AI Risk Management Faces a New Rulebook
Third-Party Risk Management (TPRM) involves understanding, assessing, and mitigating risks associated with vendors, suppliers, contractors, and other business partners. Risks can be operational, regulatory, a data and software breach, or reputational.
AI in TPRM provides a new level of intelligence and efficiency. By using advanced analytics, machine learning, and automation, AI is able to observe your suppliers in real time, document early warnings, predict vulnerabilities, and confirm compliance in ways unimaginable with manual methods. For example, AI can follow news sentiment about a supplier, identify anomalies in transaction data, or automatically monitor compliance documentation.
The EU AI Act 2024 is a significant step in the direction of regulating how AI can be used for such risk management. The EU AI Act governs how AI is used and classifies AI systems according to risk (minimal, limited, high, and unacceptable) and states a minimum set of requirements for high-risk applications of AI (including transparency, human oversight, and data governance). For AI-driven TPRM solutions, transparency will entail providing explainable outputs, mitigating bias, and following ethical and legal obligations to avoid non-compliance.
The Hidden Risks of AI in Third-Party Management
AI delivers amazing value, but it also has risks associated with implementation. Some of the risks include:
- Operational Risks: Over-reliance on automation that does not account for rare or nuanced issues.
- Data Privacy & Security Risks: Security breach or misuse of sensitive vendor and partner data.
- Ethical Risks: AI-driven decisions with bias that can lead to unfair treatment of vendors or stakeholders.
- Regulatory Risks: Failure to adequately address compliance with EU AI Acts 2024, GDPR 2018, or DORA 2025.
- Reputational Risks: Public response and trust erosion due to negative or unethical AI outcomes.
Regulatory and Ethical Pressures
AI governance is no longer a choice. Compliance is becoming more tightly regulated, and organizations are expected to align their AI systems with legal requirements as well as with societal obligations. The GDPR (2018) has strict rules around data access and user consent. The EU AI Act 2024 enforces transparency and accountability for high-risk AI systems. At a minimum, DORA 2025 ensures operational resilience of the financial system by requiring that organizational resilience does not suffer from the system worries of third-party providers.
Apart from legal requirements, there is also pressure from ESG (Environmental, Social, and Governance) initiatives, along with growing public pressure, for fairness, transparency, and accountability in decision-making involving AI.
As reported by EY, AI is playing a pivotal role in transforming how organizations identify, monitor, and manage third-party risks in today’s volatile business environment.
Traditional TPRM vs AI-Powered TPRM
| Aspect | Traditional TPRM | AI-Powered TPRM |
| Monitoring Speed | Periodic, manual checks | Real-time, continuous scanning |
| Data Sources | Limited to internal/vendor reports | Multiple internal and external feeds, including news, sentiment, and ESG data |
| Accuracy | Human judgment-based | Predictive analytics, anomaly detection, and trend forecasting |
| Scalability | Limited scalability for large vendor networks | Easily manages thousands of suppliers globally |
| Compliance Tracking | Manual audits and documentation | Automated, ongoing compliance checks with alerts |
The Strategic Imperative: AI Risk Management
AI risk management is ongoing, evolving with threats and regulations. It also consists of two aspects:
External Monitoring
Monitoring the health, compliance, and security of suppliers and partners in real time. AI TPRM tools identify data sets (financial reports, news about your suppliers or partners, etc.) and use these as risk indicators. With AI for TPRM, you can identify risk much more readily. For example, if a vendor gets breached, AI tools can recognize that in tandem with the other monitoring data being used and identify what the impact will be to you (if any), so you can take action before you have drama to manage.
Internal Governance
Making sure that our own AI systems are fair, secure, transparent, and in compliance with applicable laws, such as the EU AI Act 2024 and the General Data Protection Regulation (GDPR) 2018. Will there be biases? What explainability features should we be supplying, and should we have a human-in-the-loop for high-risk models?
Combining both vigilance and integrity forms an overall 360° risk defender: external vigilance supported by internal integrity. A deficiency in either the external or the internal will cause an issue in the other, will make decision-making tougher and slower, and will erode trust with customers, regulators, and partners. In today’s interconnected, high-speed information risk environment, the combined effort of both vigilance and integrity is not an option; it is a requirement.
Strategies to Mitigate AI Risks in TPRM
To be successful in mitigating risks in AI-based TPRM, engage in the following:
- Continuous Vendor Monitoring: Real-time risk monitoring for vendors based on an AI dashboard to scout for emerging risks.
- Bias Auditing: Complete checks on algorithms to prevent bias and unfair decision-making.
- Explainability Tools: Using tools to ensure that AI outputs can be understood and validated by the needed personnel.
- Unified Risk Data Platforms: Building and integrating internal and external risk intelligence to provide a unified risk view.
- Compliance Automation: Offering a viable way of implementing automatic compliance with the global AO process, the European Union Artificial Intelligence Act (EU AI Act, 2024), the General Data Protection Regulation (GDPR, 2018), and the Digital Operational Resilience Act (DORA, 2025).
- Two-Layer Risk Defense: External threat coverage and internal AI risk assessment.
Embedding AI Governance from Day One
IBM’s AI Trust, Risk, and Security Management (TRiSM) framework establishes its model for responsibly embedding AI. The model’s four areas include:
- Fairness: Minimizing bias and ensuring conditions resulting in equitable outcomes.
- Robustness: Having robust and impenetrable AI systems that resist manipulation and error.
- Interpretability: Enabling AI systems to make decisions understandable to stakeholders.
- Security: Protecting AI solutions from breaches and misuse.
Embedding governance from inception ensures AI in internal tools and risk monitoring adheres to ethical, operational, and regulatory standards.
Embedding that kind of governance from day one is essential; without it, internal deployments can unintentionally veer off course. As explored in the article AI Internal Deployment Governance Risks, insufficient oversight can lead to bias, security lapses, and operational failure.
My Take: A Dual-Layer Defense
Many organizations will wrongly focus on half the equation; they will strictly monitor their external suppliers, or they will spend resources securing their own AI systems while ignoring the other side. This one-layer approach has inherent blind spots that adversaries in cyberspace, compliance failures, and operational breakdowns can exploit.
Organizations achieve true resilience by integrating a dual-layer defense, maintaining uninterrupted external visibility over partners and supply chains, and ensuring AI systems are ethical, compliant, and robust. Coordination reduces vulnerabilities, boosts operational confidence, and aligns AI with expectations.
From Risk to Resilience
AI in TPRM is much more than a shield; it is also an agent of smarter, quicker, and more decisive decisions. With real-time monitoring of third-party risks, a structured governance framework, and automated compliance processes, organizations can transition from reacting to crises and take actions that mitigate risks before they become crises. AI-driven risk management enhances operations, adaptability, supplier relationships, and competitiveness.
Regular updates and bug fixing are just as important as building in fairness and transparency. As highlighted in the article Code Quality and AI Bug Fixing: PlayerZero AI Bug Fix, establishing a process for ongoing code quality and rapid bug resolution is critical to maintaining trust in your AI tools.
You should evaluate your current TPRM process today: Do we have the capability to see real-time risks from third parties? Is our internal AI governance strategy airtight? Organizations that can answer “yes” to both questions will be the organizations that thrive in the high-risk, AI-driven global economy.
