AI Risk and Third-Party Risk Management: What You Need to Know

The integration of artificial intelligence in business processes is now a standard practice. This presents new complex risks to business. This is because third-party risks are further increased by external development and maintenance of AI solutions. Such risks frequently extend over the areas of security, compliance, and operations. Organizations must understand this complex landscape to ensure organizational resilience.

The main risks of third-party AI are discussed in this article, along with ways that companies can utilize AI to manage risks. Moreover, we point out the methods and resources that ensure compliance, security, and operational stability.

Top Risks of Third-Party AI (Risk for AI)

Adopting AI through third parties creates a distinct risk profile. These are strategic threats impacting financial stability, legal compliance, and brand reputation. Organizations must now navigate several pressing areas.

Model Drift and Performance Decay

An AI model deployed today will not remain static. Model drift is the gradual loss of a model’s accuracy as real-world data changes. For a third-party AI tool in functions like forecasting, this decay leads to flawed outputs. Real-time monitoring is essential, but this duty is often ceded to the vendor. Contracts must mandate performance reporting and define clear retraining thresholds.

Opacity and “Black Box” Risks

Many advanced AI systems function as “black boxes”: their internal logic is opaque. When a third-party AI denies a loan, regulators require clear explanations. The same applies if it flags a transaction. Yet you cannot outsource accountability. If you cannot justify an automated decision, you risk regulatory penalties. You may also undermine your organization’s reputation and stakeholder trust.

Nth-Party Cascading Failures

AI systems rely on complex vendor chains. Your service provider may rely on a fourth- or fifth-party for data or computational resources. A breach or failure at any link can cascade to your operations. This creates an amplified threat surface that is difficult to map manually.

Data Integrity and Privacy Issues

AI depends entirely on data. Risks are twofold. Training data may contain biases, leading to skewed outcomes. Your data submitted to a vendor’s AI may also be exposed or misused. You remain liable for breaches that occur at a vendor. Scrutinizing data governance and provenance is now non-negotiable.

Shadow AI

Shadow AI is the unauthorized use of generative AI tools by employees or vendor teams. For instance, an employee might paste proprietary code into a public chatbot. Again, a vendor might use a tool that is not certified to work on your confidential data. Such actions create blind spots for data leaks and compliance violations. Of course, addressing it requires clear policies, contractual controls, and ongoing monitoring.

Algorithmic Bias

AI has the potential to both propagate and escalate societal biases. Algorithmic bias results from the presence of systematic errors in machine learning that lead to unfair results. This is usually due to the learning data mirroring past disparities. For instance, an AI from a vendor that is engaged for hiring may put up barriers for certain demographic groups. The reason behind this is that it has been trained on past hiring data that does not correctly represent the current situation.

When left unchecked, such prejudicial outcomes carry severe consequences. Organizations may face legal liability and fines under regulations like the EU AI Act. Besides, they can suffer reputational harm and a loss of stakeholder trust.

Using AI as a TPRM Tool (AI for Risk)

Despite the risks posed by AI, the technology also presents an effective method for risk management. It transforms third-party risk from a manual process into a dynamic one. This approach transforms reactive management into an intelligent capability. Organizations can now efficiently anticipate and address risks before they escalate.

Hyper-Automation

Intelligent AI systems automate labor-intensive TPRM and other related tasks. It can pre-fill vendor questionnaires by analyzing public data and past assessments. Moreover, it can extract compliance evidence from vast volumes of documents.

This substantially reduces bottlenecks, letting risk teams focus on analysis of third-party risk and related insights. These tools cut vendor onboarding time significantly while maintaining compliance.

Continuous Monitoring of Vendor Risks

The traditional practice of periodic vendor reviews may not offer effective monitoring. AI technology makes it possible to monitor thousands of data points constantly. It tracks financial news, cybersecurity feeds, dark web activity, and regulatory sanctions. This provides real-time visibility, allowing proactive mitigation. Investing in AI-enabled third-party risk management software is a strategic necessity.

Predictive Risk Scoring

Machine learning identifies hidden patterns. By analyzing vendor history and external threats, AI can generate predictive risk scores. These scores forecast potential disruptions like cyber incidents weeks in advance. This turns risk management into a proactive and informed process.

Agentic AI

Goal-driven AI agents can be set to identify risks and respond independently. With parameters defined, these agents can notify a vendor about a risk of non-compliance. The agents can also auto-update a risk dashboard or trigger a review workflow score based on a new news event.

Nth-Party Visibility

AI-powered tools help map the extended digital supply chain. These tools reveal unknown interdependencies through various tiers of service vendors. Such broad coverage allows for unknown risks to be identified. For example, a primary vendor might have a second-tier supplier with weak cybersecurity measures. This is where true operational resilience requires a much deeper level of visibility.

AI Implementation and Management Best Practices in TPRM

You need a fresh strategic layout to get new systems rolling. These are backed by human expertise, procedures, and knowledge.

Update Vendor Contracts

Contracts must evolve to address AI specifically. Key clauses should include mandatory AI system disclosures. They should also secure the right to audit AI models and data practices. Clear service-level agreements for incident response related to AI failures are also critical.

Human-in-the-Loop

The efficiency of AI automation is not without the need for human oversight. The strategic decisions and ethical considerations still have to be made by responsible parties. AI will do the data processing, and the professionals will be available for in-depth analysis and relationship management.

Holistic TPGRC Approach

The trend is shifting from TPRM to the Third-Party Governance, Risk, and Compliance model. This integrates risk management across procurement, legal, finance, IT, and cybersecurity. It breaks down silos to deliver a unified view of vendor risk.

AI-Specific Due Diligence

Vendor questionnaires must include pointed questions about AI model design and training data. They should also cover bias testing and oversight mechanisms. Applying a risk-based tiering approach is effective. It ensures deep, technical reviews are reserved for vendors using high-risk AI.

The Current Regulatory Context for AI Governance

The regulatory landscape is moving from guidance to enforceable law. Compliance will fundamentally shape risk programs.

EU AI Act Enforcement

The EU AI Act will be fully in force by 2nd August 2026. It establishes a strict, risk-based regulatory framework. It mandates rigorous assessments for high-risk AI systems in finance, healthcare, and infrastructure. Companies will face substantial requirements for transparency and data governance. Non-compliance exposes companies to fines of up to €35 million or 7% of global annual turnover.

Digital Operational Resilience Act

For EU financial institutions, DORA took full effect on 1st January 2026. It demands real-time visibility into ICT third-party risk. It requires strict contracts and exit strategies for critical vendors. DORA emphasizes managing concentration risk. This applies directly to reliance on major AI providers.

US Federal Centralization

A 2025 executive order marked the beginning of a centralized federal approach to AI. The intention is to have a consistent national regulation that will handle the conflicting laws in different states. The direction is clear; federal oversight of AI safety and fairness is expanding. Enterprises must strengthen vendor oversight and ensure AI compliance with federal standards.

Conclusion

Given current challenges, third-party risk management is almost impossible without AI risk management. New sophisticated threats and an evolving regulatory mandate require intelligent strategies. To succeed, it is necessary to defend using AI while maintaining strict human governance. Such a balanced approach would ensure resilience within the digitally connected ecosystem.