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AI Shared Responsibility Model: Deployer vs. Provider
This episode of the ISACA Advanced in AI Security Management (AAISM) exam prep series introduces the AI shared responsibility model — the framework that splits duties between the organization that builds an AI service and the organization that uses it. It explains why this division mirrors the familiar cloud model, how the contract makes responsibilities enforceable, and the broad categories of work that fall to each party when something inevitably goes wrong with a bought-in AI system.
What this episode covers
- Why the AI shared responsibility model parallels the cloud model and must live inside the contract, not on a slide.
- The first set of deployer duties covering auditing, data governance, identity and access management, and AI-specific incident response.
- The second set of deployer duties covering model validation, supply chain risk, and end-user transparency.
- The first set of provider duties covering compliance certifications, data security, and the provider’s incident response.
- The second set of provider duties covering responsible model development, transparency tooling, and vulnerability management.
- How contract clauses for service levels, notifications, audit rights, and disclosures stop duties from slipping into the gap.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What is the AI shared responsibility model?
Just as cloud computing has a well-known shared responsibility model, AI has one too. When an organization uses AI services from a provider, both sides must clearly understand and divide the duties between them, and those responsibilities must be addressed contractually with clear expectations for service levels, incident response, and incident notification.
What duties does the AI deployer own?
The deployer regularly audits and tests privacy and security controls including penetration testing, implements strong data governance covering quality, integrity, privacy, and provenance, enforces strong identity and access management, builds and tests an AI-specific incident response plan, validates models including adversarial testing for bias and vulnerabilities, manages supply chain risk through vendor vetting and contract terms, and ensures transparency to users with explainable models and output monitoring.
What duties does the AI provider own?
The provider holds relevant compliance and audit certifications against security and privacy standards, protects training data, model weights, parameters, logs, and inference artifacts, runs a robust incident response plan coordinated with subscribers, develops models responsibly with an ethics framework, secure coding, and bias-reduced training data, provides transparency and explainability tools and documentation, and manages vulnerabilities proactively.
Why must AI shared responsibilities be addressed contractually?
Confusion only arises when nobody wrote down who fixes what, which is why these responsibilities must be addressed contractually, with clear expectations for service levels, incident response, and incident notification. Without that contract clarity, critical duties can fall into the gap between the provider and the deployer.
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Reference: This article is based on concepts discussed in AI Shared Responsibility Model: Deployer vs. Provider.