ChaptersEventsBlog
Join CSA’s AI Safety Working Group kickoff—shape the future of secure, trustworthy AI.

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

Analyzing Log Data with AI Models

Analyzing Log Data with AI Models

Release Date: 09/15/2025

Logs are fundamental to Zero Trust. They capture critical details about user activity, device behavior, network traffic, and application access. However, when companies generate massive volumes of log data, manual review becomes unrealistic.

This publication explores how AI/ML can automate log...
The State of Cloud and AI Security 2025

The State of Cloud and AI Security 2025

Release Date: 09/09/2025

This global survey report, developed in partnership with Tenable, examines how organizations are adapting security strategies for hybrid, multi-cloud, and AI-driven environments. Drawing on insights from more than 1,000 professionals, it highlights the widening gap between rapid adoption and...
MLOps Overview

MLOps Overview

Release Date: 08/27/2025

Machine learning (ML) is becoming increasingly central to business operations, making the security of ML pipelines essential rather than optional. Machine Learning Operations (MLOps) is a set of repeatable processes to build, deploy, and continuously monitor machine learning models, focusing on...