How GRC teams can assess AI tools in third-party risk management

Third-party ecosystems have exploded in recent years, introducing both opportunity and exposure to enterprises. Once a niche within the broader risk landscape, third-party risk management (TPRM) has exploded onto the proverbial spotlight. Governance, risk, and compliance (GRC) managers struggle to oversee hundreds, even thousands, of vendors, each with unique security profiles and dependencies.

Enter AI third-party risk management, and the outlook shifts. Today, AI is powering practical, measurable improvements in TPRM. From real-time risk scoring to continuous monitoring, advancements in AI help GRC teams make faster, smarter decisions with their data at a scale unimaginable just a year ago.

Why third-party risk needs AI now

GRC teams are tasked with managing and governing third-party relationships throughout their lifecycle. But growing regulatory demands, evolving risks, and the rise of third-party AI risk have made those jobs increasingly complex.

Today’s TPRM leaders must integrate these new challenges into their strategic risk management approach in a fast-moving ecosystem defined by:

• More third-party vendors to manage than ever
• Artificial intelligence, including third-party AI risk, is entering the picture (in a big way)
• Increasingly complex nth-party relationships, stakeholder relationships, and vendor contracts
• Constantly evolving regulatory requirements in response to potential risks

Regulatory, legal, and compliance requirements evolve

As third-party ecosystems grow more complex, governments and industry bodies are tightening and expanding oversight. Regulations like the EU’s Digital Operational Resilience Act (DORA), GDPR, and NIS2 Directive, along with US frameworks such as the SEC’s cybersecurity disclosure rules and FTC safeguards, are setting stricter expectations for how organizations manage vendor relationships and protect shared data.

These changes stem from high-profile third-party breaches that have disrupted operations, exposed sensitive information, and triggered costly compliance investigations. Supply chain attacks, data leaks from compromised vendors, and insecure integrations have all forced companies to reassess their third-party risk management programs from end to end.

AI governance is also under scrutiny. The EU AI Act and proposed US AI accountability frameworks require transparency into how AI systems, especially third-party tools, are sourced, trained, and monitored. For GRC teams, keeping pace means continuously updating policies, audits, and vendor assessments to meet shifting global standards.

Third-party service adoption continues to grow

Businesses now rely on third-party providers for more functions than ever, from core operations to specialized services. As this reliance deepens and new tools and technologies enter the mix, risk and compliance teams are finding it harder to conduct the necessary due diligence and manage ongoing risk assessments.

The TPRM lifecycle itself has become more complicated, spanning due diligence, onboarding, monitoring, incident response, regulatory compliance, and eventually offboarding. Internal stakeholders are often cross-functional and have different needs, and relationships with third-party vendors can be just as complex as the risks they pose.

In short, third-party risk is growing exponentially, requiring organizations to adopt smarter tools and stronger methodologies to keep pace.

AI poses new risks

AI has created a new layer of complexity for third-party risk management. Generative AI and other systems raise major concerns around data privacy, cybersecurity, and overall governance. Yet despite these risks, adoption is accelerating across nearly every industry.

Many large organizations are actively encouraging AI use, which means GRC teams can’t afford to ignore it. TPRM programs must now account for new kinds of AI-related exposure in some of their highest risk domains, including:

• Cyber risks
• Data privacy risks
• Environmental, social, and governance (ESG) risks
• Regulatory, legal, and compliance risks
• Vendor and even nth-party risks

Ironically enough, addressing these challenges requires a more adaptive, AI-aware approach to TPRM.

Where AI is making an impact today

AI is already reshaping how organizations manage third-party risk through practical, measurable improvements. Across vendor evaluation, monitoring, and contract management, AI is helping governance, risk, and compliance teams handle more data, spot issues faster, and make decisions with greater confidence.

Together, the following applications show that AI third-party risk management isn’t about replacing human judgment — it’s about amplifying it, making every stage of the process faster, smarter, and more informed.

Vendor risk scoring

Traditionally, vendor assessments relied on periodic reviews and manual scoring. AI now makes that process dynamic. By analyzing vast datasets, from security ratings to threat intelligence feeds, AI can continuously score vendor risk in real time. This allows teams to identify emerging vulnerabilities earlier and focus their attention on higher priorities.

Continuous control monitoring

Instead of relying on static audits, AI-driven tools can monitor controls and compliance indicators continuously. These systems flag deviations or anomalies automatically, giving organizations early warnings of potential security or compliance issues. The result is a more proactive, data-driven approach to managing third-party risk throughout the vendor lifecycle.

