Conclusion

The deployment of LLMs and generative AI systems requires an agile security approach to make sure that adopting generative AI is a business accelerant. For CISOs, addressing these risks requires a multi-layered approach to security, emphasizing robust input validation, continuous monitoring, modular system architecture, and enhanced data safeguarding techniques. By implementing these strategies, organizations can capitalize on the transformative potential of AI technologies while helping customers keep sensitive data secure, mitigate adversarial risks, and address regulatory compliance requirements.

About the authors

Matthew Schwartz is a Principal Security Engineer at Amazon specializing in generative AI security and risk management. With over 20 years of experience, he helps organizations implement strategic security frameworks that enable AI integration while maintaining compliance standards, leveraging his deep expertise in cloud computing and digital transformation to protect critical assets in an increasingly AI-driven landscape.

Mac Stevens is a Senior Solutions Architect with the AWS Public Sector Team. With a background as a security leader, he brings extensive experience in addressing emerging risks. Mac specializes in generative AI security, focusing on helping customers incorporate AI technologies while maintaining robust security measures. Mac is also one of the investigators for the NIST AI Safety & Security Institute. As a passionate builder, he loves identifying innovative ways to help customers apply technology to both business and security challenges.

Thank you to the following for their contributions: Paul Vixie, Jessica Kropf, Hart Rossman, Phillip Simpson, Mark Ryland, and Matt Saner.

Written with support from partners: Accenture, Arctic Wolf, Checkmarx, Check Point, Crowdstrike, Datadog, F5, Fortinet, Hidden Layer, Netskope, Orca, PwC, Query.ai, and Snyk.