{"id":25442,"date":"2024-11-09T06:00:59","date_gmt":"2024-11-09T14:00:59","guid":{"rendered":"http:\/\/www.palada.net\/index.php\/2024\/11\/09\/news-19172\/"},"modified":"2024-11-09T06:00:59","modified_gmt":"2024-11-09T14:00:59","slug":"news-19172","status":"publish","type":"post","link":"http:\/\/www.palada.net\/index.php\/2024\/11\/09\/news-19172\/","title":{"rendered":"More value, less risk: How to implement generative AI across the organization securely and responsibly"},"content":{"rendered":"

Credit to Author: Bret Arsenault| Date: Thu, 07 Nov 2024 17:00:00 +0000<\/strong><\/p>\n

The technology landscape is undergoing a massive transformation, and AI is at the center of this change\u2014posing 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, \u201cMinimize Risk and Reap the Benefits of AI<\/strong><\/a>,\u201d we provide a comprehensive guide to navigating the challenges and opportunities of using generative AI.<\/p>\n

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<\/sup> 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.<\/p>\n

Learn how to deploy generative AI securely and responsibly<\/h2>\n

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. <\/p>\n

\"\"<\/figure>\n

Data security<\/h3>\n

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. <\/p>\n

Managing hallucinations and overreliance<\/h3>\n

Generative AI hallucinations<\/a> 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. <\/p>\n

Defending against threat actors<\/h3>\n

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. <\/p>\n

Addressing biases<\/h3>\n

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.<\/p>\n

Legal and regulatory compliance<\/h3>\n

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.<\/p>\n

Explore concrete actions for the future<\/h2>\n

As your organization adopts generative AI, it\u2019s critical to implement responsible AI principles<\/a>\u2014including fairness, reliability, safety, privacy, inclusiveness, transparency, and accountability. In this paper, we provide an effective approach that uses the \u201cmap, measure, and manage\u201d framework as a guide; as well as explore the importance of experimentation, efficiency, and continuous improvement in your AI deployment.<\/p>\n

I\u2019m 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\u2014and trust the guidance will empower your organization with the knowledge and tools needed to thrive in this new era for business.<\/p>\n

Additional resources<\/h3>\n