AI and Third Parties: How to Hold Vendors Accountable

Summary

This IANS Research guideline tackles a critical blind spot in AI governance: how to ensure your vendors aren't undermining your AI policies through their own AI implementations. Rather than offering generic vendor management advice, this resource provides specific questions, requirements, and due diligence frameworks tailored to AI risks. It bridges the gap between internal AI governance and external vendor relationships, helping organizations extend their AI accountability beyond their own walls.

The Vendor AI Blind Spot

Many organizations invest heavily in developing internal AI policies and governance frameworks, only to discover their vendors are using AI systems that create compliance gaps, security vulnerabilities, or reputational risks. This resource addresses three key scenarios where vendor AI use creates organizational risk:

• Hidden AI implementations: Vendors using AI in their services without disclosure
• Data flow complications: AI systems processing your data in unexpected ways
• Policy misalignment: Vendor AI practices that conflict with your governance standards

The guide emphasizes that traditional vendor risk assessments often miss AI-specific considerations, requiring new approaches to due diligence and ongoing monitoring.

Essential Questions for Vendor AI Accountability

The resource provides a structured question framework organized around five critical areas:

AI Disclosure and Inventory

• Does the vendor use AI in any capacity for our services?
• What specific AI systems, models, or algorithms are involved?
• Are there plans to implement AI in the future?

Data Handling and Privacy

• How does AI processing affect data residency and cross-border transfers?
• What training data was used, and does it include similar data to ours?
• How is our data isolated from AI training or improvement processes?

Risk Management and Controls

• What AI governance framework does the vendor follow?
• How do they handle AI bias, fairness, and explainability requirements?
• What incident response procedures exist for AI-related issues?

Contractual Requirements That Work

Beyond asking questions, the guide outlines specific contractual clauses and requirements that create enforceable vendor accountability:

Mandatory disclosure requirements for any AI use, including notification periods for new implementations. Data flow documentation that maps exactly how information moves through AI systems. Compliance alignment clauses that require vendors to meet your organization's AI policy standards.

The resource emphasizes making these requirements operational rather than just legal checkbox exercises, with clear metrics and review processes.

Who This Resource Is For

Vendor management teams looking to update their due diligence processes for AI-related risks. Procurement professionals who need practical language for AI-related contract negotiations. Risk and compliance officers responsible for extending organizational AI policies to third-party relationships. Legal teams drafting or reviewing vendor agreements that involve AI systems.

The guidance is particularly valuable for organizations in regulated industries where AI governance requirements must flow through to vendor relationships.

Implementation Roadmap

The resource suggests a phased approach to implementing vendor AI accountability:

Phase 1: Inventory existing vendors and identify those likely using AI Phase 2: Deploy the question framework to high-risk vendor relationships
Phase 3: Update standard contract templates with AI-specific requirements Phase 4: Establish ongoing monitoring and review processes

Each phase includes specific deliverables and success metrics, making the guidance immediately actionable rather than aspirational.