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Organizations increasingly rely on third parties and fourth parties to deliver critical business services. As those suppliers adopt AI across their operations, they introduce new governance, regulatory, operational, and reputational risks that extend beyond your direct control.
This challenge is only accelerating. In 2025, 88% of organizations reported using AI in at least one business function, and that adoption continues to grow. As AI becomes embedded across the enterprise, it inevitably extends into the third-party ecosystem through suppliers, service providers, and technology partners.
While AI governance, risk, and compliance are enterprise-wide responsibilities, third-party AI requires a different approach. Organizations need visibility into how suppliers use AI, what risks those systems introduce, and whether appropriate controls are in place throughout the vendor lifecycle.
This is where third-party risk management becomes a critical enabler of AI governance. By extending governance, risk, and compliance practices into the third-party ecosystem, an approach often described as AI for extended GRC, organizations can assess supplier AI, monitor evolving risks, and demonstrate compliance with emerging regulations.
In this guide, we’ll explore how AI governance and TPRM work together to help organizations manage AI risk across their extended enterprise.
Every one of your suppliers, software providers, outsourcing partners, or contractors may be adopting AI in ways that directly or indirectly affect your business.
These third parties’ use of AI becomes part of your overall risk landscape, even if you don’t control how those systems are developed or governed.
This is an often-overlooked threat, which causes the following challenges:
If your vendors provide AI-enabled products or services, they need to fully disclose how their models work, what data they use, how decisions are made, or what controls are in place. Without this information, you can’t assess risk or demonstrate compliance.
Each supplier may have a different approach to AI governance. Some have mature policies and oversight mechanisms. Others have little formal governance in place. You need to take these inconsistencies into account when designing your processes.
Traditional supplier due diligence processes were not designed to evaluate AI systems. You must consider factors such as model governance, bias mitigation, human oversight, data provenance, security controls, and regulatory compliance. If you don’t have a standardized assessment framework to refer to, these can be easily missed.
Even if a supplier appears well-governed, it may rely on external AI providers, cloud services, foundation models, or subcontractors. This creates fourth-party risk that can be difficult to identify and monitor, leaving your organization exposed to risks deeper within the supply chain.
As AI adoption accelerates, these challenges become increasingly difficult to ignore. You need a structured way to identify which suppliers use AI and understand the associated risks. Then, you must continuously monitor those risks as supplier technologies evolve.
AI adoption is here to stay, as we mentioned earlier. Just as your organization assesses vendors for cybersecurity, privacy, financial stability, and regulatory compliance, it should also evaluate how suppliers develop, deploy, and govern AI.
This means incorporating AI governance into your broader third-party risk management program rather than treating it as a separate initiative.
Your organization should assess:
These assessments help your organization better understand which suppliers present elevated AI risk. This knowledge allows you to put appropriate governance controls in place before those risks affect your business.
Conducting these assessments manually can be challenging, particularly if you’re managing hundreds or even thousands of third-party relationships.
This is where AI-powered TPRM solutions help.
These tools automate supplier due diligence by reviewing questionnaires, policies, certifications, and supporting documentation. They make it easier for you to identify gaps or inconsistencies and flag areas requiring further investigation.
Intelligent risk scoring helps prioritize high-risk vendors. You can then focus resources on those that present the greatest threat. The tools also make it easier to continuously monitor these suppliers. They can even analyze a wide range of internal and external data sources to identify emerging risks and changes in profiles.
Beyond risk management, AI-powered tools improve regulatory compliance by mapping supplier controls and practices against regulatory requirements. This makes it easier for you to identify compliance gaps and create a plan to fill them, thus maintaining audit readiness.
Ultimately, as AI adoption continues to expand across third-party ecosystems, organizations need a scalable approach to supplier governance. AI-powered third-party risk management provides the visibility and continuous oversight needed to identify risks earlier and strengthen AI GRC across the extended enterprise.
AI, while advantageously transforming the way businesses operate, can create risks that traditional governance frameworks were not designed to address.
These AI-specific risks include:
These risks don’t disappear simply because the AI belongs to a supplier. In many cases, your organization remains accountable for the outcomes of services delivered on your behalf. As such, supplier AI governance should be an important component of your enterprise risk management.
