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From Awareness to Action: AI for Smuggling Detection and Prevention

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Introduction

Maritime smuggling remains one of the most persistent and complex security challenges facing governments today. From narcotics and weapons to human trafficking, wildlife crime, and sanctions evasion, the oceans are the preferred highway for illicit trade. The sheer scale is staggering: estimates suggest that 70–80% of the world’s cocaine moves by sea, generating billions of dollars in revenue for transnational criminal organizations. In the Mediterranean alone, smuggling networks have been valued in the billions annually, while U.S. authorities report thousands of metric tons of cocaine intercepted via maritime routes in recent years.

The maritime environment offers smugglers distinct advantages. Vast and sparsely monitored, the world’s oceans cover over 70% of the planet, yet only a fraction is under constant surveillance. Sophisticated concealment methods — from fully submersible narco-subs to “shadow fleets” operating under false flags — make detection difficult. At major ports, physical inspection rates for incoming containers often remain in the single digits, leaving most cargo unchecked. Jurisdictional boundaries and fragmented enforcement frameworks further complicate coordinated interdiction, allowing illicit actors to exploit gaps in monitoring and response.

Traditional approaches, while critical, are increasingly outmatched. Manual inspections, random screening, and visual surveillance cannot scale to meet the volume and complexity of global maritime trade. Delayed intelligence sharing, siloed data, and resource constraints mean that many high-risk shipments or vessels are only identified after they reach their destination — when prevention is no longer an option.

To counter these challenges, agencies need tools that work at the speed and scale of modern smuggling operations. Artificial Intelligence offers that capability. By processing and analyzing vast streams of maritime, trade, and sensor data in real time, AI can uncover patterns, flag anomalies, and enable faster, more targeted enforcement actions.

This report examines three key ways AI-based technology can transform maritime smuggling detection and prevention. These capabilities represent a shift from reactive interdiction to proactive prevention — giving governments the ability to stay ahead of increasingly adaptive smuggling networks.

Windward’s AI-Driven Approach to Border Protection

Effective border protection in the maritime domain requires the ability to identify high-risk vessels before they reach port — enabling enforcement agencies to act proactively rather than reactively. Windward’s Smuggling Risk model was designed to meet this challenge by applying advanced machine learning to global vessel activity, pinpointing the vessels most likely to be engaged in smuggling operations.

The model evaluates every vessel in the global fleet daily, producing risk recommendations based on an ensemble-learning approach that combines multiple AI models — both supervised and unsupervised. These models are trained on a proprietary database of historical maritime crime records, including cases of drug trafficking, contraband smuggling, and other cross-border offenses.

Windward’s analysis incorporates two primary categories of indicators:

Behavioral Risk Indicators – Patterns of activity that deviate from expected maritime “pattern of life,” such as dark activity, first-time visits, course deviations and loitering.
Identity Risk Indicators – Signals of irregular or deceptive vessel ownership and registration, such as frequent or multiple identity changes, invalid vessel identifiers, and irregular or opaque business structures.

By combining behavioral analysis with identity intelligence, and by benchmarking each vessel’s activity against baselines for its class and seasonal norms, the model can detect anomalies that would be difficult to identify manually.

The result is a three-tier risk classification — low, moderate, and high — that allows border security, customs, and law enforcement agencies to focus their resources on the vessels most likely to pose a threat. This targeted approach not only increases interdiction rates but also reduces the operational burden of inspecting low-risk traffic, enabling a smarter allocation of personnel and assets.

From Overload to Action: Applying Risk Models in Practice

One of the greatest challenges for border protection and customs agencies is scale. On any given day, thousands of vessels are bound for U.S. ports. On August 11, for example, 5,488 vessels reported the United States as their destination. This volume is far beyond the capacity of any agency to investigate comprehensively in real time.

All vessels with the U.S. as their reported destination on August 11, 2025. Source: Windward Maritime AI™ Platform

Smuggling high or moderate risk vessels with the U.S. as their reported destination on August 11, 2025. Source: Windward Maritime AI™ Platform

From there, patterns and priorities emerge instantly: most of these vessels were headed to Houston (15) and New Orleans (12), with others bound for a smaller number of ports. The system also reveals links to high-risk regimes such as Russia, Iran, Venezuela, and North Korea, allowing enforcement agencies to assess geopolitical as well as criminal risk factors.

