AI supply chain visibility gaps leave vendor risk unseen

At a glance

What this is: Panorays argues that AI supply chain governance breaks when teams review only the contracted vendor and miss the downstream services and data flows that actually process sensitive information.

Why it matters: This matters to IAM and security practitioners because AI-enabled suppliers create hidden third-party and nth-party trust chains that traditional vendor review and periodic assurance processes do not control.

By the numbers:

• In a 2026 CISO survey of 200 US-based CISOs, 77% ranked third-party risk among their biggest threats.
• Only 13% track nth-party vendors, the layers behind the layers where AI systems move data.
• 60% say AI vendors are a different kind of risk than regular software.
• Only 22% have a real policy for evaluating AI vendors.

Context

AI supply chain risk arises when the systems used to deliver a business capability rely on multiple upstream services, APIs, and infrastructure layers that the buying organisation does not directly govern. In practice, the approved vendor is often only the front door, while data is processed, transformed, retained, and sometimes routed through additional services the security team never separately assessed.

For IAM and NHI teams, the governance issue is not only supplier assurance but delegated access and data handling across machine-to-machine integrations. The article’s core point is that the control boundary stops at the contract, while the operational boundary extends into model providers, transcription services, cloud hosting, and the identities those services use to exchange data.

Key questions

Q: What should organisations do first to govern AI supply chain risk?

A: Start by building an AI inventory that lists models, agents, prompts, skills, MCP servers and the dependencies around them. Once that map exists, attach ownership, approval status and runtime reach so risk can be triaged, audited and enforced instead of guessed at from code alone.

Q: Why do third-party AI dependencies create more risk than traditional SaaS vendor reviews?

A: Third-party AI dependencies are harder to govern because the organization often does not control the model, the tool layer, or the hidden suppliers behind them. A single dependency can change behavior through prompts, permissions, or sub-processing paths without a visible code change. That makes provenance, version control, and continuous monitoring more important than static questionnaires.

Q: What breaks when AI vendors use hidden upstream services?

A: The organisation loses sight of where sensitive data is processed, retained, and routed, which means policy and legal assumptions no longer match the technical path. Once that happens, vendor approval no longer proves data control, and internal accountability becomes hard to assign across the chain.

Q: What should organisations do when AI outputs can influence transactions or advice?

A: Treat the model as part of a delegated business workflow and apply scoped permissions, logging, and human approval before any customer-impacting action. The practical standard is not whether the model sounds safe, but whether it is technically prevented from crossing the action boundary.

Technical breakdown

How AI supply chains hide behind the approved vendor

AI supply chains are composed of chained services that may include an application front end, a transcription or enrichment layer, a model provider, and the cloud infrastructure underneath them. Each layer can have its own API keys, retention rules, regional hosting choices, and subprocessors. The buying organisation often sees only one named supplier, but the data path is governed by several separate control planes. This creates a visibility gap where approved vendor status does not equal controlled data processing. Practical implication: map the full upstream service chain before accepting any AI-enabled workflow into production.

Practical implication: Map the full upstream service chain before accepting any AI-enabled workflow into production.

Why nth-party dependencies change the governance model

Nth-party risk is the risk created by vendors’ vendors. In AI services, it matters because hidden dependencies can change without a new contract or a fresh security review, especially when a supplier adds a model call, swaps hosting, or delegates processing to another service. Traditional annual questionnaires are too slow for this environment because the architecture can shift between assessments. That means the issue is not just supplier trust, but trust in the supplier’s own supplier controls. Practical implication: treat nth-party discovery as a continuous control, not a one-time assurance task.

Practical implication: Treat nth-party discovery as a continuous control, not a one-time assurance task.

Shadow AI and silent model failure create a different risk class

Shadow AI is unmanaged or undiscovered AI use inside the organisation. The article also highlights a second issue: model outputs can be confidently wrong without any operational fault signal, which means governance has to cover both hidden adoption and silent quality failure. Security teams cannot rely on crash events, error codes, or conventional vulnerability signals to spot the problem. They need guardrails, inventory, and review processes that look at what the AI is connected to and what decisions it influences. Practical implication: build controls for discovery, logging, and human review around AI outputs and their downstream use.

Practical implication: Build controls for discovery, logging, and human review around AI outputs and their downstream use.

Threat narrative

Attacker objective: The practical attacker objective in this pattern is to expand access to sensitive business data through hidden supply chain dependencies and to exploit the resulting visibility gap.

1. Entry occurs when an employee submits sensitive content into an AI-enabled business app that forwards the data to multiple upstream services.
2. Credentialed service-to-service calls and embedded APIs move the content through transcription, model, and hosting layers that the buyer does not directly control.
3. Impact emerges when hidden third-party processing, retention, or hallucinated output alters confidentiality, compliance, or business decisions without being visible in the original vendor review.

