspects except infrastructure
Examples:
Customer responsible for all security layers
Examples:
Device and application security critical
Examples:
Agent governance and oversight essential
Examples:
Code review and security validation required
Examples:
Context security and protocol implementation
Responsibility Matrix
Complete mapping of security responsibilities across all deployment models and security domains
Comprehensive responsibility matrix mapping Provider (P), Customer (C), and Shared (S) responsibilities across all AI deployment models
16 Security Domains
Traditional security areas plus emerging AI-specific domains for comprehensive coverage
Key Principles
Fundamental principles for understanding and applying the shared responsibility model
Even fully managed SaaS AI services require customer security efforts. You can't outsource all responsibility.
Example: Using ChatGPT? You're still responsible for data classification, usage policies, and user training.
More control over your AI deployment means more security obligations. The trade-off is flexibility vs. responsibility.
Example: On-premises AI gives you full control but requires managing all security layers.
When responsibility is shared, both parties must fulfill their parts. Communication and coordination are critical.
Example: Data security in PaaS requires provider encryption AND customer key management.
Agent governance and context pollution aren't future problems. They're current challenges requiring immediate attention.
Example: Agentic AI systems need governance frameworks today, not tomorrow.
Getting Started
Four simple steps to implement the AI Security Shared Responsibility Model in your organization
Use the deployment models guide to categorize your current and planned AI systems
Review the matrix to understand your obligations for each deployment model
Understand coverage areas and ensure all domains are addressed in your security program
Identify gaps and create action plans to address security responsibilities
Related Resources
Explore complementary frameworks and resources for comprehensive AI security
Start Implementing Today
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