AI Is Reshaping Vendor Risk Management — Why Traditional Third-Party Governance Needs to Evolve.

For years, vendor risk management (VRM) programs were built around relatively stable software ecosystems. Organisations assessed vendors periodically, reviewed security certifications, signed Data Processing Agreements (DPAs), and maintained spreadsheets to track risk and remediation activities.

That operating model is increasingly struggling to keep pace with modern AI-driven environments.

Today, AI capabilities are being embedded into nearly every software platform. SaaS vendors introduce generative AI assistants, productivity suites deploy copilots, cybersecurity platforms rely on autonomous AI detection, and HR and customer service systems increasingly use AI-driven functionality.

These capabilities are often introduced rapidly and continuously, sometimes without organisations fully understanding how AI systems process data, introduce dependencies, or affect operational governance.

As a result, many organisations are struggling to maintain visibility into their AI supply chain.

Why AI Is Changing Vendor Risk Management.

In many organisations, AI adoption is no longer driven centrally. Business teams increasingly activate AI functionality directly within existing platforms because the functionality is already embedded and appears operationally low risk.

However, the underlying risk profile can change significantly.

A vendor that previously operated within a controlled SaaS environment may now:

• transfer prompts to external foundation models;
• involve additional subprocessors;
• process sensitive data in new jurisdictions;
• retain prompts or user inputs;
• rely on external AI infrastructure providers.

This introduces governance complexity that extends beyond traditional cybersecurity or privacy assessments.

Vendor risk management is therefore no longer only about assessing vendors themselves. Increasingly, organisations must understand the broader AI ecosystems operating behind those vendors.

For many organisations, this creates a growing gap between rapidly scaling AI adoption and existing governance capabilities. GRCPerfect will explore this challenge further during its upcoming webinar on Operational AI Governance under the EU AI Act .

Why Traditional Vendor Assessments Struggle to Keep Up.

Most third-party risk management programs still rely heavily on periodic reviews, static questionnaires, spreadsheet-based tracking, and point-in-time assessments.

These methods were designed for environments where software changed incrementally. AI ecosystems evolve far more dynamically than traditional software environments.

A vendor assessed six months ago may now rely on entirely new AI providers or introduce AI functionality that significantly changes its risk profile.

Many organisations only discover these changes after deployment, or after operational or compliance concerns emerge.

As a result, traditional governance processes increasingly struggle to maintain:

• continuous visibility;
• AI-specific oversight;
• centralized inventories;
• cross-functional accountability.

What Are the Biggest AI Vendor Risks?.

As organisations expand their use of AI-enabled vendors, several governance risks are becoming increasingly important.

How the EU AI Act Impacts Third-Party Governance.

The regulatory environment around AI and third-party governance is evolving rapidly

The EU AI Act.

The EU AI Act introduces obligations related to:

• risk management;
• transparency;
• human oversight;
• technical documentation;
• governance;
• post-market monitoring.

Importantly, organisations do not need to develop AI systems themselves to be affected.

Obligations may depend on an organisation’s role within the AI value chain, including whether it acts as a provider, deployer, importer, distributor, or authorized representative.

The European Commission has increasingly emphasized that organisations deploying AI-enabled systems may also carry governance and accountability obligations under the EU AI Act, even when the underlying AI models are provided by third parties.

As a result, organisations increasingly need stronger visibility into how AI is embedded across vendor ecosystems, how data is processed, and which governance controls are in place around AI-enabled services.

This means vendor governance processes increasingly need to evaluate:

• AI governance maturity;
• model transparency;
• data usage practices;
• accountability mechanisms;
• operational oversight controls.

NIS2 and Supply Chain Security.

NIS2 significantly increases focus on supply chain resilience and third-party cybersecurity risk.

Organisations are expected to demonstrate stronger oversight over critical suppliers, outsourced services, operational dependencies, and ICT risk management. Similar governance expectations are increasingly reflected across broader security and resilience frameworks such as ISO 27001 .

AI integrations complicate this further because they introduce rapidly evolving external dependencies into business-critical systems.

DORA.

Under DORA , financial institutions must strengthen oversight of ICT third-party risk, including concentration risk and operational resilience.

As financial vendors increasingly adopt AI-driven functionality, organisations must assess operational dependency on AI providers, resilience of AI-enabled services, and governance around automated decision-making.

GDPR and AI Governance.

AI also introduces additional GDPR governance considerations, including lawful basis for AI processing, automated decision-making, transparency obligations, international data transfers, and data minimization.

Many existing DPAs were not originally designed to address the complexity of modern AI processing activities and evolving AI supply chains.

As AI adoption accelerates, organisations increasingly need governance models that extend beyond traditional contractual controls.

Building Continuous AI Governance.

Managing AI-driven third-party risk requires organisations to move beyond periodic vendor assessments and fragmented governance models.

As AI ecosystems evolve continuously, governance must become more operational, integrated, and continuous by design.

AI governance can no longer be treated as a one-time compliance exercise. Frameworks such as the NIST AI Risk Management Framework (AI RMF) increasingly position AI governance as a continuous operational capability focused on accountability, oversight, and resilience.

Organisations that fail to modernize vendor governance may increasingly struggle to maintain control across rapidly evolving AI ecosystems.

How GRCPerfect Helps Organisations Modernize Vendor Governance.

From AI compliance to operational AI governance.

GRCPerfect helps organisations move beyond fragmented compliance activities toward a more structured and operational approach to managing AI-driven vendor risk.

Instead of assessing AI , privacy , security , third-party risk , and compliance in separate tools or siloed processes, GRCPerfect brings these governance domains together within one integrated system.

This enables organisations to strengthen oversight of AI-enabled vendors, understand third-party dependencies, and manage evolving governance obligations across frameworks such as the EU AI Act, GDPR, NIS2, DORA, and ISO 27001.

As AI becomes embedded across vendor ecosystems, organisations need governance models that connect vendor risk management with privacy, security, risk, and compliance — continuously and operationally.