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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. In this post, we discuss how to address these challenges holistically.

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Linking ESG Programs to Corporate Financial Performance: An Econometric Analysis Approach

CSM Magazine

As businesses increasingly prioritize the incorporation of environmental, social, and governance (ESG) initiatives into their daily operations, many executives are rightfully pondering not only the moral implications of responsible ESG practices but – perhaps more importantly – how to quantify their impact on corporate financial performance (CFP).

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Revolutionizing Communication: Unleashing the Power of the Best Artificial Intelligence Chatbots

SmartKarrot

Here are some best practices to ensure that your AI chatbot communication is secure and private: Data Encryption: Ensure that all data transmitted between your AI chatbot and users is encrypted using industry-standard encryption protocols.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

Consider your security posture, governance, and operational excellence when assessing overall readiness to develop generative AI with LLMs and your organizational resiliency to any potential impacts. Many AWS customers align to industry standard frameworks, such as the NIST Cybersecurity Framework.