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CSA Research

Best practices, guidance, frameworks and tools to help the industry secure the cloud. Read our research to get your questions around cloud security answered.
Research

CSA Research is created by the industry for the industry and is both vendor-neutral and consensus driven. Our research is created by subject matter experts who volunteer for our working groups. Each working group focuses on a unique topic or aspect of cloud security, from IoT, DevSecOps, Serverless and more, we have working groups for over 20 areas of cloud computing. You can view a list of all active research working groups. To find out more about how our research is created and the process we follow you can view the CSA Research Lifecycle.

Contribute to CSA Research

Peer reviews allow security professionals from around the world to collaborate on CSA research. Provide your feedback on the following documents in progress.

Latest Research

AI Controls Matrix v1.1

AI Controls Matrix v1.1

Release Date: 06/22/2026

The AI Controls Matrix (AICM) is a first-of-its-kind vendor-agnostic framework for cloud-based AI systems. Organizations can use the AICM to develop, implement, and operate AI technologies in a secure and responsible manner. Developed by industry experts, the AICM builds on CSA’s Cloud Controls...
AICMv1.1 Implementation Guidelines for Cloud Service Providers (CSP)

AICMv1.1 Implementation Guidelines for Cloud Service Providers (CSP)

Release Date: 06/22/2026

Cloud Service Provider (CSP): Delivers the underlying cloud infrastructure that hosts and supports AI systems and workloads, and is responsible for designing, developing, implementing, and enforcing controls to mitigate security, privacy, and compliance risks in the cloud services they provide.

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AICMv1.1 Auditing Guidelines for Orchestrated Service Providers (OSP)

AICMv1.1 Auditing Guidelines for Orchestrated Service Providers (OSP)

Release Date: 06/22/2026

Orchestrated Service Provider (OSP): Provides AI platforms and orchestration layers that integrate and govern models in enterprise environments, and is responsible for implementing controls to mitigate security, privacy, and compliance risks associated with LLM/genAI technologies.

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