The Gartner survey revealing that 45% of organizations have faced third-party-related disruptions highlights a pressing need for enhanced risk management. Despite its relatively new application to the discipline, artificial intelligencer (AI) is poised to be the assist that third-party risk management (TPRM) needs. With governed AI usage, businesses can achieve real-time risk assessment and predictive analytics, ensuring resilience against emerging threats. And as organizations navigate an increasingly intertwined risk landscape, AI stands as a potential ally in safeguarding operations now and for the future.

The Compelling Case for TPRM Automation with AI

It's understandable to be cautious about AI adoption in an organization. To address its accuracy and security, the National Institute of Standards and Technology (NIST) has developed a comprehensive AI Risk Management Framework (RMF) to guide organizations in assessing and mitigating risks associated with AI systems. This framework emphasizes a risk-based approach, acknowledging that while eliminating AI risk is impossible, it can be managed through structured understanding and prioritization. Major companies, including Microsoft, have endorsed this framework, highlighting its significance in the industry.

There is a growing commitment across various sectors to implement rigorous testing and validation processes for AI systems, ensuring their safety, reliability and compliance with regulatory standards. And that’s because there are significant gains to using AI third-party risk management, like:

• Unprecedented Efficiency: AI third-party risk management drastically reduces the time and resources spent on manual tasks. From automated data gathering during initial due diligence to continuous monitoring of vast datasets, your team can focus on strategic analysis and higher-value activities instead of repetitive administration.
• Enhanced Scalability: As your vendor ecosystem expands, manual processes struggle to keep pace with the increasing complexity. AI-enabled GRC platforms provide the scalability needed to manage a growing number of third-party vendors without a proportional increase in workload or headcount.
• Significant Risk Reduction: The proactive nature of AI-driven TPRM minimizes the likelihood of overlooking critical risks. AI can identify subtle patterns and anomalies in vendor data that might be missed by human analysts, enabling earlier intervention and preventing potential breaches, compliance violations or reputational damage related to sensitive data.

How AI-Enabled GRC Platforms Automate Key TPRM Functions

AI isn't just about streamlining existing processes; it's about fundamentally transforming how you approach third-party risk. Here's how AI-enabled GRC platforms with advanced AI components automate critical TPRM functions:

Intelligent Vendor Onboarding and Due Diligence

• AI-Powered Data Extraction: AI can automatically extract key information from vendor questionnaires, contracts and publicly available sources, significantly reducing manual data entry during initial due diligence.
• Automated Risk Profiling: Based on extracted data and pre-defined risk frameworks, AI can automatically generate initial risk profiles and categorize third-party vendors based on inherent risk levels.
• Smart Questionnaire Routing: AI can dynamically route relevant assessment questionnaires to vendors based on their risk profile and the services they provide, streamlining the vendor security assessment.

  The University of Kentucky's Information Technology Services (UK ITS) Cybersecurity team has integrated AI into their third-party risk assessment processes. By leveraging AI tools to summarize lengthy reports, such as SOC 2 reports, the university has significantly increased efficiency, allowing more assessments to be handled in less time. This approach not only saves hours of manual work but also reduces costs associated with processing.

• Enhanced Background Checks: AI can analyze vast datasets from various sources (e.g., news articles, sanctions lists, adverse media) to identify potential red flags and enrich vendor background checks.

Continuous Monitoring with AI-Driven Insights

• Real-time Data Aggregation: AI can continuously pull data from diverse internal and external sources related to your third-party vendors (e.g., financial news, regulatory updates, security bulletins) for comprehensive monitoring of third-party relationships.
• Anomaly Detection: Machine learning algorithms can identify deviations from expected vendor behavior or emerging risks that might indicate financial distress, security vulnerabilities or compliance issues.
• Predictive Risk Scoring: AI can analyze historical data and current trends to predict potential future risks associated with specific vendors, allowing for proactive mitigation strategies within your third-party supply chain.

Automated Risk Assessments and Scoring

• Dynamic Risk Weighting: AI can dynamically adjust the weighting of different risk factors based on their relevance to specific third-party vendors and the evolving threat landscape.
• Automated Scorecard Generation: Based on continuous monitoring and assessment data, AI can automatically generate and update vendor risk scorecards, providing a clear and current view of their risk posture.
• AI-Driven Recommendations: Some advanced platforms can even provide AI-powered recommendations for mitigating identified risks, suggesting specific controls or actions.

