How Third-Party Risk Managers Can Learn About AI and ML

Understand the third-party risk management applications for artificial intelligence and machine learning, and access resources for exploring how to use these impactful technologies in your job role. 
Brad Hibbert
Chief Operating Officer & Chief Strategy Officer
August 07, 2023
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Thanks to ChatGPT and other similar applications, artificial intelligence (AI) and its potential uses are the focus of many conversations these days. Naturally, there is a lot of confusion over what exactly AI is and how it can benefit us in our jobs. As a third-party risk manager, it's essential for you to stay up to date with the latest developments in AI and machine learning (ML) – and understand how to use these technologies to enhance your risk management processes while avoiding their potential pitfalls.

This post defines AI-related terms, shares ways that AI can benefit third-party risk management (TPRM) programs, and recommends resources for learning how to use AI to be a more effective in your job role.

What Is Artificial Intelligence?

Artificial intelligence (AI) is a field of computer science focused on creating machines that can perform tasks typically requiring human intelligence. AI systems use algorithms and large datasets to process information, recognize patterns, and make informed predictions or recommendations. The benefits of AI include task automation, improved problem-solving, enhanced decision-making and more.

What Is Machine Learning?

Machine learning (ML) is a subset of AI that enables computers to learn from data and improve their performance over time without being explicitly programmed. It's like teaching a computer to self-sufficiently recognize patterns and make predictions based on examples it has seen before.

Machine learning can be categorized into various types, including supervised learning, unsupervised learning, and reinforcement learning. Each type has specific risk management applications and use cases, including:

  • Supervised Learning: In this approach, the model learns from labeled data, where inputs are paired with corresponding outputs. It can be used for credit risk assessment, fraud detection, and asset price prediction.
  • Unsupervised Learning: This type deals with unlabeled data and focuses on finding patterns, relationships or clusters within the data. Unsupervised learning can help in anomaly detection and risk identification.
  • Reinforcement Learning: This technique involves an agent that learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. It has applications in portfolio optimization and dynamic risk management.

How Artificial Intelligence and Machine Learning Can Help Your Third-Party Risk Management Program

As a risk manager, being knowledgeable about AI and machine learning will enable you to harness the potential of these technologies, identify potential risks associated with their implementation, and make informed decisions on how to leverage them effectively and responsibly in your third-party risk management strategies.

Here are just a few of the ways you can leverage AI in your third-party risk management program:

  • Task automation: AI-powered systems can streamline routine third-party risk assessments, data analysis and reporting. This improves efficiency and accuracy while helping third-party risk managers to focus on higher-level activities.
  • Predictive analytics: AI models can analyze historical data and patterns to predict potential risks, helping you to take proactive measures to mitigate them.
  • Anomaly detection: AI algorithms can identify unusual patterns or behaviors that may indicate fraud, security breaches or other risks.

How Will AI Impact Your TPRM Program?

Read our 16-page report to discover how AI can lower third-party risk management costs, add scale, and enable faster decision making.

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6 Recommendations for Third-Party Risk Managers Who Want to Learn About AI and ML

Learning about AI and machine learning may seem intimidating, but there are several accessible and practical ways to get started. Here are six recommendations to help you expand your knowledge in this area:

1. Take online courses and tutorials

Many platforms offer beginner-friendly online courses and tutorials on AI and machine learning. Look for courses designed specifically for non-technical professionals. These courses often provide a high-level overview of the concepts, applications and potential risks associated with AI and machine learning. Some popular platforms for online learning include:

2. Attend workshops and seminars

Look out for workshops, seminars or webinars conducted by industry experts or organizations that focus on AI and machine learning for non-technical audiences. These events often provide practical insights and real-world examples that can help you understand the relevance and implications of AI in risk management. One such source is The Future of AI and Its Impact on Your Organization (

3. Read books and articles

There are plenty of books and articles written for non-technical readers that explain AI and machine learning in simple terms. Look for books or articles published in business or risk management journals. These resources can provide a solid foundation and help you grasp the broader implications of AI in your field.

4. Collaborate with technical colleagues

Engage in discussions with colleagues or teams who have expertise in AI and machine learning. By collaborating with them, you can gain insights into how these technologies are being used in practical applications and understand potential challenges and solutions from a risk management perspective.

5. Participate in industry events

Attend conferences, panel discussions, and industry events that focus on AI, machine learning, and third-party risk management. These events provide networking opportunities, and you can learn from professionals who have implemented AI solutions in their organizations. Listening to case studies and success stories can help you understand the benefits and limitations of these technologies.

6. Don’t forget your regulatory responsibility

One of the most significant concerns surrounding AI and machine learning is data privacy and security. Businesses that utilize AI need to comply with strict data protection regulations like the General Data Protection Regulation (GDPR) in the European Union or similar laws in other regions. Companies must ensure that they collect, store and process data responsibly and with user consent. With new laws and guidelines continuously being proposed and implemented, it is crucial to refer to the latest regulations and developments in specific regions and industries.

Take the Next Step on Your AI and ML Journey

Remember that you don't need to become a technical expert in AI and machine learning. As a third-party risk manager, your goal is to have a working understanding of these technologies, their implications, and how they can enhance your third-party risk management strategies. Focus on learning the foundational concepts, understanding relevant applications, and staying informed about the evolving landscape of AI in your industry. Over time, you can build on this knowledge and collaborate with technical experts to make informed decisions and recommendations in your role.

For more on how your organization can take advantage of AI and ML technologies to improve your TPRM program, read How to Harness the Power of AI in Third-Party Risk Management, or request a demo for a strategy session today.

2014 04 10 Headshot Brad Suit
Brad Hibbert
Chief Operating Officer & Chief Strategy Officer

Brad Hibbert brings over 25 years of executive experience in the software industry aligning business and technical teams for success. He comes to Prevalent from BeyondTrust, where he provided leadership as COO and CSO for solutions strategy, product management, development, services and support. He joined BeyondTrust via the company’s acquisition of eEye Digital Security, where he helped launch several market firsts, including vulnerability management solutions for cloud, mobile and virtualization technologies.

Prior to eEye, Brad served as Vice President of Strategy and Products at NetPro before its acquisition in 2008 by Quest Software. Over the years Brad has attained many industry certifications to support his management, consulting, and development activities. Brad has his Bachelor of Commerce, Specialization in Management Information Systems and MBA from the University of Ottawa.

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