By: Jon Murphy
Abstract: Your vendors are using AI, and there’s a good chance your board doesn’t know where, how, or why. As artificial intelligence quietly spreads through supply chains, it’s creating invisible risks with very real consequences. From data exposure to regulatory fallout, the next major crisis may not start inside your company. It will likely come from outside it. This article reveals why “invisible AI” is becoming one of the most urgent threats facing organizations today, and what leaders must do before it’s too late.
The Question No Board Could Answer
In a recent boardroom discussion, I asked a simple question:
“Which of your top vendors are using AI inside your environment today?”
Silence.
Not because the board didn’t care. Not because leadership was unprepared. But because no one actually knew.
That moment captures a growing reality across industries: organizations are racing to govern the AI they build, while remaining largely blind to the AI already embedded deep within their vendor ecosystems.
“Trust, but verify.”
President Reagan wasn’t talking about agentic artificial intelligence buried inside third-party software stacks, but he might as well have been.
In 2026, the most dangerous AI in your organization may not be the model you built. It’s the AI your vendors already deployed without telling you.
The Hidden Expansion of AI Risk
Generative AI and agentic systems are no longer experimental. They are embedded, quietly and rapidly, into supply chains, SaaS platforms, and operational workflows.
Across industries, a consistent pattern is emerging: organizations are increasingly dependent on third-party tools that contain undisclosed or poorly governed AI capabilities. Industry research suggests that a majority of enterprises now rely on third-party software with embedded AI features, many of which are not fully inventoried or understood at the board level (Gartner, 2025; McKinsey, 2024). In many cases, these systems access sensitive data, make autonomous decisions, and introduce risk without clear visibility or accountability.
If your vendor uses AI, you use AI, whether you approved it or not.
This isn’t just a technical issue. It’s a business risk.
- Revenue disruption
- Regulatory exposure
- Brand damage
- Operational fragility
AI risk doesn’t respect organizational boundaries. It travels through your vendors.
Understanding AI Supply Chain Risk
The integration of AI into vendor ecosystems introduces a new class of converged risks that extend beyond traditional third-party risk models.
Key Risk Categories
- Data Privacy: AI systems often require access to sensitive data, increasing the risk of exposure or misuse.
- Cybersecurity: Unvetted AI tools can become entry points for sophisticated attacks.
- Model Integrity: Bias, drift, and manipulation can undermine decision-making.
- Regulatory Compliance: Frameworks like GDPR, the NIST AI Risk Management Framework (AI RMF), and ISO/IEC 42001 are raising the bar for accountability, requiring organizations to demonstrate transparency, governance, and risk controls around AI systems.
- Reputational Harm: Failures in AI governance can quickly become public incidents.
The Ripple Effect
AI risk is rarely isolated.
With agent-to-agent (A2A) interactions, vulnerabilities can propagate across systems at machine speed, before security teams can detect or respond.
Third-party risk has become fourth- and fifth-party AI risk.
A Governance Model That No Longer Works
Too many boards are trying to govern 2026 risk with 2016 structures.
Traditional third-party risk management programs were designed for predictable, bounded systems, not autonomous technologies that learn, adapt, and act independently.
The result is a dangerous gap between perceived control and actual exposure.
You can’t govern what you can’t see, and you can’t trust what you haven’t tested.
The Board’s New Mandate
AI risk is no longer an IT issue. It is a board-level fiduciary responsibility.
Boards must move beyond passive oversight and take an active role in governing AI risk across the extended enterprise.
- Define AI Risk Appetite
Boards must clearly articulate acceptable levels of AI risk, particularly in vendor environments. This includes defining escalation thresholds and identifying which AI use cases require board visibility.
- Integrate AI into Enterprise Risk Management
AI supply chain risk must be embedded into ERM frameworks, not treated as a standalone technical concern.
- Establish Accountability
Clear ownership must exist across management, risk functions, and vendors. Ambiguity is where AI risk metastasizes.
- Demand Transparency
Boards must insist on visibility into where AI exists, how it operates, what its role-based limitations are supposed to be, and what data it touches.
- Elevate Vendor Oversight
Vendor evaluation is no longer a procurement exercise. It is a core governance function.
Evaluating AI Vendors: The Questions That Matter
When onboarding or reviewing vendors, boards should ensure leadership can answer:
- What data does the AI access, process, or generate?
- How is that data protected?
- How are models validated for accuracy and bias?
- What security controls protect the system?
- What happens when the AI fails or is compromised?
- Does the vendor disclose how their AI works?
- Are there audit rights and enforcement mechanisms?
If these questions cannot be answered clearly, the risk is already material.
Why This Matters Now
This is not a future problem. According to recent industry analysis, over 60% of organizations report using AI-enabled third-party tools, while a significant portion of boards lack full visibility into where and how those tools operate within their environments (Deloitte, 2025).
Regulatory scrutiny is accelerating. AI adoption is outpacing governance. And organizations are becoming increasingly dependent on systems they do not fully understand.
As the recent Mercor breach demonstrates, third-party risk can cascade across entire industries. The next wave of major breaches and compliance failures may not come from internal systems. They will likely originate in the supply chain.
The exposure is external, systemic, and largely invisible.
The Organizations That Will Win
The organizations that thrive in this new era will not be the fastest adopters of AI. They will be the most disciplined governors of it.
They will:
- Demand full visibility into AI across their ecosystem
- Treat vendor AI risk as enterprise risk
- Hold leadership accountable for transparency and control
- Continuously monitor and validate AI behavior
They will replace blind trust with continuous verification.
A Crisis Hiding in Plain Sight
“The most dangerous AI in your organization isn’t the one you built. It’s the one you inherited through trust.”
Invisible AI is already shaping decisions, accessing data, and introducing risk across your organization.
The question is not whether it exists.
The question is whether your board is prepared to govern it.
Because in the age of AI-driven supply chains:
Vigilance isn’t a competitive advantage. It’s the cost of survival.
And the organizations that fail to act won’t just fall behind.
They’ll be left explaining why they never saw the risk coming.
About the Author
Jon Murphy is a seasoned, multi-award-winning enterprise risk and resiliency leader with a Big 4 consulting background, serving as a trusted advisor to corporate boards and C-level executives. His expertise covers AI governance, quantum readiness, cybersecurity, regulatory compliance, enterprise risk management, privacy, and organizational resiliency. A recognized technology professional, speaker, and author, Jon has been published in CSO Online, CIOReview, CIO, and Bloomberg BusinessWeek, among others. He has designed and implemented contingency plans and exercises across multiple industries, successfully implementing those plans in managing responses to complex real-world threats.
Disclaimer: The views and opinions expressed in this article are solely those of the author and do not necessarily reflect the views of his employer or any affiliated organizations.
