From warehouse automation to autonomous delivery routing, artificial intelligence is rapidly being embedded in supply chain operations. Yet the very systems designed to enhance performance may expose organizations to new, often invisible risks, unless security is built in from the start.
High Output, High Stakes for Misuse
AI is becoming integral to how supply chains forecast, route, stock, and deliver. The early focus has been on well-scoped use cases, demand planning, inventory checks, label scanning—where benefits are immediate and measurable. But adoption is quickly shifting toward more autonomous, agentic systems capable of carrying out tasks with minimal human oversight.
Gartner forecasts that by 2028, AI agents will drive 15% of business decisions, including logistics operations. These agents, comprising a defined goal, an AI model, and a toolkit of software and hardware, introduce new dimensions of efficiency. However, they also introduce new vulnerabilities. In a networked supply chain, where systems interoperate across vendors, partners, and geographies, a manipulated AI agent doesn’t need to crash. It only needs to be misdirected.
Such misdirection is hard to detect and costly to unwind. An AI routing system could be subtly altered, sending refrigerated trucks on suboptimal routes that result in spoiled goods. Miscalibrated agents might de-prioritize replenishment of critical components or misinterpret seasonal trends, throwing off availability across retail or manufacturing nodes. These aren’t theoretical edge cases, they are real operational exposures.
Security Must Evolve With Intelligence
Traditional cybersecurity focuses on system access and data protection. With AI, the threat vector shifts: attackers target the model’s logic or output, not just its perimeter. That means supply chain AI needs oversight at two levels—its internal reasoning (“thought”) and its external behavior (“action”).
Organizations must invest in proactive defenses, including AI-driven red-teaming, continuous scenario testing, and real-time anomaly detection. Critically, each use case must be treated as unique: what works for a forecasting model may not suffice for a robotic fulfillment agent. Risk modeling should extend beyond system uptime to cover decision quality, error propagation, and downstream impacts.
The irony is that AI must help defend AI. When implemented well, autonomous oversight can contain faults early and adapt to emerging threats faster than static security protocols. But this requires intentional design—not assumptions that off-the-shelf tools will suffice.
Building Trust Into Intelligent Systems
As AI becomes a decision-maker rather than just a decision-support tool, supply chain leaders must reassess not just how AI is deployed, but how it is governed. Agentic systems offer real value—but they also challenge long standing assumptions about reliability, visibility, and control.
The goal is not to slow innovation, but to structure it. That means treating AI security not as a back-end function, but as a design requirement, integrated from the outset and continuously tested as systems evolve. Governance, model validation, and incident response need to mature alongside AI capabilities, especially as automation scales across physical operations.
