AI Is Raising the Stakes for Supply Chain Cybersecurity 

AI is moving from analysis into operational supply chain workflows, which brings up an important question. Are cybersecurity controls keeping pace? 

It’s not news that artificial intelligence is moving quickly through the supply chain. Manufacturers, shippers, and logistics providers are using it to interpret demand, flag inventory problems, review documents, and plan transportation. However, this next stage gives AI systems greater authority to act on that information. 

In turn, it raises new conversations about supply chain cybersecurity.  

A tool that summarizes a report presents one level of risk. An AI agent with access to an ERP, warehouse management system, or supplier portal presents another. If its identity is compromised or its instructions are manipulated, the impact may reach physical operations. 

As a result, supply chain cybersecurity needs to develop at the same pace as AI adoption, which brings up another question: Are controls, accountability, and recovery plans ready for systems that operate with greater speed and independence? 

AI is accelerating both adoption and attack 

Cybersecurity research already highlights the change.  

The World Economic Forum reported that 87% of respondents saw AI-related vulnerabilities increase during 2025. Among large organizations, 65% identified third-party and supply chain risk as their greatest challenge to cyber resilience. 

Verizon’s 2026 Data Breach Investigations Report found that 31% of breaches began with the exploitation of software vulnerabilities, making it the leading initial access route. The report also found generative AI supporting 15% of observed attack techniques. Attackers are using AI to find weaknesses and produce convincing social engineering at a pace that reduces the time available for detection and response. 

IBM placed the 2026 global average cost of a data breach at $4.99 million, up 12% from the prior year. AI-driven attacks increased 56%, while organizations using AI and automation extensively in security recorded average savings of $1.93 million compared with those using none. 

In essence, AI is strengthening both sides. This means that supply chain operators must look to balance the efficiency it offers with an equally deliberate security program to avoid introducing risk into already connected operations. 

Supply chain cybersecurity risk grows when AI moves from advice to action 

Much of today’s enterprise AI use still centers on research, analysis, and recommendations. That said, technology developers are more frequently describing a future in which AI agents handle broad workflows with limited human intervention.  

In supply chain operations, that might include adjusting replenishment, releasing orders, booking freight, communicating with suppliers, or changing fulfillment priorities. 

An AI agent generally requires a digital identity, system credentials, access to company data, and permission to take specific actions. Those privileges make the agent part of the organization’s attack surface. A compromised account might alter a routing instruction or expose customer records. A manipulated input could prompt an agent to trust fraudulent supplier information. An inaccurate output, acted upon automatically, might create a physical disruption before a person reviews the decision. 

That last risk surprises people, because better accuracy is one of the reasons companies adopt AI. In many cases it does deliver. AI can spot patterns, catch discrepancies and process documents faster and more consistently than a person working alone. But its accuracy depends on the data it receives and the context it lacks. It can’t always tell when an input is outdated and it doesn’t know the nuanced exceptions an experienced planner or warehouse lead carries in their head. When no one reviews its output, one bad input can spread across thousands of transactions at machine speed.

Todd Dorschner, director of IT infrastructure and cybersecurity at WSI | Kase, says, “When you take out that human in the middle and just assume that it’s going to be right, you’re risking your accuracy.” 

Claims that AI will “take over” business functions deserve practical interpretation. Near-term supply chain risk is less about one system replacing an entire organization and more about software receiving authority across many connected tasks.  

As that authority grows, identity controls and human accountability become operational necessities. As Seth Rogatzki, manager of IT applications at WSI | Kase, explains, “An LLM like Claude does what you tell it to do; it’s not omniscient. You’re the leader asking it to do something, and as the leader, you have to take ownership of what that output is.” 

Accountability becomes harder to maintain when AI operates across connected platforms. A decision made in one system may trigger actions elsewhere, making the security of the full workflow dependent on every vendor and integration involved. 

Supply chain connections increase the exposure 

Supply chains depend on shared information. Purchase orders, forecasts, shipment records, customer data, bills of lading, and inventory status move among internal teams and outside partners. APIs and EDI connections keep transactions moving, while warehouse devices and transportation platforms translate data into physical activity. 

