More value, less risk: How to safely and responsibly implement generative AI in your organization

The technological landscape is undergoing a massive transformation, and artificial intelligence is at the center of these changes, creating both new opportunities and new threats. While attackers can use AI to carry out malicious activities, it can also be a game-changer for organizations to help defeat cyberattacks at machine speed. Already today, generative artificial intelligence stands out as a transformative technology that can help drive innovation and efficiency. To maximize the benefits of generative AI, we need to strike a balance between eliminating potential risks and enabling innovation. In our recent strategy paper, “Minimize risks and take advantage of AI,” we provide a comprehensive guide to navigating the challenges and opportunities of using generative AI.

From data security and governance, transparency and accountability, to regulatory compliance, business leaders and security chiefs are most concerned about the use of generative artificial intelligence in their organizations, according to a recent survey by ISMG.¹ In this document, the first in a series on AI compliance, governance, and security from the Microsoft security team, we provide business leaders and CTOs with an overview of potential security risks when deploying generative AI, as well as information on recommended security measures and approaches to implement technology responsibly and efficiently.

Learn how to safely and responsibly deploy generative AI

In this article, we explore five critical areas that will help ensure the responsible and effective deployment of generative AI: data security, managing hallucinations and overconfidence, eliminating bias, legal and regulatory compliance, and threat protection. Each chapter provides important information and practical strategies for overcoming these challenges.

Data security

Data security is a top concern for business and cybersecurity leaders. Specific concerns include data leakage, excessive data access, and improper internal sharing. Traditional practices such as applying data permissions and lifecycle management can improve security.

Management of hallucinations and excessive dependence

Generative AI hallucinations can lead to inaccurate data and erroneous decisions. We explore methods that help ensure the accuracy of AI results and minimize the risks of overconfidence, including anchoring data from trusted sources and using an AI team.

Protection against threats

Criminals use artificial intelligence for cyberattacks, which makes security measures extremely important. We look at defenses against malicious model instructions, AI jailbreaks, and AI-driven attacks, with a particular focus on authentication measures and insider risk programs.

Eliminating prejudices

Reducing bias is critical to ensuring the fair use of AI. We discuss methods for identifying and mitigating biases from training data and generative systems, emphasizing the role of ethics committees and diversity practices.

Compliance with legislation and regulations

It is difficult to navigate the rules of artificial intelligence due to unclear instructions and global differences. We offer best practices for aligning AI initiatives with legal and ethical standards, including establishing ethics committees and using frameworks such as the NIST AI Risk Management Framework.

Learn specific actions for the future

Since your organization uses generative artificial intelligence, its implementation is critical principles of responsible AI—including fairness, reliability, security, privacy, inclusiveness, transparency and accountability. In this paper, we propose an effective approach that uses the “map, measure and manage” framework as a guide; and explore the importance of experimentation, efficiency, and continuous improvement in AI deployment.

I’m excited to kick off this series on AI compliance, governance and security with a strategy paper on minimizing risk and enabling your organization to take advantage of generative AI. We hope this series will serve as a guide to unlocking the full potential of generative AI while ensuring security, compliance, and ethical use, and trust that these guidelines will give your organization the knowledge and tools it needs to thrive in this new era for business.

Additional resources

Get more information from Bret Arsenault about his new security challenges Microsoft Security Blogs covering topics such as next-generation embedded security, internal risk management, hybrid workload management, and more.

---

^{1, 2} ISMG’s First Annual Generative Artificial Intelligence Study – Business Rewards vs. Security Risks: Research ReportISMG.