AI Governance Is Only as Strong as Its Weakest Vendor

AI governance requires accountability, explainability and risk assessment, Christopher Mascaro, chief cyber and fraud officer at North, writes in a new PYMNTS eBook, “AI Runs Payments. Governance Decides What Happens Next.”

There is a gap in organizational AI governance: Who is accountable for AI outcomes, and who has the information and control needed to manage them? The board is accountable to shareholders and regulators, executives are accountable for strategic risk decisions. These decisions are then passed to operational teams who are accountable for day-to-day management of systems whose most consequential components were not developed by them.

Closing this gap requires a shared understanding across all levels of the organization. A grasp of what third-party AI risk looks like, why it demands dedicated governance and what role each level of leadership and operations plays in ensuring responsible outcomes. Without this shared understanding, accountability becomes diffuse and governance unreliable.

The AI Supply Chain and Risks

A manufacturer’s product quality depends on every supplier in its chain. An organization’s AI outcomes depend on every vendor in its ecosystem. A biased foundation model produces biased applications. An opaque vendor creates ungovernable dependencies. Vendor concentration creates systemic risk extending well beyond any individual deployment.

This supply chain lens clarifies the governance task: Map the AI supply chain, assess risk at each node, establish governance standards that apply consistently across the chain and build the monitoring capabilities needed for ongoing compliance. It also clarifies accountability. An organization cannot disclaim responsibility for a vendor model’s harmful output any more than a manufacturer can disclaim responsibility for a supplier’s defective component. The accountability follows the deployment, not the development.

Explainability as Shared Language

Model explainability provides the common language connecting technical, operational and strategic governance activities. For the board, explainability findings translate into clear, evidence-based reporting on whether AI systems operate within acceptable bounds. For risk functions, explainability provides the analytical basis for quantifying and managing vendor AI exposure with precision. For operational teams, explainability tools provide the real-time visibility needed to detect and respond to changes in vendor model behavior before they escalate into incidents.

Without explainability, each level operates with a different and incomplete picture. The board sees policies and compliance checklists but cannot verify system behavior. Risk teams see qualitative assessments but lack the data to quantify exposure accurately. Operational teams see model outputs but lack the interpretive tools to identify emerging problems. Explainability connects these fragmented perspectives into a coherent, actionable governance view that enables informed decision-making at every level.

A Shared Discipline

Model governance for third-party AI must be shared rather than siloed. The board sets expectations, allocates resources and holds the organization accountable for governance outcomes. Executive leadership establishes standards, invests in infrastructure and ensures vendor AI governance receives the same strategic attention as other enterprise risks. Operational teams execute governance processes that include conducting evaluations, maintaining monitoring, updating documentation and escalating issues when thresholds are breached.

The connective tissue is a model governance framework with clear roles, defined processes, standardized documentation and reporting cadences flowing from operations through leadership to the board. When accountability is distributed appropriately and supported by robust explainability and governance capabilities, the result is trust from all perspectives.