Streamlined Document Collection and Evidence Management

• Automated Request and Follow-up: AI-driven workflows can automatically send document requests to third-party vendors and manage follow-ups for timely submissions.
• Intelligent Document Analysis: AI can analyze submitted documents (e.g., SOC 2 reports, insurance certificates) to verify completeness, identify key clauses and flag potential issues, including those related to privacy diligence.
• Centralized and Audit-Ready Repository: All vendor-related documents and evidence are stored in a secure, centralized repository, sometimes referred to as a knowledge base, making audits significantly more efficient and helping protect sensitive data.

Real-Time Alerts and Reporting

• Automated Trigger-Based Alerts: AI can trigger real-time alerts based on predefined risk thresholds or significant changes in a vendor's risk profile within your third-party relationships.
• Customizable Dashboards and Reports: AI-enabled platforms can generate customizable dashboards and reports that provide stakeholders with clear, concise, and up-to-date insights into third-party risk exposure.
• Natural Language Reporting: Some advanced AI features can even generate plain-language summaries of risk assessments and monitoring findings, making it easier for non-technical stakeholders to understand the information and fostering customer trust.

Real-World Impact: AI Third-party Risk Management in Action

The transformative power of AI in TPRM is being realized in highly regulated industries, helping software companies and others build resilient organizations:

• Financial Services: AI algorithms are being used to continuously monitor the financial health and regulatory compliance of thousands of vendors, identifying potential risks like insolvency or sanctions violations before they escalate. Automated due diligence powered by AI accelerates the onboarding of new fintech partners while contributing to compliance with regulations like GLBA, GDPR and CCPA, particularly concerning privacy diligence and sensitive data.

  Prometeia, a financial consulting firm, has developed an AI Model Validation Framework tailored for financial applications. This framework addresses risks associated with AI by focusing on key pillars such as data, methodology, process, and governance. It aims to enhance trust in AI adoption by standardizing validation processes, ensuring compliance with legislative frameworks like the EU AI Act, and improving internal procedures.
  

• Healthcare: AI-enabled GRC platforms are helping healthcare organizations manage the complex web of business associates and ensure HIPAA compliance. AI analyzes security certifications, breach notifications and access controls to provide a dynamic risk assessment of each vendor, minimizing the risk of data breaches and patient privacy violations.
• Pharmaceuticals: AI is streamlining the vendor qualification process for critical suppliers of raw materials and manufacturing services, ensuring adherence to stringent quality standards and regulatory requirements like GMP within the third-party supply chain. Continuous monitoring powered by AI tracks supplier performance and identifies potential supply chain disruptions proactively.

Best Practices for Implementing AI Automation in TPRM

Successfully integrating AI into your TPRM strategy requires careful planning and execution, focusing on establishing responsible AI usage policies:

• Define Clear Objectives and Use Cases: Before implementing any AI technologies, clearly define the specific TPRM challenges you want to address and the desired outcomes. Focus on specific use cases where AI can deliver the most significant impact on managing third-party vendors.
• Ensure Data Quality and Governance: The effectiveness of AI algorithms heavily relies on the quality and integrity of the data they analyze. Establish robust data governance policies and processes to ensure accurate and reliable information, especially concerning sensitive data from your third-party relationships.
• Prioritize Integration with Existing Systems: Choose an AI-enabled GRC platform that seamlessly integrates with your existing TPRM tools, CRM, ERP and other relevant systems to avoid data silos and maximize efficiency.
• Phased Implementation and Iteration: Avoid trying to automate everything at once. Implement AI in a phased approach, starting with high-impact areas and iteratively expanding based on lessons learned and demonstrable ROI.
• Focus on Change Management and Stakeholder Buy-in: Clearly communicate the benefits of AI automation to your team and other stakeholders. Address their concerns and provide adequate training to ensure smooth adoption and maximize user acceptance. Highlight how AI will augment their capabilities, not replace them entirely, promoting responsible AI usage.
• Maintain Human Oversight and Validation: While AI provides powerful automation, human expertise remains crucial. Implement processes for reviewing AI-generated insights and recommendations, especially for critical risk decisions. Ensure your team understands how the AI works and can identify potential biases or inaccuracies as part of your responsible AI programs.

