Best Practices to Deploy AI Safely

In a rapidly evolving technological landscape, deploying artificial intelligence (AI) safely is of utmost importance. Yonatan Zunger, Microsoft’s Corporate Vice President and Deputy Chief Information Security Officer (CISO) for AI, outlines essential strategies for organizations looking to implement AI responsibly. This insight is part of an ongoing series where leaders share critical perspectives on technology’s role in security.

Understanding the Challenges of AI Deployment

As the CISO for AI, Zunger dedicates his efforts to preemptively identifying potential failures and every conceivable risk associated with AI systems. He emphasizes that AI products—particularly generative AI—have shown real value in practice, illustrating that organizations are increasingly tasked with either developing AI solutions in-house or deploying third-party technologies. Zunger’s blog serves as an introduction to fundamental principles of safe AI deployment, which extend beyond the confines of Microsoft and have broader applicability in technology adoption.

Core Principles for Safe Deployment

Deploying AI safely entails a comprehensive understanding of possible risks and having contingency plans that instill confidence in managing failures. Zunger delineates several key principles:

• Identify potential failures within the system and create plans accordingly. These plans can range from modifying system design to ensure resilience, to setting thresholds for detecting failures.
• Analyze the overall system, considering all elements—users, processes, and the technology itself—to assess a wide variety of risks.
• Implement this risk assessment from the inception of a project to its conclusion, ensuring that planning for potential failures goes hand-in-hand with system design.
• Create a written safety plan that outlines identified risks and corresponding strategies. This document serves as a guide for future assessments and decision-making.

These principles resonate with established safety engineering frameworks and highlight the foundational importance of thorough risk management in any technological application.

AI-Specific Considerations

Developing AI systems comes with unique challenges, particularly given the inherent unpredictability associated with AI technologies. Zunger notes that errors like “hallucinations” or incorrect user inputs can affect outputs. Thus, organizations must adopt a mindset that treats the AI similarly to entry-level team members: enthusiastic yet prone to error. This requires a focus on building oversight mechanisms into AI processes:

• Incorporate thorough testing that anticipates a range of scenarios, ensuring systems function correctly under diverse input types.
• Encourage multiple evaluations of decisions to ensure clarity and agreement among criteria. This step helps identify any misalignment between intent and output.
• Precision in communication is critical, especially where AI outputs transition to human decision-making. Clarity can mitigate misunderstandings that lead to errors.

Ultimately, Zunger insists that while novel technologies present new risks, they reinforce the necessity of adhering to best practices for safety that have existed for years.

Practical Resources for Safe AI Deployment

Further resources to assist organizations in implementing these principles are anticipated. In the meantime, the blog encourages readers to explore Microsoft’s Responsible AI site for more comprehensive guides and frameworks, as well as to engage with the provided video example highlighting a systematic walkthrough of the AI deployment analysis process.

As technological advancements continue to shape various sectors, prioritizing safety in AI deployment is not just an operational consideration but a crucial component of fostering trust and reliability in automated systems.