AI 101: A Primer for Third-Party Risk Managers

Artificial Intelligence (AI) is rapidly reshaping the landscape of risk and compliance, especially within third-party risk management (TPRM). As organizations increasingly rely on vendors, cloud providers, and outsourced services, risk managers must understand how AI impacts both operational opportunities and new vulnerabilities. This primer offers a foundational guide—AI 101 for Third-Party Risk Managers—to help professionals navigate AI’s complexities, benefits, and risks within vendor ecosystems. 

What Is AI in the Context of Third-Party Risk Management? 

AI refers to systems designed to perform tasks that typically require human intelligence—such as interpreting data, identifying patterns, predicting outcomes, or automating decisions. Within TPRM, AI is being used to streamline due diligence, monitor vendor behavior, detect anomalies, and improve overall risk visibility. 

However, AI also introduces new challenges: opaque algorithms, data privacy concerns, ethical implications, and potential regulatory breaches. For risk managers, the goal is not only to leverage AI but to understand and govern it—especially when third parties deploy AI-driven solutions. 

Why AI Matters to Third-Party Risk Managers 

✅ Enhanced Risk Detection 

AI can analyze vast amounts of data from vendors—financial health, cybersecurity posture, legal disputes, ESG scores—and flag emerging risks faster than human teams. 

⚙️ Operational Efficiency 

Automated assessments and continuous monitoring significantly reduce manual workload, speeding up vendor onboarding and risk reviews. 

⚠️ New Risk Categories 

Vendors using AI introduce new exposure: algorithmic bias, data misuse, model drift, and unethical AI practices. Managing third-party AI risk is now critical to safeguarding brand integrity and regulatory compliance. 

Key Types of AI Used in Vendor Risk Management 

Top AI Use Cases in Third-Party Risk Management 

1️⃣ Automated Due Diligence 

AI tools scan vendor documents, financials, and reputational data to score risk levels, saving analysts hours of review time. 

2️⃣ Continuous Vendor Monitoring 

Instead of annual reviews, AI enables real-time surveillance of cyber incidents, sanctions, legal filings, and ESG metrics. 

3️⃣ Predictive Risk Analytics 

Machine learning models forecast potential vendor disruptions—such as insolvency or security breaches—allowing proactive mitigation. 

4️⃣ Contract and Policy Analysis 

NLP technology parses large contracts, highlighting non-compliance clauses or missing obligations, ensuring better governance. 

New AI Risks Emerging from Third Parties 

While AI improves risk management, it also creates risks to be assessed during vendor onboarding and ongoing monitoring: 

🔍 Lack of Transparency (“Black Box” Models) 

Vendors may use AI decision-making without explaining how outputs are generated—posing auditability challenges. 

🧩 Algorithmic Bias 

Unchecked AI models can discriminate in hiring, insurance, lending, or other critical services, leading to legal and ethical exposure. 

🛡️ Data Privacy & Security 

AI systems often require large datasets, increasing the chance of data leaks or misuse by third parties. 

🔄 Model Drift & Performance Failure 

AI accuracy may degrade over time, producing faulty decisions if not regularly maintained and tested. 

Regulatory Landscape: AI Under Scrutiny 

Governments are introducing frameworks to regulate AI operations, transparency, and accountability. Third-party risk managers must stay alert to evolving regulations, including: 

• EU AI Act – Classifies AI use cases by risk level and mandates stringent controls for high-risk systems. 

• NIST AI Risk Management Framework (USA) – Introduces guidelines for trustworthy and responsible AI. 

• ISO/IEC 23894 – A standard specifically for AI risk management. 

  
  

Compliance Tip: When a vendor uses AI in service delivery, ensure they adhere to both sector regulations and emerging AI governance standards. 

Best Practices: Managing AI Risks in Third Parties 

1️⃣ Ask the Right Due Diligence Questions 

• Does the vendor use AI in critical processes? 

• Can they explain the AI model logic and data sources? 

• How do they prevent bias and ensure fairness? 

  
  

2️⃣ Demand AI Governance Documentation 

Look for AI policies, ethical use statements, and validation reports. Mature vendors should have an internal AI risk framework. 

3️⃣ Require Auditability and Transparency 

Ensure contractual rights to audit AI systems or request explanations for automated decisions. 

4️⃣ Monitor AI Outputs, Not Just AI Inputs 

Include AI performance indicators in your ongoing vendor monitoring strategy. 

5️⃣ Align AI Controls with Existing Frameworks 

Integrate AI oversight into your cybersecurity, privacy, and compliance assessments. AI risks should not be isolated—they belong in enterprise risk management. 

How AI Enhances the Role of the Risk Manager 

Rather than replacing risk managers, AI amplifies their strategic capabilities—enabling faster insights, deeper risk intelligence, and more proactive governance. 

Future Outlook: AI as a TPRM Essential 

As AI adoption grows among vendors, it will soon be a core category in risk frameworks alongside cybersecurity, financial stability, and regulatory compliance. Third-party risk programs must evolve from checkbox reviews to dynamic AI oversight models. 

Organizations that master AI-driven TPRM will gain: 

• Faster risk identification 

• Stronger compliance posture 

• Greater resilience across vendor networks 

AI 101 is no longer optional for third-party risk managers. It’s the foundation for navigating tomorrow’s vendor landscape. By understanding both AI’s potential and threats, risk leaders can build safer, smarter, and future-ready supply chains.