More value, less risk: How to implement generative AI across the organization securely and responsibly

The technology landscape is undergoing a massive transformation, and AI is at the center of this change—posing both new opportunities as well as new threats.  While AI can be used by adversaries to execute malicious activities, it also has the potential to be a game changer for organizations to help defeat cyberattacks at machine speed. Already today generative AI stands out as a transformative technology that can help boost innovation and efficiency. To maximize the advantages of generative AI, we need to strike a balance between addressing the potential risks and embracing innovation. In our recent strategy paper, “Minimize Risk and Reap the Benefits of AI,” we provide a comprehensive guide to navigating the challenges and opportunities of using generative AI.

According to a recent survey conducted by ISMG, the top concerns for both business executives and security leaders on using generative AI in their organization range, from data security and governance, transparency and accountability to regulatory compliance.¹ In this paper, the first in a series on AI compliance, governance, and safety from the Microsoft Security team, we provide business and technical leaders with an overview of potential security risks when deploying generative AI, along with insights into recommended safeguards and approaches to adopt the technology responsibly and effectively.

Learn how to deploy generative AI securely and responsibly

In the paper, we explore five critical areas to help ensure the responsible and effective deployment of generative AI: data security, managing hallucinations and overreliance, addressing biases, legal and regulatory compliance, and defending against threat actors. Each section provides essential insights and practical strategies for navigating these challenges. 

Data security

Data security is a top concern for business and cybersecurity leaders. Specific worries include data leakage, over-permissioned data, and improper internal sharing. Traditional methods like applying data permissions and lifecycle management can enhance security. 

Managing hallucinations and overreliance

Generative AI hallucinations can lead to inaccurate data and flawed decisions. We explore techniques to help ensure AI output accuracy and minimize overreliance risks, including grounding data on trusted sources and using AI red teaming. 

Defending against threat actors

Threat actors use AI for cyberattacks, making safeguards essential. We cover protecting against malicious model instructions, AI system jailbreaks, and AI-driven attacks, emphasizing authentication measures and insider risk programs. 

Addressing biases

Reducing bias is crucial to help ensure fair AI use. We discuss methods to identify and mitigate biases from training data and generative systems, emphasizing the role of ethics committees and diversity practices.

Legal and regulatory compliance

Navigating AI regulations is challenging due to unclear guidelines and global disparities. We offer best practices for aligning AI initiatives with legal and ethical standards, including establishing ethics committees and leveraging frameworks like the NIST AI Risk Management Framework.

Explore concrete actions for the future

As your organization adopts generative AI, it’s critical to implement responsible AI principles—including fairness, reliability, safety, privacy, inclusiveness, transparency, and accountability. In this paper, we provide an effective approach that uses the “map, measure, and manage” framework as a guide; as well as explore the importance of experimentation, efficiency, and continuous improvement in your AI deployment.

I’m excited to launch this series on AI compliance, governance, and safety with a strategy paper on minimizing risk and enabling your organization to reap the benefits of generative AI. We hope this series serves as a guide to unlock the full potential of generative AI while ensuring security, compliance, and ethical use—and trust the guidance will empower your organization with the knowledge and tools needed to thrive in this new era for business.

Additional resources

• Get the Grow Your Business with AI You Can Trust e-book.
• Explore the Introduction to Generative AI and Safety guide.

Get more insights from Bret Arsenault on emerging security challenges from his Microsoft Security blogs covering topics like next generation built-in security, insider risk management, managing hybrid work, and more.

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^{1, 2} ISMG’s First annual generative AI study – Business rewards vs. security risks: Research report, ISMG.