The Risks of Using AI in TPRM (and How to Mitigate Them)

AI technologies can deliver tremendous benefits to your third-party risk management (TPRM) program, but be prepared to address these potential risks.
Brad Hibbert
Chief Operating Officer & Chief Strategy Officer
August 17, 2023
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AI-Powered Third-Party Risk ManagementArtificial intelligence (AI) has the potential to revolutionize the practice of third-party risk management. However, third-party risk managers should understand AI’s current limitations and risks – and have a mitigation strategy ready. Below are five risks and mitigations that you should consider as your organization evaluates how AI can support your TPRM program.

1. Data Quality and Bias Risks

AI models heavily rely on data quality and accuracy. Poor data quality can lead to erroneous risk assessments, while biased data can perpetuate unfair treatment of suppliers or third parties.

To mitigate this risk, implement robust data governance practices, including data validation, cleansing and enrichment. Continuously monitor and audit data to ensure its accuracy and completeness. Finally, employ diverse and representative data sets to address bias in AI models.

2. Lack of Transparency and Comprehension

AI models can be highly complex and difficult to interpret. Lack of transparency and comprehension can raise concerns about the validity of third-party risk assessments and decision-making processes.

To overcome this, use AI algorithms and models that offer interpretations and explanations. Choose AI tools that provide insights into how the model arrived at specific risk scores or predictions. Using transparent AI systems will help you to build trust with stakeholders and enhance regulatory compliance.

3. Cybersecurity and Data Privacy Risks

AI systems that handle sensitive risk and supplier data become attractive targets for cyber-attacks and data breaches. To mitigate cyber and data privacy concerns:

  • Implement robust cybersecurity measures, including encryption, access controls, and regular security audits
  • Conduct privacy impact assessments to identify and address potential data privacy risks
  • Validate controls against commonly used cybersecurity frameworks such as NIST, ISO, SOC 2 or CIS
  • Use the frameworks to comply with relevant data protection regulations for safeguarding sensitive information

To ensure your usage does not infringe on your customer’s rights, be sure to engage your organization’s legal and compliance teams, as well as external auditors, prior to evaluating AI technologies.

How Will AI Impact Your TPRM Program?

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4. Shortfalls in Human-AI Collaboration and Oversight

Overreliance on AI without human oversight can lead to errors or unintended consequences that may go unnoticed – especially as the model is being trained. To mitigate this potential risk, establish clear roles for human-AI collaboration, where human experts provide oversight, validate AI-generated insights, and intervene when necessary. Develop a feedback loop to continuously improve AI models based on human expertise and feedback.

5. AI Talent Scarcity and Skills Gaps

Because AI is still a relatively newly leveraged capability in many organizations, a shortage of skilled AI professionals may hinder the successful implementation and utilization of AI in third-party risk management programs.

To overcome this challenge, invest in AI talent development and training for existing third-party risk management teams. Collaborate with external experts or partner with AI service providers to fill the skills gap. Finally, encourage a culture of continuous learning to keep up with AI advancements.

Common sources of AI education and best practices include:

Take the Next Step in Your TPRM Program’s AI Journey

By proactively addressing these key risk factors, your organization can maximize the benefits of AI capabilities in TPRM and supply chain risk management programs. You can pave the way for successful AI adoption by implementing mitigation strategies centered around data quality, transparency, cybersecurity, human-AI collaboration, and talent development. As a result, you’ll evolve you third-party risk management program while ensuring a more resilient and competitive future for your business.

Curious to learn more? Read our paper, How to Harness the Power of AI in Third-Party Risk Management, or schedule a demo 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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