{"id":30085,"date":"2023-10-31T08:00:00","date_gmt":"2023-10-31T15:00:00","guid":{"rendered":"https:\/\/azure.microsoft.com\/en-us\/blog\/?p=30085"},"modified":"2025-06-12T09:14:11","modified_gmt":"2025-06-12T16:14:11","slug":"whats-new-in-data-ai-prioritize-ai-safety-to-scale-with-confidence","status":"publish","type":"post","link":"https:\/\/azure.microsoft.com\/en-us\/blog\/whats-new-in-data-ai-prioritize-ai-safety-to-scale-with-confidence\/","title":{"rendered":"What\u2019s new in Data & AI: Prioritize AI safety to scale with confidence"},"content":{"rendered":"\n

A few months ago, I had the opportunity to speak to some of our partners about what we\u2019re bringing to market with Azure AI<\/a>. It was a fast-paced hour and the session was nearly done when someone raised their hand and acknowledged that there was no questioning the business value and opportunities ahead\u2014but what they really wanted to hear more about was Responsible AI<\/a> and AI safety.<\/p>\n\n\n\n

This stays with me because it shows how top of mind this is as we move further in the era of AI with the launch of powerful tools that advance humankind\u2019s critical thinking and creative expression. That partner question reminded me of the importance that AI systems are responsible by design. <\/em>This means the development and deployment of AI must be guided by a responsible framework from the very beginning. It can\u2019t be an afterthought.<\/p>\n\n\n\n

Our investment in responsible AI innovation goes beyond principles, beliefs, and best practices. We also invest heavily in purpose-built tools that support responsible AI across our products. Through Azure AI tooling, we can help scientists and developers alike build, evaluate, deploy, and monitor their applications for responsible AI outcomes like fairness, privacy, and explainability. We know what a privilege it is for customers to place their trust in Microsoft.<\/p>\n\n\n\n

Brad Smith authored a blog<\/a> about this transformative moment we find ourselves in as AI models continue to advance. In it, he shared about Microsoft\u2019s investments and journey as a company to build a responsible AI foundation. This began in earnest with the creation of AI principles<\/a> in 2018 built on a foundation of transparency and accountability. We quickly realized principles are essential, but they aren\u2019t self-executing. A colleague described it best by saying “principles don\u2019t write code.” This is why we operationalize those principles with tools and best practices and help our customers do the same. In 2022, we shared our internal playbook for responsible AI development, our Responsible AI Standard<\/a>, to invite public feedback and provide a framework that could help others get started. <\/p>\n\n\n\n

I\u2019m proud of the work Microsoft has done in this space. As the landscape of AI evolves rapidly and new technologies emerge, safe and responsible AI will continue to be a top priority. To echo Brad, we\u2019ll approach whatever comes next with humility, listening, and sharing our learning along the way.<\/p>\n\n\n\n

In fact, our recent commitment to customers<\/a> for our first-party copilots is a direct reflection of that. Microsoft will stand behind our customers from a legal perspective if copyright infringement lawsuits are brought forward from using our Copilot, putting that commitment to responsible AI innovation into action.<\/p>\n\n\n\n

In this month\u2019s blog, I\u2019ll focus on a few things. How we\u2019re helping customers operationalize responsible AI with purpose-built tools. A few products and updates are designed to empower our customers with confidence so they can innovate safely with our trusted platform. I\u2019ll also share the latest on what we\u2019re delivering for organizations to prepare their data estates to go forward and succeed in the era of AI. Finally, I\u2019ll highlight some fresh stories of organizations putting Azure to work. Let\u2019s dive in!<\/p>\n\n\n\n

Availability of Azure AI Content Safety delivers better online experiences<\/h2>\n\n\n\n

One of my favorite innovations that reflects the constant collaboration between research, policy, and engineering teams at Microsoft is Azure AI Content Safety. This month we announced the availability of Azure AI Content Safety<\/a>, a state-of-the-art AI system to help keep user-generated and AI-generated content safe, ultimately creating better online experiences for everyone.<\/p>\n\n\n\n

The blog shares the story of how South Australia\u2019s Department of Education is using this solution to protect students<\/a> from harmful or inappropriate content with their new, AI-powered chatbot, EdChat. The chatbot has safety features built in to block inappropriate queries and harmful responses, allowing teachers to focus on the educational benefits rather than control oversight. It\u2019s fantastic to see this solution at work helping create safer online environments!<\/p>\n\n\n\n

As organizations look to deepen their generative AI investments, many are concerned about trust, data privacy, and the safety and security of AI models and systems. That\u2019s where Azure AI can help. With Azure AI, organizations can build the next generation of AI applications safely by seamlessly integrating responsible AI tools and practices developed through years of AI research, policy, and engineering.<\/p>\n\n\n\n

All of this is built on Azure\u2019s enterprise-grade foundation for data privacy, security, and compliance, so organizations can confidently scale AI while managing risk and reinforcing transparency. Microsoft even relies on Azure AI Content Safety to help protect users of our own AI-powered products. It\u2019s the same technology helping us responsibly release large language models-based experiences in products like GitHub Copilot, Microsoft Copilot, and Azure OpenAI Service, which all have safety systems built in.<\/p>\n\n\n

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Azure AI<\/h2>\n\n\t\t\t\t\t
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Create innovative AI solutions<\/p>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tGet started<\/span>\n\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\n\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n

New model availability and fine-tuning for Azure OpenAI Service models<\/h2>\n\n\n\n

This month, we shared two new base inference models (Babbage-002 and Davinci-002) that are now generally available, and fine-tuning capabilities for three models (Babbage-002, Davinci-002, and GPT-3.5-Turbo) are in public preview. Fine-tuning is one of the methods available to developers and data scientists who want to customize large language models for specific tasks.<\/p>\n\n\n\n

Since we launched Azure OpenAI, it\u2019s been amazing to see the power of generative AI applied to new applications! Now it\u2019s possible to customize your favorite OpenAI models<\/a> for completion use cases using the latest base inference models to solve your specific challenges and easily and securely deploy those new custom models on Azure.<\/p>\n\n\n\n

One way that developers and data scientists can adapt large language models for specific tasks is fine tuning. Unlike methods like Retrieval Augmented Generation (RAG) and prompt engineering that work by adding information and instructions to prompts, fine tuning works by modifying the large language model itself.<\/p>\n\n\n\n

With Azure OpenAI Service<\/a> and Azure Machine Learning, you can use Supervised Fine Tuning, which lets you provide custom data (prompt\/completion or conversational chat, depending on the model) to teach new skills to the base model.<\/p>\n\n\n\n

We suggest companies begin with prompt engineering or RAG to set up a baseline before they embark on fine-tuning\u2014it\u2019s the quickest way to get started, and we make it simple with tools like Prompt Flow<\/a> or On Your Data<\/a>. By starting with prompt engineering and RAG, developers establish a baseline to compare against, so your effort is not wasted.<\/p>\n\n\n

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Recent news from Azure Data and AI<\/h2>\n\n\n\n

We\u2019re constantly rolling out new solutions to help customers maximize their data and successfully put AI to work for their businesses. Here are some product announcements from the past month:<\/p>\n\n\n\n