Artificial Intelligence and Agentic LLMs have taken the globe by storm. The rapid advancement of AI capabilities has changed how LLMs are now employed in the global supply chain industry. We have moved from an age of static spreadsheets to the era of Agentic AI; autonomous systems that not only predict demands but actively shape how our supply chain operates by negotiating with suppliers, booking freight, and helping in decision making.
While companies rush towards these autonomous systems, one critical aspect often goes ignored; these Agentic AI systems are based on LLMs which cannot think for themselves, but work based on pattern recognition and can be easy to manipulate. If your LLM is not deployed with the right security measures in place or it is not trained on the right data, your supply chain operation will suffer.
To mitigate such risks, supply chain leaders and business owners must adopt an in-depth security policy that governs the organization’s strategy focused on five key pillars:
1. Validate And Red Team Before Deployment
Before your agentic AI is deployed and allowed to interact with customers or vendors or to make decisions, it must undergo rigorous ‘Red Teaming’ to identify any design flaws that can be exploited. Security teams need to simulate jailbreak attempts to ensure that the agent cannot be tricked or manipulated by a supplier or a customer into violating business code of conduct.
The necessity of such validation is exhibited by an incident in 2023, when a man convinced a Chevrolet’s AI bot to sell a $70,000 SUV for $1.
You must ensure that your AI model meets compliance requirements and ensure the integrity of the dataset that your model is trained on.
2. Prevent Data Poisoning Via SBOMs
While securing the data is important, it is also important to ensure the data your supply chain trains on is grounded in truth. Provenance and lineage protocols for the data your model trains on must be implemented allowing scrutiny of results. This is vital as LLMs are prone to Data Poisoning.
Data Poisoning may be a result of training your model on flawed data or having a malicious actor alter your records or inject faulty data. Any demand forecasting done based on this flawed data will result in a skewed output.
Consider a scenario where a malicious actor injects data that falsely reports the number of products present in your warehouse. This would trigger the Bullwhip Effect; a phenomenon defined by the amplification of variability of orders as they flow upward in the supply chain compared to the variable of customer demand, resulting in your automated procurement systems ordering obsolete inventory that clogs your warehouse and drains capital.
This is why organizations should use a Software Bill Of Materials (SBOMs); a list of components or ingredients that make up a software, ensuring every data source is cryptographically signed and verified.
3. Mitigate Indirect Prompt Injection Risks
Even after you have scrutinized the data your LLM trains on, the threat persists. LLMs generate responses on the queries they are fed. This means that every document or piece of data that your LLM requires should be treated as a potential security threat.
Take for example, malicious code or text that is hidden in plain text of a PDF. An agent might read this malicious code and execute it, thereby damaging the integrity of the system. This is known as Indirect Prompt Injection.
4. Mandate Human Oversight For Critical Decisions
AI, no matter how well-trained, should be treated as a tool and not a final decision-maker. Scrutinize your models’ results and take the final decision. Organizations should always keep humans in the loop of all the workflows AI is a part of.
5. Formalize Governance And Prepare For Regulation
Organizations should establish proper AI governance policies. All changes or updates to the AI model should be documented. Workflows which involve critical decisions should involve human oversight and all decisions taken with the assistance of LLM should be recorded for accountability.
This practice is becoming a regulatory requirement under frameworks like the EU AI Act, which mandates traceability for AI systems deployed in critical infrastructure. By formalizing governance, you create an audit trail that allows you to trace all decisions, and if an error occurs the logs can provide the data needed to correct the model.
The Future Of AI and LLMs In Supply Chain
Over the next couple of years, AI will play a critical role in shaping the supply chain industry. Companies which integrate AI effectively and securely will be at the forefront of resilient supply chain practices, while companies that deploy AI models carelessly will face disruptions that are not only difficult to predict but even harder to mitigate.
AI will drive the supply chain operations, but trust will be the fuel.
That is why understanding and mitigating model poisoning is no longer optional but a strategic step towards the future.