Building a foundation for AI success: Governance

This is the last post in our six-part blog series. See part one, part two, part three, part four, part five, and download the white paper.

To date, this series has explored four of the five drivers of AI readiness: business strategy, technology and data strategy, AI strategy and experience, and organization and culture. Each is critical to an organization’s ability to use AI to deliver value to the business, whether it’s related to productivity enhancements, customer experience, revenue generation, or net-new innovation. But nothing is ultimately more important than AI governance, which includes the processes, controls, and accountability structures needed to govern data privacy, data governance, security, and responsible development and use of AI in an organization.   

“We recognize that trust is not a given but earned through action,” said Microsoft Vice Chair and President Brad Smith. “That’s precisely why we are so focused on implementing our Microsoft responsible AI principles and practices—not just for ourselves, but also to equip our customers and partners to do the same.” 

In that spirit, we have collected a set of resources that encompass best practices for AI governance, focusing on security, privacy and data governance, and responsible AI. 

Security

Just as AI enables new opportunities, it also introduces new imperatives to manage risk, whether related specifically to AI usage, app and data protection, compliance with organizational and legal policies, or threat detection. The Microsoft Security Blog includes a set of resources to help you modernize security operations, empower security professionals, and learn best practices to mitigate and manage risk more effectively.  

One of the first steps you can take is to understand how AI is being used in the organization so you can make informed decisions and implement the appropriate controls. This post lays out the primary concerns leaders have about implementing AI, as well as a set of recommendations on how to discover, protect, and govern AI usage. 

For example, you may have heard of (or already be implementing) red teaming. Red teaming, according to this post by the Microsoft AI Red Team, “broadly refers to the practice of emulating real-world adversaries and their tools, tactics, and procedures to identify risks, uncover blind spots, validate assumptions, and improve the overall security posture of systems.” The post shares additional education, guidance, and resources to help your organization apply this best practice to your AI systems. 

Microsoft’s holistic approach to generative AI security considers the technology, its users, and society at large across four areas of protection: data privacy and ownership, transparency and accountability, user guidance and policy, and secure by design. For more on how Microsoft secures generative AI, download Securing AI guidance.  

Privacy and data governance

Building trust in AI requires a strong privacy and data governance foundation. As our Chief Privacy Officer Julie Brill has said, “At Microsoft we want to empower our customers to harness the full potential of new technologies like artificial intelligence, while meeting their privacy needs and expectations.” Enhancing trust and protecting privacy in the AI era, originally posted on the Microsoft on the Issues Blog, describes our approach to data privacy, focusing on topics such as data security, transparency, and data protection user controls. It also includes a set of resources to help you dig deeper into our approaches to privacy issues and share what we are learning. 
 

Data governance refers to the processes, policies, roles, metrics, and standards that enable secure, private, accurate, and usable data throughout its life cycle. It’s vital to your organization’s ability to manage risk, build trust, and promote successful business outcomes. It is also the foundation for data management practices that reduce the risk of data leakage or misuse of confidential or sensitive information such as business plans, financial records, trade secrets, and other business-critical assets. This post shares Microsoft’s approach to data security and compliance so you can learn more about how to safely and confidently adopt AI technologies and keep your most important asset—your data—safe. 

Responsible AI

“Don’t ask what computers can do, ask what they should do.” That is the title of the chapter on AI and ethics in a book Brad Smith coauthored in 2019, and they are also the first words in Governing AI: A Blueprint for the Future, which details Microsoft’s five-point approach to help governance advance more quickly, as well as our “Responsible by Design” approach to building AI systems that benefit society. 

The Microsoft on the Issues Blog includes a wealth of perspectives on responsible AI topics, including the Microsoft AI Access Principles, which detail our commitments to promote innovation and competition in the new AI economy and approaches to combating deepfakes in elections announced as part of the new Tech Accord announced in February in Munich. 

The Responsible AI Standard is the product of a multi-year effort to define product development requirements for responsible AI. It captures the essence of the work Microsoft has done to operationalize its responsible AI principles and offers valuable guidance to leaders and practitioners looking to apply similar approaches in their own organizations.

You may also have heard about our AI customer commitments, which include:  

• Sharing what we are learning about developing and deploying AI responsibly and assist you in learning how to do the same. 
• Creating an AI assurance program.
• Supporting you as you implement your own AI systems responsibly. 

The Empowering responsible AI practices website brings together a range of policy, research, and engineering resources relevant to a spectrum of roles within your organization. Here you can find out more about our commitments to advance safe, secure, and trustworthy AI, learn about the most recent research advancements and collaborations, and explore responsible AI tools to help your organization define and implement best practices for human-AI interaction, fairness, transparency and accountability, and other critical objectives. 

Next steps

As Brad Smith concluded in Governing AI: A Blueprint for the Future, “We’re on a collective journey to forge a responsible future for artificial intelligence. We can all learn from each other. And no matter how good we may think something is today, we will all need to keep getting better.” 

Download our e-book, “The AI Strategy Roadmap: Navigating the Stages of AI Value Creation,” in which we share the emerging best practices that global leaders are using to accelerate time to value with AI. It is based on a research study including more than 1,300 business and technology decision makers across multiple regions and industries.