Breaches seen in the wild

• Hugging Face API tokens exposed 2023: Lasso Security found 1,681 live Hugging Face tokens in public code, 655 with write access, reaching 723 organisations including Meta; all were revoked.
• Ultralytics PyPI compromise 2024: A GitHub Actions injection and a leftover PyPI token let attackers publish four Ultralytics releases that installed a cryptominer.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI supply chain governance fails when organisations treat the named vendor as the control boundary. The article shows that the real processing path often includes transcription, model, and cloud layers that sit outside the original review pack. That means supplier assurance is only meaningful when it extends to the full chain of delegated services and data handling. Practitioners should assume the first contract is not the last trust decision.

Nth-party visibility is now a governance requirement, not a nice-to-have map. When a vendor can add model calls or infrastructure dependencies without a new procurement cycle, annual review models cannot keep pace. This is a control design problem, not an awareness problem. Security and IAM teams need continuous discovery of upstream services, ownership, and data paths so hidden dependencies are surfaced before they become policy exceptions.

Shadow AI is the operational expression of unmanaged supply chain trust. If business teams can route sensitive files through AI services outside formal governance, the organisation has lost sight of where data is processed and who can touch it. That is especially relevant to identity teams because service identities, API keys, and delegated access are what make those hidden flows possible. The practical conclusion is that AI governance must include machine identity oversight as part of supplier control.

Confidence without correctness creates a new risk class for decision governance. The article’s point about hallucination is important because bad output can travel as trusted work product, not as an obvious security event. That makes detection-centric thinking insufficient. Programmes should focus on where AI output feeds approvals, compliance statements, or operational decisions, because that is where error becomes business impact.

AI supplier control is converging with NHI governance. The same patterns that create machine identity risk, such as hidden credentials, delegated APIs, and unmanaged service-to-service trust, now appear in AI supply chains. The field needs a more explicit model for who or what is acting on behalf of whom, and under which retained controls. Practitioners should align AI supplier review with machine identity governance rather than leaving it in generic procurement oversight.

From our research library:

• 92% of organisations expose NHIs to third parties, raising concerns about supply chain security, according to the Ultimate Guide to NHIs.
• Breaches involving third parties rose to 48% of all breaches, a 60% increase on the previous year, according to Verizon's 2026 Data Breach Investigations Report.
• Read next: AI Supply Chain Security and AI-BOM Guide

What this signals

AI supplier governance is becoming a visibility problem before it is a model-risk problem. The immediate change for practitioners is that procurement review, contract terms, and annual assessments no longer provide enough assurance when data can move through multiple hidden services. Teams should expect continuous discovery to become the baseline control for AI-enabled suppliers, especially where sensitive files or regulated content are involved.

Nth-party exposure is the real control gap in AI supply chains. If a supplier can change model routing, hosting, or downstream APIs without a fresh review, the buyer’s governance model is already behind the architecture. NHI and IAM teams should treat service identities, API tokens, and delegated access as part of the third-party programme, not as a separate technical detail.

For practitioners

• Map the full AI service chain Document every upstream component that processes business data, including transcription services, model providers, and cloud hosting layers, not just the contracted front-end application.
• Continuously discover nth-party dependencies Replace annual questionnaires with automated discovery that flags newly introduced upstream services, API integrations, and hidden subprocessors as they appear.
• Verify data isolation and retention claims Test how data is separated in practice, confirm retention and opt-out settings at each processing layer, and challenge any vendor claim that cannot be evidenced end to end.
• Govern AI service identities and API access Inventory the machine identities, tokens, and API keys that let AI services move data between suppliers, then review their scope and lifecycle as part of supplier governance.
• Add human review to AI outputs that drive decisions Require explicit review for AI-generated content that influences compliance, financial assumptions, or operational approvals, because silent output errors do not create their own alert.

Key takeaways

• AI supply chain risk arises when organisations trust the named vendor but miss the hidden services, hosting layers, and data flows that actually process information.
• A 2026 CISO survey cited by Panorays shows strong concern about third-party risk, yet only a small minority track nth-party vendors or maintain a formal AI vendor policy.
• Practitioners need continuous discovery, end-to-end data path verification, and control over service identities if they want AI supplier governance to match how these systems operate.

Key terms

• Nth-Party Risk: Nth-party risk is exposure created by a supplier’s suppliers and other downstream dependencies that the buying organisation does not directly contract with or control. It matters because the risk often travels through shared data, inherited access, and hidden service chains, leaving direct visibility incomplete.
• Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
• Service-to-Service Delegation: Service-to-service delegation is the practice of one system calling another on behalf of a business process, usually through APIs, tokens, or machine identities. It is the mechanism that makes hidden AI supply chains possible, and it shifts security responsibility from a single vendor to a chain of trusted systems.
• Data Flow Governance: Data flow governance is the discipline of controlling where sensitive data can move, who can move it, and how that movement is recorded. It links access policy to runtime evidence so teams can spot policy violations across accounts, regions, and third-party access paths.

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It helps security and identity practitioners connect delegated access control to the wider governance decisions their programmes depend on.