by Dave Akers, IHSA
“Ignore AI, and you’ll own the next Blockbuster card.” — Arianna Huffington
AI is significantly enhancing the security and safety of maritime shipping containers in 2026. This transformation has moved from experimental pilots to practical, real-time applications that protect against both physical and digital threats. These are some of the uses developed since 2018 to enhance the security of containers in transit and at touchpoints in ocean and inland shipping.
Cargo Inspection & Threat Detection
- Predictive Compliance: New AI-powered tools are now used at ports to scan cargo manifests, descriptions, and historical data to flag mis-declared or dangerous cargo (e.g., improperly labeled lithium-ion batteries or chemicals) before they are loaded.
- AI Video Analytics: Ports like Rotterdam and Los Angeles use AI-driven video surveillance to automatically identify unauthorized individuals, suspicious vehicle behavior, and potential theft in real-time across vast facilities.
Monitoring Containers at Sea
- Overboard Detection: As of January 1, 2026, new International Maritime Organization (IMO) rules require mandatory reporting of containers lost at sea. To comply, ships are deploying AI systems that use onboard cameras to automatically identify and report containers falling overboard.
- Semi-Submerged Hazard Detection: AI-powered marine cameras (such as those from SEA.AI) now use deep learning and thermal imaging to detect semi-submerged containers that pose a collision risk to other vessels, even in low-light or rough sea conditions.
Real-Time Tracking and Transparency
- Smart Tracking Systems: AI-enabled platforms provide end-to-end visibility by reading a container’s unique BIC number via cameras on cranes and trucks. This allows for automated tracking from arrival to storage, ensuring that misplaced or tampered-with containers are flagged immediately.
- Maritime Transparency Index (MTI): Launched in early 2026, this machine-learning risk scoring system assigns transparency ratings (0 to 5) to vessels and voyages, helping port authorities prioritize inspections for high-risk or “dark fleet” ships.
Cybersecurity for Cargo Data
- Network Threat Detection: AI tools like Darktrace are used to monitor maritime networks, blocking cyberattacks that target cargo manifests or GPS signals in seconds.
- Blockchain Integration: AI is increasingly paired with blockchain to verify the authenticity of digital bills of lading, preventing the use of AI-generated forged documents for smuggling or fraud.
How Rail Carriers are Using AI
Protecting ocean container transport in stack container IPI moves
In 2026, rail carriers use Artificial Intelligence (AI) to protect ocean containers during stack-container Inland Point Intermodal (IPI) moves through enhanced safety, security, and predictive asset management.
- Predictive Maintenance for Rolling Stock: AI analyzes data from thousands of onboard sensors to identify potential equipment failures before they occur, reducing service disruptions during critical IPI moves.
- Real-Time Obstacle Detection: Systems like Rail Vision use AI-driven imaging to identify hazards on the tracks in real-time, preventing accidents that could damage high-value container stacks.
- Dynamic Security Monitoring: AI-enhanced surveillance systems and drones with image recognition detect unauthorized access, potential theft, or suspicious activities at intermodal terminals and along rail corridors.
- Automated Exception Management: AI automatically identifies shipment variability—such as delays or route disruptions—allowing teams to resolve issues before they impact final delivery.
- Smart Cargo Monitoring: AI-powered sensors within containers provide live updates on environmental variables like temperature, humidity, and door-openings to ensure cargo integrity during transit.
Operational Protections for Stacked Containers
- Optimal Stacking Strategies: AI algorithms process real-time data to determine the most efficient and stable stacking configurations, reducing the risk of damage from unnecessary reshuffling or shifting during movement.
- Edge Computing for Safety: Real-time AI inference is performed locally at the edge (on trains or at trackside) to detect safety risks like overheating components or equipment drift, ensuring immediate response without waiting for cloud processing.
- Scenario Modeling: Operators use AI to model “what-if” scenarios for weather or labor disruptions, proactively rerouting freight to protected corridors.
How is US Customs and Border Protection (CBP) Using AI?
CBP has shifted clearance activities “upstream,” analyzing data weeks before a shipment arrives.
- Trade Entity Risk Model: AI-driven models evaluate the risk of trade entities by analyzing historical transactions, relationships with trading partners, and compliance history to assign risk scores to every cargo shipment.
- AI Product Passports: Through the Global Business Identifier (GBI) program, CBP uses AI-powered digital records to track goods from raw material to finished product, enabling “pre-clearance” for trusted traders.
- Automated Targeting System (ATS): Machine learning algorithms scan manifests and shipping data to detect anomalies—such as unusual routing or suspect supplier networks—allowing legitimate goods to clear faster.
Non-Intrusive Inspection (NII) Enhancement
CBP uses AI to “see” inside containers without physically opening them, significantly increasing throughput.
- Automated Recognition Technology: AI-driven analytics are integrated into X-ray and CT scanning systems to automatically detect contraband, such as narcotics or weapons, by highlighting density anomalies for officer review.
- Commodity Detection Models: Neural networks analyze X-ray images of cargo to predict commodity codes, reducing the need for officers to manually verify every item against the manifest.
- Anomaly Detection in Homogenous Cargo: Specific algorithms identify deviations within containers holding similar goods (e.g., a hidden package inside a shipment of electronics), placing bounding boxes around unidentifiable objects for immediate inspection.
Trade Enforcement & Compliance
AI acts as an automated auditor for every declaration filed with CBP.
- Document Automation: Natural Language Processing (NLP) and OCR extract data from invoices and packing lists, cross-checking weights, values, and identifiers to flag inconsistencies before filing.
- Classification & Valuation Validation: AI suggests correct Harmonized Tariff Schedule (HTS) codes and flags under-invoiced shipments by comparing declared values to trade lane benchmarks.
- Forced Labor Detection: AI models trace global supply chains to identify shipments from suppliers suspected of using forced labor, automatically triggering import bans where necessary.
Operational Maintenance & Infrastructure
- ARMOR Project: CBP uses AI for predictive maintenance of Radiation Portal Monitors (RPMs). The system predicts equipment failure dates, reducing repair times by two weeks and ensuring screening lanes remain operational for arriving containers.
- Autonomous Hull Inspections: AI-powered underwater drones (ROVs) inspect maritime vessel hulls for parasitic smuggling attachments before containers are even offloaded at the port.
Analogy for Understanding
Think of AI in maritime and rail transport as a highly advanced air traffic control system for a busy city’s ground and sea traffic. Instead of drivers (logistics managers) constantly reacting to unexpected traffic jams or broken-down vehicles, the system predicts where the jams will happen hours in advance, automatically fixes the “potholes” before they cause a flat tire, and gives “VIP passes” to trusted drivers so they can bypass the longest lines at the toll booth.
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