NLP for contract review

Natural language processing (NLP) is taking the pain out of contract management. AI can rapidly scan and interpret lengthy vendor agreements to flag key clauses, missing terms, and potential compliance gaps. It helps risk and legal teams cut through volume and complexity, reducing the chance of oversight while speeding up review cycles.

Limitations and pitfalls to watch out for

While it can be tempting to leave everything to AI, it’s crucial to understand the limitations of AI solutions. Artificial intelligence, generative AI, large language models (LLMs), and machine learning are all designed to support human work, not replace it. Without a clear understanding of your organization’s needs or proper AI governance, poor implementation can create new risks instead of mitigating them.

Data dependency

AI systems are only as strong as the data behind them. Low-quality or incomplete data can cause even the most advanced models to produce inaccurate or misleading results. Garbage in, garbage out. Before committing to an AI-driven TPRM solution, make sure its data sources are reliable and its training methods are sound.

Model explainability

The effectiveness of any AI solution depends on how transparent its underlying model is. Users should be able to evaluate the model’s methodology, data quality, and training process with reasonable assurance. Be cautious of “black box” models that lack transparency or make it difficult to understand how conclusions are reached.

Lack of human oversight

No matter how advanced an AI solution is, it should never operate without human oversight. AI outputs must be reviewed, validated, and acted upon by people who understand the business context. A layer of human judgment is essential to ensure accuracy, accountability, and trust throughout the TPRM workflow.

How to evaluate AI-powered TPRM solutions

With AI third-party risk management solutions promising the world, it’s important for risk and compliance stakeholders to approach AI solutions with clear eyes and an operational, risk-based mindset. The goal is to understand what the technology truly delivers and ensure it aligns with your organization’s governance and operational needs.

Is it true AI or rules-based?

Many tools marketed as “AI” are actually rules-based automation systems. Ask vendors to clarify how their technology works, what models it uses, and how those models are trained, validated, and monitored for accuracy, ethics, and transparency. True AI should demonstrate measurable intelligence and adaptability, not just workflow automation.

Can it integrate with your workflows?

Even the most advanced AI won’t help if it doesn’t fit your environment. Look for solutions that enhance efficiency by integrating seamlessly with your existing TPRM workflows, systems, and data sources. The right AI tool should simplify, not complicate, your enterprise’s comprehensive risk management plan and processes.

Does it provide clear audit trails?

Transparency and accountability are essential. Reliable AI tools must offer complete audit trails that log decisions, model outputs, and data lineage in real time. Vendors should also be prepared to complete privacy and security questionnaires and provide assurance about their controls and disclosures.

More to consider when evaluating AI vendors

Here are some other questions to consider when evaluating AI vendors and related AI risks:

• What AI models and algorithms are used to power the solution? Are these AI models reliable, ethical, accurate, and transparent?
• Are vendors and third parties with AI-enabled solutions willing to answer data privacy and security questionnaires and provide meaningful assurance about their controls and disclosures?
• Do these AI systems provide reliable audit trails for logging and real-time monitoring?
• How do AI solutions fit into existing workflows and operations at the organization?
• Does the selected AI solution meet the company’s needs and address the problem?

AI tools should go through the same TPRM lifecycle, due diligence, questionnaires, and onboarding processes and controls as other third-party providers. They may need additional scrutiny and risk assessment due to the novelty and potential impact of generative AI. GRC teams may want to consider developing risk assessments tailored towards evaluating AI-powered tools.

Organizations may also want to audit AI tools already in use by employees and workforce members in an unofficial capacity. These “shadow AI” tools can introduce vulnerabilities and unidentified or undetected risks to an organization.

Improve efficiency with AI third-party risk management

AI third-party risk management is essential, yet many organizations still juggle spreadsheets, project management software, and disconnected systems to track and assess vendors. Equipping teams with purpose-built tools to automate due diligence, questionnaires, risk mitigation, and the overall TPRM lifecycle enables them to deliver insights rather than being buried in manual reviews.

Ready to discover what’s possible for your governance, risk, and compliance teams? See how Optro helps scale your TPRM workflows and connects to AI-ready data sources with a free demo.

About the authors

Madison Dreshner, CISA, is a Manager of Compliance Solutions at Optro. Madison joined Optro from PwC, where she specialized in external reporting for a wide array of clients, including SOC 1 & 2 reporting, as well as SOX compliance. Connect with Madison on LinkedIn.