Is AI Hiding In Your Supply Chain?

Why AI Governance Matters Now

Without proper oversight, financial institutions are flying blind – and that’s risky.

AI is increasingly embedded within third-party services, often operating in the background without visibility or control. This creates unknown exposures – where decisions are made, data is handled, and risks evolve – often without anyone realising… until it’s too late.

As AI adoption accelerates, visibility into how it’s used by suppliers is no longer optional. It’s becoming a cornerstone of responsible supply chain governance.

The Risk Landscape: What to Watch For

Even if your organisation isn’t using AI, your suppliers almost certainly are.

Regulators are already paying close attention to several key risk areas, including:

• Data privacy – AI often processes large volumes of personal data, raising compliance questions around protection and usage.

• Accountability – If a decision is made by an AI model, who’s ultimately responsible?

• Bias – AI models can inherit and obscure biases present in their training data.

• Explainability – Generative AI often functions as a “black box,” making it hard to justify decisions in regulated environments.

Without a structured framework, AI-related risks are harder to identify – and even harder to manage – leaving organisations exposed to blind spots deep within third-party processes.

The Challenge of Ownership

A major challenge for many organisations is assigning ownership for AI risk.

Unlike cybersecurity or privacy, AI doesn’t have a “natural home” in most businesses.

Is it a privacy issue? A data governance issue? A cyber issue? Or does it sit with procurement or compliance?

This lack of clarity can create friction – and risk.

While ownership may still be evolving, waiting for perfect clarity could leave firms exposed.

Identifying accountability – whether within one function or shared across teams – is a crucial step toward building effective AI governance.

Managing AI Risk Across the Supplier Lifecycle

Managing AI risk isn’t about a single checkpoint – it should be woven into every stage of the third-party lifecycle:

• Early-stage due diligence – Ask directly whether AI is being used and review related policies.

• Contracting – Include clauses that govern AI use, with notification requirements and risk controls.

• Ongoing monitoring – Revisit any gaps flagged during onboarding and ensure they’re addressed.

• Termination – Plan ahead for data return and clarity on any AI model training IP.

AI can range from obvious tools like ChatGPT to quietly embedded functions within outsourced processes – both carry risks that need oversight.

How FSQS Helps You Stay in Control

The good news: you don’t have to build your AI oversight process from scratch.

FSQS, trusted by leading financial institutions, already supports key steps in managing AI risk during supplier onboarding and monitoring.

By embedding targeted AI questions, FSQS helps you:

• Identify where AI is used across the supplier lifecycle

• Assess governance maturity and readiness

• Strengthen oversight without adding unnecessary burden

This turns AI governance from a reactive scramble into a proactive, consistent, and scalable process.

Looking Ahead

AI is already reshaping how services are delivered and received – and that impact will only accelerate.

From governance frameworks to due diligence and monitoring, this shift demands both awareness and action.

By staying informed and engaging proactively with suppliers, you can help ensure AI innovation is underpinned by responsible, transparent, and secure practices.