AI’s hidden third-party risks: What middle market leaders must know

Most middle market organizations have little to no idea how much artificial intelligence is already operating inside their vendor ecosystem. Even a trusted, well-audited third-party relationship can quietly introduce AI, sometimes without notification, creating governance gaps that traditional vendor risk frameworks were never designed to catch.

As AI adoption accelerates through both embedded capabilities and purpose-built platforms, leaders need to identify where AI enters their technology ecosystem, understand how vendors use and manage it and integrate AI-specific considerations into existing third-party risk management (TPRM) processes.

Strengthening AI-specific due diligence, establishing contractual guardrails, mapping fourth- and Nth-party dependencies, and shifting from periodic reviews to continuous monitoring, supported by tailored key performance indicators (KPIs) and key risk indicators (KRIs), can provide critical real-time visibility into evolving AI risk.

Organizations are facing increasing pressure from boards, investors and customers to demonstrate an AI strategy. As many turn to AI-enabled solutions already embedded in their technology ecosystems, the challenge becomes not whether AI is present, but whether governance and risk management processes have evolved quickly enough to keep pace. 

The evolving enterprise AI risk landscape

AI is reshaping every major domain across the enterprise, often faster than existing governance frameworks can keep pace, creating new challenges for how leaders identify, manage and monitor risk.

Key areas of exposure include:

• Strategic risk: AI-driven shifts in competitive dynamics, cost structures and business models outpace existing strategies and planning cycles.
• Financial risk: Inefficient model usage, unoptimized infrastructure and reliance on costly vendors erode the value of AI investments.
• Operational risk: AI integration into core processes increases error rates, creates inconsistent outputs and raises support demands.
• Compliance and regulatory risk: Evolving AI requirements expand compliance obligations and expose organizations to greater regulatory scrutiny.
• Legal and liability risk: AI-generated recommendations, decisions and content create new forms of legal exposure and liability.
• Data risk: AI dependence on large volumes of data magnifies existing challenges related to data quality, lineage, privacy and data leakage.
• Cybersecurity risk: AI-driven attack surfaces increase exposure to prompt injection, model manipulation, data extraction and third-party vulnerabilities.
• People and culture risk: Skill gaps, inconsistent adoption and potential misuse hinder effective AI implementation.
• Supply chain and third-party risk: AI-enabled ecosystems with external models, data providers and vendors extend risk across the broader supply chain.
   

Embedding AI governance into vendor risk

Traditional TPRM programs were designed for static vendor relationships, with up-front due diligence followed by periodic monitoring. 

However, AI changes those assumptions: models are continuously updated, retrained and prone to behave differently over time, requiring ongoing monitoring of both performance and risk. AI ecosystems are also more interconnected, spanning model developers, data providers and underlying infrastructure. As a result, traditional TPRM frameworks do not provide the continuous oversight and technical transparency that AI requires.

Middle market organizations should strengthen AI governance by addressing AI risk at each stage of the TPRM lifecycle, including:

However, there’s often a hidden governance gap: AI can enter through an existing vendor without an organization ever realizing it, creating a governance gap that traditional TPRM controls may not catch.

For example, a regulatory technology vendor supporting anti-money laundering transaction monitoring could replace its rule-based detection engine with a large language model without notifying the organization, leaving it relying on AI within a critical compliance process it never approved or validated.

This breakdown isn’t intentional and typically stems from differing views on what qualifies as a material, disclosable change. However, the consequences can be significant: a potential regulatory finding, reputational damage and considerable time lost investigating an issue that should have been visible from the start.

Building accountability into AI adoption

Clear, integrated ownership throughout the AI lifecycle strengthens governance and reduces third-party risk. Without this accountability, organizations risk losing visibility into how AI vendors and solutions evolve over time.

Key steps include:

• Define ownership: Use cross-functional workshops and a responsible, accountable, consulted, informed (RACI) model to assign clear roles at each stage of the AI lifecycle, from intake through decommissioning.
• Integrate use case intake: Fold TPRM into AI use case intake to capture third- and fourth-party risks when scoring AI models and use cases.
• Build collaboration: Build cross-functional capabilities to assess both technical security and ethical AI risks when evaluating new vendors or technologies.
• Balance oversight: Pair centralized oversight with business-level decision making on AI and vendor use within each function's area of responsibility.
   

Best practices for evaluating third-party AI risks

To effectively manage AI risk exposure, rigorously evaluate third-party vendors by taking the following key measures to safeguard data, operations and compliance:

Using KPIs and KRIs to monitor AI risk

KPIs and KRIs play different but complementary roles in AI vendor oversight. KPIs help organizations understand whether their AI risk management processes are operating as designed by reviewing, for example, the percentage of AI-enabled vendors that have completed an AI-specific risk assessment.

KRIs, by contrast, provide early warning signs that risk may be increasing by looking at unexpected model behavior, unresolved vendor control gaps, concentration across common fourth parties, or changes in how vendors use organizational data. Reporting both types of metrics can help leaders move beyond one-time assessments and maintain visibility as AI capabilities continue to evolve.

Strengthening due diligence for AI vendors

Rather than creating a parallel AI review, integrate AI-specific considerations into existing TPRM processes, gates and handoffs by evaluating key aspects of a vendor’s AI and machine learning system, including:

• Tools and methodologies used for development and implementation
• Training data collection, management and validation
• Processes for continuous performance monitoring
• Evaluation and management of bias, fairness and ethical considerations
• Security and privacy measures protecting the system and its data
• Alignment with industry standards and regulatory requirements
• Human oversight and role in AI-supported decision making
• Training and skills required across the system’s lifecycle

AI vendor contracts should establish nonnegotiable requirements for data use, security, model changes, audit rights and subcontractor controls. Defining these requirements up front helps maintain control over organizational data, model behavior and broader AI supply chain risk.

In addition, it is important to map fourth- and Nth-party providers across critical and high-risk AI vendors to understand dependencies, data access and concentration risk.