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Who it's for:
- AI/ML Engineers and Developers
- Data Scientists
- Risk Management Professionals
- Compliance Officers and Auditors
- Business Leaders, Executives, and Project Managers
- Communications and Public Relations Professionals
AI Model Risk Management Framework
Release Date: 07/23/2024
- Benefits of a comprehensive AI risk management framework, including the more responsible use of AI, enhanced transparency, informed decision-making processes, and robust model validation
- Elements, benefits, and limitations of the four core components: AI model cards, data sheets, risk cards, and scenario planning
- How to combine the core components into a comprehensive AI risk management framework
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Acknowledgements
Josh Buker
Research Analyst, CSA
Chris Kirschke
Cloud Portfolio Information Security Officer at Albertsons Companies
Security Leader with over 20+ years of experience across Financial Services, Streaming, Retail and IT Services with a heavy focus on Cloud, DevSecOps and Threat Modeling. Advises multiple security startups on Product Strategy, Alliances and Integrations. Sits on multiple Customer Advisory Boards helping to drive security product roadmaps, integrations and feature developments. Avid hockey player, backpacker and wine collector in his spare t...
Jeffrey Ritter
Michael Roza
Risk, Control and Compliance Professional at EVC
Since 2012 Michael has contributed to over 100 CSA projects completed by CSA's Internet of Things, Zero Trust/Software-Defined Perimeter, Top Threats, Cloud Control Matrix, Containers/Microservices, DevSecOps, and other working groups. He has also served as co-chair of CSA's Enterprise Architecture, Top Threats, and Security-as-a-Service working groups while also serving as the Standards Liaison Officer for IoT, ICS, EA, SECaaS, and Cloud K...
Eric Tierling
Mark Yanalitis
Candy Alexander
Maria Schwenger
Renata Budko
Hadir Labib
Vani Mittal
Nick Ray
Interested in helping develop research with CSA?
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