15
CONCLUSION
Next steps
AWS is committed to helping you build generative AI
applications that grow your business while helping you
meet your security, privacy, and compliance goals.
We stand firm in our belief that generative AI applications can be securely
designed, developed, and operated. We also acknowledge the validity of
security and privacy concerns about these technologies. Generative AI raises
new challenges in defining, measuring, and mitigating issues around data
privacy, IP, legislative oversight, equality, and transparency.
With the introduction of new products, the growing complexity and scale of
solutions, new training parameters, and ever-growing datasets, generative
AI security will become even more essential in the days ahead. By developing
an effective and comprehensive security strategy for generative AI workloads
now, you can maximize your competitive advantage—and stand prepared for
the rapidly approaching future.
The good news: The basic controls needed to securely design, develop, and
run generative AI applications have been in place for years—and are aligned
with trusted, proven principles of cloud security, such as those found in the
AWS Well-Architected Framework.
By exploring the practices outlined in this eBook, you've already taken your
first step toward securing your generative AI workloads.
Now, take the next step with AWS. We can provide you with deep insights
and specific guidance needed to stay up to speed on emerging topics, think
through your unique challenges, and unlock the full benefits of generative
AI—all while protecting your data, your customers, and your business.
Learn more about generative AI on AWS ›
Get started quickly with Amazon Bedrock ›
Build and customize FMs on Amazon SageMaker ›
Elevate your security in the cloud with AWS ›
Transform responsible AI from theory into practice ›
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