Addressing Common Concerns with AI Third-Party Risk Management

In response to the rapid advancement of AI technologies, several countries have established AI Safety Institutes to evaluate and ensure the safety of advanced AI models. For instance, the United Kingdom founded the AI Safety Institute (AISI) in April 2023, evolving from the Frontier AI Taskforce. This institute focuses on independent safety evaluations, emphasizing that AI companies cannot "mark their own homework." Similarly, the United States has established its own AISI as part of the National Institute of Standards and Technology (NIST).

While the potential of AI in TPRM is immense, it's important for software buyers and organizations to acknowledge and be prepared with these common concerns.

• Data Privacy: Ensure that the AI-enabled GRC platform adheres to all relevant data privacy regulations (e.g., GDPR, CCPA) regarding the collection, storage, and processing of vendor data, particularly sensitive data. Implement robust data security measures and robust AI-proof deidentification techniques where necessary, reinforcing privacy diligence.
• Model Transparency and Explainability: Understand how the AI algorithms work and ensure a degree of transparency in their decision-making processes. While "black box" AI might offer accuracy, explainable AI (XAI) provides valuable insights into the reasoning behind risk scores and recommendations, fostering customer trust and enabling better human oversight and responsible AI usage. This is key for managing the complexity of AI systems and their AI components.
• Integration with Existing Systems: As mentioned earlier, seamless integration is crucial. Thoroughly evaluate the platform's integration capabilities and plan for potential challenges during the implementation process.
• Initial Investment and ROI: While the long-term benefits of AI automation are significant, the initial investment can be a concern for software companies and other businesses. Conduct a thorough cost-benefit analysis to demonstrate the potential ROI through increased efficiency, reduced risk, and avoided losses.
• Potential for Bias: AI algorithms are trained on historical data, which may contain inherent biases. Implement measures to identify and mitigate potential biases in the AI models to ensure fair and accurate risk assessments within your responsible AI programs.

Next Steps for AI Third-Party Risk Management

If your team or your organization has discussed the idea of AI being a tool in your TPRM program, it’s certainly worth pursuing. Here’s a few high-level recommendations to further the conversations:

1. Educate Yourself and Your Team: Stay informed about the latest advancements in AI-enabled GRC solutions and their potential applications in managing third-party relationships. Encourage your team to participate in webinars, industry events, and training sessions on ai technologies and responsible ai usage.
2. Identify Your Key Pain Points: Conduct an internal assessment to pinpoint the most time-consuming, error-prone, and high-risk areas within your current TPRM processes. These are prime candidates for AI-driven automation and a more effective vendor security assessment.
3. Research AI-Enabled GRC Vendors: Explore the market for GRC platforms with robust AI capabilities. Evaluate vendors based on their specific TPRM functionalities, industry expertise, integration capabilities, security posture, and pricing models. Pay attention to how they handle AI components and support responsible AI programs.
4. Request Demos and Proof of Concepts (POCs): Engage with potential vendors and request tailored demos that address your specific use cases, focusing on how their AI technologies can streamline initial due diligence and continuous monitoring. Consider conducting a POC to evaluate the platform's effectiveness in your environment, including handling sensitive data and performing privacy diligence.
5. Develop a Phased Implementation Plan: Outline a strategic roadmap for implementing AI automation, starting with pilot projects in key areas and gradually expanding to encompass more comprehensive TPRM processes within your third-party supply chain.
6. Prioritize Change Management and Communication: Engage with your team and stakeholders early and often to address concerns, highlight benefits, and ensure a smooth transition to AI-powered processes. Leverage a central knowledge base to share information and best practices.

By applying safeguarded AI-enabled GRC, experienced third-party risk managers can move beyond the limitations of manual processes, build more resilient and scalable TPRM programs, and ultimately, better protect their organizations. Right now, it looks like the immediate future of TPRM is automated and intelligent.

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