AI supply chain risk: The hidden danger facing financial services

When artificial intelligence first exploded onto the business scene, organisations naturally turned their focus inward. Management teams rushed to manage internal risks by implementing acceptable use policies, establishing technical guardrails, and restricting staff to approved tools like Microsoft Copilot.

However, this internal preoccupation created a significant blind spot: AI supply chain risk. While businesses were busy policing their own employees, few were questioning how their key suppliers were using AI, particularly when handling confidential client data.

The blind spot in AI governance

The core of AI supply chain risk comes down to visibility and data privacy. Many organisations have little to no insight into whether their key vendors are using AI "behind their backs" to deliver services or process shared data.

If a supplier feeds your proprietary business information, financial records, or client details into third-party AI models without your knowledge, your data could be exposed to privacy breaches, improper model training, or regulatory non-compliance.

Identifying high-risk AI in your supply chain

Regulatory bodies are rapidly catching up to these third-party risks. For example, European Union regulations targeting the financial services sector now explicitly define what constitutes a "high-risk" AI system.

This regulatory framework provides firms with a concrete baseline to evaluate their supply chain. Rather than guessing where the danger lies, organisations can actively audit their vendors against these regulatory definitions to determine if any third-party providers are utilising high-risk AI tools to process their data.

Contractual safeguards and vendor declarations

Mitigating AI supply chain risk initially relies on foundational governance, contracts, and vendor relationship management.

A useful parallel can be drawn to traditional subcontracting rules. In regulated financial services, a service provider cannot simply outsource a client's work to a third party without explicit permission. AI usage should be treated with the same rigour:

• Mandatory declarations: Require key vendors to declare precisely how and where they use AI in delivering their services.
• Scope control: Update vendor agreements so suppliers must seek formal permission before altering how they use AI in relation to your data.
• Contractual boundaries: Ensure non-negotiable clauses are in place regarding how third parties store, process, and protect your information.

Technical controls and dark web monitoring

While contracts create legal boundaries, they cannot physically stop data from leaving a supplier's network once shared. To build a robust defence, firms must combine legal protections with technical safeguards:

• Outbound data loss prevention (DLP): Establish strict technical controls internally to govern exactly what sensitive data is permitted to leave your business via email, cloud applications, or vendor portals in the first place.
• Threat intelligence and dark web scanning: Once data leaves your perimeter, you rely on vendor compliance. To address the "unknown," organisations can deploy specialised monitoring tools that scan the open internet and the dark web for sensitive company keywords, employee email addresses, or proprietary data snippets.

If a supplier suffers a breach or mismanages your data through an insecure AI tool, dark web monitoring acts as an early warning system, alerting you if your corporate information appears on cybercriminal forums so you can take immediate remedial action.