Risk Management in the Age of AI

Charles M. Walls | September 13, 2023 | Views: 88

Artificial Intelligence (AI) has taken the world by storm, revolutionizing industries and reshaping the way we interact with technology. But as AI continues to proliferate, so do the risks associated with it. In a recent webinar from the International Systems Security Association (ISSA), we delved into the latest trends and regulations in data privacy and protection, best practices for managing AI risks, and the emerging ethical considerations surrounding AI. Here, we present a comprehensive overview of the insights gleaned from this enlightening session.

The Landscape of AI Risk Management

Copyright Concerns: One of the foremost challenges in the realm of AI is copyright. As AI models consume vast amounts of data, there is a growing concern regarding intellectual property rights, especially when these models generate content that may be considered original.

Types of Information Requiring Governance: Effective AI risk management hinges on the governance of several critical types of information, including personal data, internal data, and even the AI models themselves. Ensuring the ethical use and protection of such data is paramount.

The Need for Explainability: As AI models become more complex, there is a growing demand for explainability. Understanding how these models arrive at their conclusions is vital, both for transparency and to identify potential biases.

Best Practices for AI Risk Management

Data Understanding and Inventory: Organizations must have a profound understanding of the data used in their AI models. This includes ingress, data types, and source traceability.

Avoiding Assumptions: A critical best practice is not to expect AI models to know the answer to complex questions. Human intervention and oversight are crucial to ensure AI operates safely and ethically.

Polling the Industry

During the webinar, several polls shed light on the current state of AI adoption and risk management practices:

1. "Do you have 'traditional' AI programs or projects at your organization?"
• Yes: 34%
• No: 49%
• Don't know: 16%
2. If yes to the first question, "Do you have any that are currently paused due to concerns around governance or risk?"
• Yes: 32%
• No: 37%
• Don't know: 30%
3. "Do you have any generative AI projects in POC or in production at your organization?"
• Yes: 36%
• No: 41%
• Don't know: 21%

Driving Factors for AI Implementation

Companies are driven to embrace AI for various reasons:

• Compliance: Adhering to regulations and ensuring data privacy is a key driver.
• Risk and Reward Tradeoffs: A solid grasp of the balance between AI risks and rewards motivates organizations.
• Competition: Staying competitive in the market often necessitates AI adoption.

Defining AI: A Gray Area

Defining AI is no simple task. The webinar highlighted the ambiguity surrounding AI's definition, with an unclear SEC definition compounding the challenge.

The Role of Metadata Management

Traditional and new metadata management plays a crucial role in AI risk management. Writing code is considered one of the most effective ways to utilize metadata or data schemas.

Ethical Considerations in AI Risk Management

Organizations are increasingly focusing on ethical considerations when implementing AI risk management strategies. This includes:

• Addressing Biased Data: Efforts to mitigate biases in AI datasets are gaining prominence.
• Enhancing Workplace Culture: Making the workplace more enjoyable and inclusive is another facet of ethical AI adoption.

Recommendations for Generative AI Beginners

For those embarking on generative AI projects, several recommendations were shared:

• Education and Control: Understanding the technology and having control over AI models is essential.
• Standards and Regulations: Adhering to proprietary standards and industry regulations ensures responsible AI usage.
• Model Explainability: Prioritize explainability to understand AI model outputs.
• Effective Tool Usage: Leverage available tools to streamline AI development and management.
• Data Traceability: Maintain data traceability to ensure accountability.
• Guard Against Hallucinations: Be vigilant and check for any unexpected or incorrect model outputs.
• Source Data Tracing: Ensure the ability to trace data back to its source for transparency and accountability.

The webinar shed light on the multifaceted landscape of AI risk management, emphasizing the importance of data governance, ethics, and transparency. As organizations continue to harness the power of AI, staying informed and implementing best practices are crucial to navigate the evolving challenges of the AI age.