AI draws value from those connections. It also creates another route through them. 

The risk does not stop with a company’s technology. An outside provider or logistics partner may hold credentials or process sensitive information. Each relationship introduces questions about access, monitoring, retention, incident reporting, and subcontractors.  

NIST’s Cybersecurity Framework 2.0 places governance alongside identification, protection, detection, response, and recovery, positioning cybersecurity as an enterprise risk issue rather than an isolated IT responsibility. 

Stronger AI governance starts with operational controls 

AI governance should reflect what a system touches and what would happen if it failed.  

A low-risk writing assistant does not require the same restrictions as an agent connected to procurement or warehouse execution. Security teams and supply chain leaders need a shared inventory of approved tools, their data sources, and their permissions. 

Priority controls include: 

• Authenticating each agent based on how it connects to each system, whether it inherits a user’s permissions or runs through a secured API or application connection.

• Limiting access to the minimum users, systems, records, and actions required for its task.  

• Requiring human approval for high-impact changes involving payments, suppliers, orders, inventory, or transportation.  

• Logging prompts, data access, decisions, and system actions in a form that supports investigation.  

• Testing shutdown, isolation, manual fallback, and data recovery procedures before a disruption.  

Using best practices from a security standpoint is still the optimal approach in Rogatzki’s opinion. At WSI, AI only has read access; it’s not able to write. He says, “We’re very intentional about what we have AI do.” 

NIST’s AI Risk Management Framework recommends incorporating trustworthiness into the design, use, and evaluation of AI systems. Its generative AI profile addresses risks specific to these tools, while a 2026 critical infrastructure initiative focuses on AI-enabled capabilities in operational settings.  

The common principle is straightforward: risk management belongs in the design and approval process, not after deployment. 

Vendor reviews need AI-specific questions 

Traditional security questionnaires may not reveal how a provider uses AI. Procurement and risk teams should ask whether customer data trains models, where it is stored, and who accesses it. Contracts should define breach notification, audit rights, deletion requirements, and responsibility for an AI-generated action. 

Organizations also need visibility into fourth-party exposure.  

A familiar supply chain platform may rely on a separate model provider, cloud service, or integration partner. The security posture of the named vendor tells only part of the story when critical processing occurs elsewhere. 

Vendor review assessment 

Review area	What to verify
Data access	What company, customer, and operational data the vendor accesses
AI use	Whether company data is entered into AI tools or used to train models
Access controls	How users, service accounts, and AI agents are authenticated and restricted
Data storage	Where data is stored, encrypted, retained, and deleted
Third parties	Which cloud providers, subcontractors, and AI platforms support the service
Monitoring	How the vendor detects unauthorized access and suspicious activity
Incident response	Breach notification timelines, response procedures, and points of contact
Business continuity	Backup systems, recovery timeframes, and manual operating procedures
Compliance	Relevant certifications, audits, regulations, and security testing
Contract terms	Audit rights, liability, data ownership, and end-of-contract deletion requirements

Cyber resilience protects physical operations 

A supply chain cybersecurity incident is rarely confined to just a screen. It may also stop order processing, obscure inventory, delay production, misdirect freight, or prevent a warehouse from confirming shipments.  

Any incident response plan needs to define how operations continue when trusted data or connected systems become unavailable. To prepare, operations, IT, security, legal, and external partners all must participate.  

Teams will need clear decision authority and verified offline contacts. They also need practical alternatives for receiving orders, confirming inventory, producing documents, and communicating shipment status during an outage. 

AI will play a growing role in supply chain decisions. However, its value will depend partly on whether organizations protect the identities, information, and connected systems behind those decisions.  

Strong supply chain cybersecurity supports adoption by setting clear boundaries and preparing operations for failure. 

“I always picture AI as an intern,” Dorschner says, “It’s book smart, but doesn’t know your business. It doesn’t know your real-life examples.” 

As AI takes on more responsibility, governance decides whether speed builds resilience or adds risk. As Rogatzki puts it, ‘AI is more about human augmentation than replacement.’