Trust but Verify: Navigating the New Era of AI in Trade Compliance

The trade compliance landscape has presented shippers with an undeniable opportunity … and a complex decision. Artificial intelligence has demonstrated staggering capability; specialized AI models are now passing the U.S. Customs Brokers License Exam (CBLE). This is a feat that requires mastery of thousands of pages of complex regulations. This suggests that not using an AI tool could be seen as failing to exercise reasonable care, as is sometimes the case in other sectors, such as healthcare.

On the other hand, regulators are signaling a cautious, possibly restrictive approach. The U.S. Customs and Border Protection (CBP) recently issued ruling H350722, which has made trade compliance professionals question how to use AI tools compliantly. While the ruling acknowledges the utility of online platforms, it draws a firm line against using AI to independently prepare and file customs entries without appropriate human oversight.

To effectively and safely use AI in trade compliance in this landscape requires adopting a philosophy of “Trust but Verify.”

The Problem with Probabilistic AI

Skepticism of AI pervades society for various reasons, and confusion and doubt about AI tools among compliance trade professionals is hardly rare. In the trade sector specifically, this dubiousness may come from a legitimate distrust of large language models (LLMs). LLMs have many interesting features, but they are ultimately probabilistic engines. They predict the next likely word in a sentence like a highly sophisticated version of autocomplete. 

While this works reasonably well for writing emails, it’s dangerous in a regulated environment where plausible but imprecise outputs can lead to hefty regulatory fines or even seized shipments. In trade compliance, semantics can be everything. A public LLM might see the phrase “The sky is …” and then draw on a massive corpus that is effectively the entire internet, and predict the answer “blue.” And most times, “blue” is the word that comes next.

But in the context of the Harmonized Tariff Schedule (HTS), the right word might not be the one that comes next in most contexts. A general LLM scraping Wikipedia and Reddit is unlikely to find the correct answer to very specific trade compliance queries. Without a deterministic filter — a layer of rigid, rules-based logic — AI is prone to incorrect assertions.

But this is an issue with using a public-facing LLM. AI is an incredibly broad term referring to numerous different tools. So, while getting Claude or Gemini to produce documents for a CBP filing is unwise, this doesn’t mean AI has no place in trade compliance. To succeed, enterprise AI adoption must shift from open-ended prompting to structured governance.

Building a Governance Framework

To align with CBP expectations and mitigate regulatory risk, an AI compliance tool must be more than just a chatbot. It requires a multi-layered architecture designed by trade attorneys and licensed customs brokers who understand the scope of use cases and the nuances of the law.

1. A Tight Corpus of Knowledge

General AI can pull from anywhere on the internet, which includes outdated blogs, incorrect forum posts, and irrelevant data. A professional trade compliance AI platform must be constrained to a much narrower corpus, such as the current HTS, the Code of Federal Regulations (CFR), and official CBP Rulings. When the AI “reads” a product description, its logic must be anchored exclusively to relevant sources of regulatory truth.

2. The Bright-Line Rule: Human-in-the-Loop

Perhaps the most critical guardrail is the “bright-line” rule: No AI-generated outputs should ever enter an enterprise system without explicit human sign-off.

AI is great for preparing evidence and even suggesting decisions. But AI should not be a decision-maker. A human professional is needed for that. Their role shifts from manual data entry to validation engineering — reviewing the AI’s conclusions, checking its evidence, and confirming the logic.

An AI Adoption Checklist

If you’re evaluating AI tools to future-proof your compliance program, you must ask the right questions of your technology vendors. A robust AI solution should meet the following criteria:

• Decomposed logic: Does the tool show its work? 
• Traceability: Can you click a button and see the exact regulatory ruling or HTS section the AI used to make its determination?
• Auditability: Does the system produce a programmatic audit trail? 
• Schema-constrained outputs: Does the AI produce structured data that accommodates your existing ERP or GTM systems?
• Version control: Can the AI tie its historical outputs back to the specific version of the HTS that was active at the time of entry?

Future-Proofing Your Program

AI is no longer a futuristic concept in trade, it’s the current reality. By implementing practical guardrails and a “Trust but Verify” workflow, you can embrace the speed of technology without sacrificing the integrity of compliance programs.

Ready to see how AI can safely transform your trade operations? Contact KYG Trade to learn how our AI-assisted approach can help you achieve higher efficiency while maintaining accuracy and compliance.