You can lean on the new requirements that governments and regulators around the world are introducing. Regulations and frameworks for AI accountability and risk management, like the EU AI Act and NIST AI RMF lay out your responsibilities and guidance on how to implement them. Strong governance mechanisms can mitigate AI-related risks while demonstrating compliance with evolving regulations.
Rather than creating separate governance programs for internal and supplier AI, you can extend existing third-party risk management processes to include AI governance, risk assessments, and compliance activities. This creates a more scalable and consistent approach to managing AI risk wherever it exists.
AI governance, risk management, and compliance are often discussed as separate disciplines. In practice, however, they’re most effective when working together as part of your organization’s broader third-party risk management strategy.
AI governance is the process of enforcing policies and guardrails that help ensure AI is safe and ethical. Its purpose is to align AI initiatives with organizational objectives while managing risks related to ethics, compliance, security, privacy, and operational performance.
Within third-party risk management, governance means ensuring your suppliers have appropriate policies, oversight mechanisms, accountability structures, and controls for the AI systems they develop or use.
You can typically implement AI governance through a structured framework. This helps you, and them, maintain transparency, accountability, fairness, and regulatory compliance while reducing the likelihood of unintended outcomes.
More focused than AI governance, AI risk management is the process of systematically finding and addressing any potential risks associated with artificial intelligence technologies (e.g., data poisoning, use of sensitive data, and algorithmic bias).
In a supplier context, AI risk management means identifying where vendor AI could introduce security, operational, compliance, or reputational risks to your organization and determining whether appropriate mitigation measures are in place.
The goal is to identify potential negative impacts of the vendors’ AI implementation and actively create systems to mitigate them.
AI compliance ensures AI systems are designed, deployed, and managed in alignment with applicable laws, regulations, industry standards, and ethical principles.
These regulations provide the oversight needed to keep AI within responsible boundaries, ensuring it’s transparent and safe in order to protect the interests of customers and employees.
You may think it’s similar to general data compliance, but rather than controlling how data is collected, stored, and protected, AI compliance governs how the system makes decisions.
For third-party risk teams, compliance also means verifying that suppliers meet applicable AI regulations and internal governance requirements before AI-related risks become your organization’s responsibility.
So, what are the regulations, standards, and frameworks shaping AI compliance?
The number is increasing rapidly, with over 1,000 AI-related policy initiatives proposed across 72 countries. Currently, the main ones include:
This law governs the development and/or use of AI in the European Union (EU). It’s considered the world’s first comprehensive regulatory framework for AI and takes a risk-based approach to regulation, applying different rules to AI according to the threats they pose.
The four broad categories are
The idea is that the level of regulatory oversight required increases alongside the level of risk. For example, for organizations using or developing high-risk AI systems, the Act introduces requirements around risk management, data governance, human oversight, transparency, documentation, and ongoing monitoring.
For organizations managing third parties, this means understanding whether suppliers develop or use AI systems that fall into regulated categories, and whether they have the appropriate controls and documentation in place to demonstrate compliance.
Developed by the U.S. National Institute of Standards and Technology (NIST), this is a framework rather than a law. It provides voluntary guidance for identifying and managing AI-related risks.
The framework is built around 4 functions:
The NIST AI RMF highlights that businesses should understand how their suppliers are using AI and the risks this may introduce. This means knowing where third-party AI is involved and checking that appropriate safeguards are in place. It also states you should keep oversight of those risks over time.
ISO 42001 is the first international standard for Artificial Intelligence Management Systems (AIMS). It provides organizations with a structured framework for governing AI responsibly throughout its lifecycle. By achieving the ISO 42001 certification, your company demonstrates that your organization’s AI management system has been independently assessed against the requirements of the standard, supporting responsible and trustworthy AI governance.
The standard includes several key components:
You should look for evidence that suppliers align with these standards, especially when you are in a heavily regulated industry or work with sensitive information.
Establishing an effective GRC framework for third parties can be a significant challenge. You can’t continuously monitor their AI systems to assess emerging risks, or check if they’re adapting to regulatory changes.
These responsibilities can quickly become difficult to manage through manual processes alone, and so this is where artificial intelligence plays a supporting role.
When adopted responsibly, using AI for risk and compliance, including TPRM, helps automate processes like risk assessments and compliance activities, identify anomalies, and surface insights that enable you to be more proactive.
