From load matching to predictive maintenance, artificial intelligence is quickly becoming part of trucking — so quickly that it may be putting your fleet's cybersecurity risk.

AI is powering the most revolutionary changes we have seen since the advent of the internet. It’s changing the way we interact with technology, the way we work, and the way bad actors infiltrate fleet operations.

Never before has a moderately skilled attacker been able to create convincing fake audio and video of a trusted business associate in minutes on a shoestring budget. Never before have we had autonomous digital entities loose in our tech stack that can self-escalate privileges and open new attack surfaces as they work to fulfill their missions.

The Fast-Growing Cybersecurity Risks from Artificial Intelligence

We hear a lot about the need to “secure AI,” and to implement strong governance and controls around agentic identities and AI-enabled tools. What does that look like in practice?

How do we manage an agentic identity that possesses the skills of an expert and the blind enthusiasm of an overeager intern who lacks the experience to know better? In other words, how do you control an AI system that can take action inside your fleet’s technology stack?

And how do we protect our trucking companies from the dangers of increasingly difficult-to-detect deepfakes and AI-crafted social engineering?

These are the hard questions that we face in trucking right now, and they are not going away.

The World Economic Forum's Global Cybersecurity Outlook 2026 found that 87% of organizations surveyed identified AI-related vulnerabilities as the fastest-growing cyber risk they faced in 2025. That figure was not led by nation-state actors or sophisticated criminal enterprises. It was driven by companies deploying AI faster than they could govern it, and attackers moving into the gap.

Where Do We Begin to Address AI Risks in Trucking?

So where do we start? It is important to first take a step back and ask, “What business case are we trying to solve?”

Once we have identified this, we can determine the exact scope of the solution we need to develop. Deploying advanced AI-enabled tools in haste because they promise to solve all our challenges is a recipe for trouble.

Before we can determine what the security solution is, we need to clarify exactly what it is we are trying to secure. This answer will vary from tool to tool.

If your fleet is using AI-powered routing or predictive maintenance platforms, your primary concerns are data quality and data leakage, along with protections against prompt injection (malicious prompts designed to manipulate the tool's output).

Agentic AI is an altogether different animal. These tools do not simply take in data as a prompt and return more data. Agentic tools take actions. They interact with other systems.

This means securing these systems looks a lot like securing a user’s account. We need to consider what credentials the agent holds, what systems and data it has access to, and how much damage would occur if the agent were compromised or manipulated by a malicious actor.

These two types of AI-related security concerns cannot be handled in the same way or covered by the same controls.

How to Control AI Tools in Your Tech Stack

Agentic AI is beginning to see adoption across the trucking industry, whether through integrations with technology management system (TMS) platforms for automated load matching, or through application programming interface (API) access to load boards, fuel card systems, or any other tool found in the tech stack of most fleets.

It is important to remember that every integration point for these agents is an identity with credentials and permissions. A compromised or misconfigured agent can quickly escalate from a minor security incident to a significant operational issue for your fleet operations.

So how do we properly constrain an overeager virtual intern with expert capabilities and no judgment about when to stop? You treat it the same way as a new hire who has been granted too many permissions.

• Define the minimum permissions the agent to accomplish its specifically assigned tasks and nothing more.
• Give the agent its own dedicated credentials that are closely monitored and regularly audited.
• Set hard limits on automated tasks versus those that require a human in the loop.

Segmenting duties between separate agents is also important here. A routing agent should not also be able to access payroll. A maintenance-related agent should not have access to load board APIs.

As we work to select or develop a solution, the security controls must be baked into the design process. Given the speed with which AI capabilities have increased over the last several years, the knowledge base and technical controls to properly secure this technology are still racing to catch up. This hard truth requires us to move deliberately and with caution as we implement AI-enabled solutions.

Deepfakes and Fraud Risks for Fleets

Deepfakes and AI-powered social engineering present a challenge that will not be solved by technology alone.

A moderately skilled attacker can clone the voice of a trusted business associate or internal executive with ease. For example, a dispatcher could receive a call that sounds like an executive asking to reroute a payment or change load instructions.

Video deepfakes are not far behind and present a growing threat.

Combating this means process changes and awareness training on the specific challenges posed by these new attack vectors.

In addition to training, process changes that address the nature of this threat directly will be necessary. Verification procedures for any request involving money, authorization, or credential changes are paramount. If a team member receives an unsolicited but legitimate-sounding phone call asking them to redirect a payment, they need to terminate the call and call back on a trusted number to verify the legitimacy of the request.

Good security always begins with governance and a clear strategy from the top down. Organizations must focus on cementing their AI strategy and developing clear policies around acceptable use, control requirements, and vendor management processes. Leaders must be involved in developing this strategy in collaboration with their internal AI experts, who we must be developing through education and training opportunities.

Security Programs Must be Flexible

Successfully securing this latest wave of technology is a perfect example of the flexibility we must maintain in our security programs. The tools evolve, the threats evolve, and our security programs must evolve with them.

What must not change is the security mindset. Security teams must work closely with organizational leadership to make sure the new AI tools that can solve real-world trucking problems are deployed efficiently and securely.

As fleets adopt AI across dispatch, maintenance, and back-office systems, the time is now to focus on training and securing these tools before small risks turn into costly problems.