Rethinking AI and cybersecurity in the supply chain

From risk to resilience

In a digital age driven by speed, scale and interconnectivity, the question confronting every business leader is no longer if AI will reshape the supply chain, but how, and at what cost.

The integration of generative and agentic AI technologies into supply chain systems offers transformative potential—streamlining procurement, enhancing transparency, and uncovering new market opportunities at unprecedented speed. Yet for all its promise, AI is introducing a labyrinth of cybersecurity and governance risks that even the most seasoned C-suite executives are still learning to navigate. The subsequent landscape is forcing many leaders to question many long-held assumptions on the nature of risk, resilience and innovation.

AI and cybersecurity in the supply chain: Overview

• The trojan horse of innovation
• Beyond the first link in the chain
• Governance at the speed of change
• The cost of caution vs. the price of falling behind
• Closing thoughts

The trojan horse of innovation

During a recent virtual C-Suite Exchange, in partnership with Risk Ledger, a diverse group of cybersecurity leaders, data strategists and privacy experts revealed just how fragmented—and urgent—the conversation around AI and supply chain risk has become.

One participant described a striking development: a client had asked bidders to confirm they had not used generative AI in their response. While intended to safeguard sensitive data, the request also underlined a paradox: many firms do use AI to gather, filter or synthesise evidence for bids. This is not negligence; it is necessity. But the tension between innovation and compliance is growing sharper by the day.

As one CSO noted, “It’s not just about whether AI is being used—but where, by whom, and whether anyone has eyes on the risks it may quietly usher through the back door.”

Beyond the first link in the chain

The most sobering insight? The real danger may not lie within your direct suppliers, but several layers downstream. A supplier's supplier might adopt an AI model hosted on foreign servers with questionable data sovereignty. Another might unknowingly integrate compromised open-source components. In such a world, “zero trust” is not a policy, it is an operational imperative. The risks are no longer theoretical; they are immediate and deeply structural.

“We’re not just working with vendors,” one executive put it bluntly. “We’re working with their ecosystems. If they’ve got a weak link, then so do we.”

Governance at the speed of change

Many organisations are actively investing in stronger controls by establishing AI governance boards, securing regional data enclaves, and imposing strict review paths for onboarding new technologies, the call heard. But these processes, while essential, are often slow. And in a fast-moving AI landscape, speed is the advantage.

So how do you reconcile agility with assurance? Some businesses are crafting internal “passports” for their AI services—meta-level certifications that detail data lineage, sovereignty, and compliance. Others are pushing for open standards or blockchain-enabled verifications to enable trust at scale. But most leaders agreed: the lack of shared, enforceable standards remains one of the biggest blockers to responsible AI adoption.

The cost of caution vs. the price of falling behind

Interestingly, while many companies are leaning heavily on large, trusted providers like Microsoft or Oracle, others are beginning to question whether such conservatism could stifle innovation.

“Ironically,” said one privacy lawyer, “smaller suppliers often show more flexibility, greater transparency, and are more willing to be audited. Yet we default to the big players—because it feels safer.”

This safety-first instinct, however, may come at a strategic cost. As one executive warned, “If we only work with who we trust, we may miss the wave of next-generation tech. We risk locking ourselves into legacy players—and locking out the future.”

Perhaps the most provocative insight of the session was philosophical, not technical. Generative AI isn’t just another digital tool—it is fast becoming a “team member” in its own right. One speaker compared today’s AI systems to recent graduates: full of promise, occasionally misguided, and still in need of constant supervision.

But tomorrow? AI won’t just support operations, it may very well direct them.

The challenge, then, is cultural as much as technical. Leaders must prepare organisations not only to use AI, but to trust it just enough without relinquishing control. This will demand a paradigm shift in training, governance, leadership and mindset.

Closing thoughts

As the conversation drew to a close, one executive summarised the collective sentiment: “AI in the supply chain raises more questions than answers. But not asking them is the real risk.”

Indeed, the imperative for the C-suite is clear. Leaders must move beyond reactive conversations and toward proactive strategies by embedding AI literacy into their cultures, asking hard questions about risk and resilience, and redefining trust in a world where algorithms can influence outcomes just as much as people.

Because in the end, the supply chain of the future isn’t just smarter. It’s also far more human—and far more vulnerable—than we ever imagined.