Here are some core components of a strong TPRM GRC framework, the implementation of which can be enhanced by AI:
You can’t govern AI if you don’t know which systems are in use within your organization. That’s why an inventory is the first step in GRC, helping to maintain visibility into all AI systems being developed, deployed, or procured, along with their purpose, data sources, owners, and risk profiles.
The same principle applies to third-party risk. You first need visibility into which suppliers are using AI, where AI supports critical services, what data those systems access, and how much risk they introduce.
Risk classification helps prioritize supplier oversight by considering factors such as:
AI-powered TPRM platforms can automate much of this discovery process by identifying supplier AI usage, classifying vendors according to risk, and maintaining an up-to-date inventory across the third-party ecosystem.
One of the main concerns surrounding AI is ethical and fair use. Without proper oversight, AI can introduce unintended consequences, such as biased hiring decisions, discriminatory lending outcomes, inaccurate recommendations, or opaque decision-making that is difficult to explain or challenge.
When these risks originate within a supplier, they can still affect your organization through poor customer experiences, regulatory scrutiny, or reputational damage.
To reduce these risks, organizations should establish clear principles and controls around fairness, transparency, human oversight, accountability, privacy, and security.
Supplier assessments should therefore examine whether vendors have appropriate governance policies, testing procedures, human review processes, and documented controls for responsible AI use.
If you hold your vendors accountable for their decisions, they are more likely to monitor their AI models more stringently, because they don’t want to lose your business.
AI can support this process by monitoring outputs for bias and flagging any anomalies, which are tasks that ordinarily are exposed to human error when conducted manually.
AI systems are anything but static; their performance and accuracy, along with their risk levels, change over time as data, user behavior, and external conditions evolve.
The same is true for suppliers. Vendors frequently introduce new AI capabilities, adopt different foundation models, expand AI into new business functions, or change the way customer data is processed.
A supplier that presented minimal AI risk during onboarding may become significantly higher risk within months.
This constant vigilance is difficult to maintain manually, though, particularly as the number of AI models you or your suppliers use increases.
To avoid team overwhelm and a lack of oversight, AI GRC tools and TPRM platforms can provide automated 24/7 monitoring, automated alerts, anomaly detection, and performance analysis.
AI governance isn’t effective without ownership and control, which is why defined policies and assigned roles are a vital component of a GRC framework.
This includes:
For third-party programs, governance should also establish clear expectations for suppliers. Contracts, procurement processes, and vendor onboarding should define how AI may be used and what disclosures are required, as well as what evidence suppliers must provide to demonstrate responsible AI governance.
AI can support by automating approval processes and conducting control testing, as well as monitoring for policy compliance.
Your business must ensure AI systems comply with relevant regulations and industry requirements.
As such, maintaining documentation and audit trails is becoming increasingly important as regulatory scrutiny grows.
The same applies to supplier AI. You increasingly need evidence that vendors are meeting regulatory expectations and internal governance requirements regarding AI usage.
AI-powered governance platforms simplify this process by automatically collecting supplier evidence, mapping controls to regulatory frameworks, identifying compliance gaps, and maintaining audit-ready documentation across the vendor lifecycle.
This reduces manual effort while giving organizations greater confidence that supplier AI risks remain visible and well-governed.
AI is no longer confined to your own organization and has become another important aspect of third-party risk.
Managing these risks requires more than traditional vendor assessments. You need ongoing visibility into how suppliers are using AI, the governance controls they have in place, and how their AI risk evolves over time. That’s why AI governance, risk, and compliance should be an integral part of every modern third-party risk management program.
By embedding AI GRC into TPRM, you can evaluate supplier AI more effectively, strengthen due diligence, continuously monitor emerging risks, and stay ahead of an evolving regulatory landscape, all while building greater trust across your third-party ecosystem.
This is where Aravo helps.
Built on Aravo’s Intelligence First™ approach, the platform provides a complete third-party risk management solution, while also supporting governance, risk, and compliance activities together in one place. This allows you to manage supplier AI risk alongside cybersecurity, privacy, ESG, resilience, and regulatory compliance.
With automated workflows, AI-powered risk insights, continuous monitoring, and audit-ready reporting, Aravo gives your teams the visibility they need to identify supplier AI risks earlier and make better risk decisions, scaling governance as AI adoption continues to accelerate.
Ready to see how Aravo can help you manage AI risk across your third-party ecosystem? Request a Demo
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