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Who it's for:
- System engineers and architects
- Privacy and security professionals
Securing LLM Backed Systems: Essential Authorization Practices
Release Date: 08/13/2024
Organizations are increasingly leveraging Large Language Models (LLMs) to tackle diverse business problems. Both existing companies and a crop of new startups are vying for first-mover advantages. With this mass adoption of LLM-backed systems, there is a critical need for formal guidance on the secure design of them. Organizations especially need this guidance when an LLM must make decisions or utilize external data sources.
This document by the CSA AI Technology and Risk Working Group describes the LLM security risks relevant to system design. After exploring the pitfalls in authorization and security, it also outlines LLM design patterns for extending the capabilities of these systems. System designers can use this guidance to build systems that utilize the powerful flexibility of AI while remaining secure.
Key Takeaways:
- LLM security measures and best practices
- Authorization considerations for the various components of LLM-backed systems
- Security challenges and considerations for LLM-backed systems
- Common architecture design patterns for LLM-backed systems
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Acknowledgements
Michael Roza
Risk, Audit, Control and Compliance Professional at EVC
Michael Roza
Risk, Audit, Control and Compliance Professional at EVC
Michael Roza is a seasoned risk, audit, control and compliance, and cybersecurity professional with over 20 years of experience across multinational enterprises and startups. As a Cloud Security Alliance (CSA) Research member for over 10 years, he has led and contributed to more than 140 CSA projects spanning Zero Trust, AI, IoT, Top Threats, DecSecOps, Cloud Key Management, Cloud Control Matrix, and many others.
He has co-chaired...
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