A table view of all risky vessels inbound to the U.S showing risk indicators and key identifiers. Source: Windward Maritime AI™ Platform

This targeted list becomes a launch point for data-backed prioritization. Automated vessel screening provides deeper context without requiring manual research. In this instance, the system identified one inbound vessel that was already under sanctions and associated with an Iranian national designated by the U.S. Treasury for shipping Iranian LPG and crude oil. These links were surfaced in seconds, giving decision-makers actionable intelligence while there is still time to act.

By turning an unmanageable flood of maritime traffic into a concise, risk-prioritized list, AI-powered risk models enable agencies to work smarter, not harder — focusing resources where they are most likely to yield results, and doing so with the speed necessary to intercept and prevent smuggling before it reaches U.S. shores.

Early Detection and Intelligence: Acting Before Threats Materialize

While narrowing thousands of vessels down to a manageable, prioritized list is a powerful capability, it represents only the most basic function of AI-driven risk assessments for border security. True prevention requires more than reacting to what is already on its way.

In maritime border security, time is the most valuable commodity. The earlier an emerging threat is detected, the faster agencies can act — whether to interdict, investigate, or prevent. Yet early detection is not simply a matter of watching for known risks. While agencies can monitor for vessels already flagged in intelligence databases, the greater challenge lies in uncovering “unknown unknowns” — threats not yet on the radar.

In practice, this requires a tool capable of continuously scanning an entire area of interest for anomalies that signal possible vulnerabilities or illicit activity. Windward’s Early Detection capability does exactly this, focusing on high- and moderate-risk vessel populations to identify unusual concentrations and behaviors, such as dark activity, slow sailing or drifting, loitering, anchoring, and at-sea meetings.

All anomalies involving vessels with smuggling risk over the past 30 days, as of August 12, 2025. Source: Windward Early Detection

Example 1: Unusual Drifting Within the U.S. EEZ

On July 14, 2025, Windward’s Early Detection solution flagged a 150% increase in the number of vessels with smuggling risk drifting within the United States Exclusive Economic Zone (EEZ). The count jumped from an expected 10 vessels to 25, triggering an immediate alert.

Windward’s Early Detection Solution showing a sharp spike in vessels with smuggling risk drifting in U.S. EEZ

The anomaly was driven by vessels exhibiting behavioral indicators historically associated with smuggling — dark activity, unusual loitering, course deviations, and other deviations from their historical “pattern of life.” Such a sharp spike, so close to U.S. shores, warranted rapid investigation.

Windward’s MAI Expert™, a Gen AI–driven virtual subject matter expert, analyzed the anomaly in real time and connected it to wider trade and port disruptions across the United States. Between July 7 and 14, port activity had slowed sharply, with container processing down 8–18% and a noticeable drop in port employment. Recently imposed trade tariffs had caused rerouting, canceled port calls, and uncertainty across global supply chains.

The result: vessels drifting offshore while awaiting berth availability or new routing instructions. This congestion created reduced inspection capacity and longer wait times — a window of opportunity for illicit transfers, unauthorized activity, or smuggling attempts near the U.S. coastline.

Pinpointing Vulnerabilities
Once the anomaly was detected, Windward’s Intelligence Platform allowed analysts to drill into the contributing vessels and behaviors:

- Risk Profile: 2 high-risk vessels, 23 moderate risk.

- Flags of Convenience: 8 Panama, 5 Liberia, 3 Marshall Islands, 3 Singapore, 1 China.

- Vessel Types: 18 bulk carriers, 4 crude oil tankers — vessel classes often used in high-volume, low-visibility transfers.

- Mapping these events revealed clusters of drifting activity around major U.S. port approaches — geographic points where enforcement attention could be most impactful during times of disruption.

Example 2: Threats Emerging Beyond the Immediate Area of Interest

Not all threats originate within a nation’s borders. On another occasion, Windward’s Early Detection flagged a 422% increase in vessel meetings involving vessels with smuggling risk in the Venezuelan EEZ. Over a short period, 6 vessels conducted 6 commodity transfer meetings, primarily between tankers and cargo ships already flagged as high or moderate risk.

ts chart — A sharp spike in meetings between vessels with smuggling risk in Venezuelan waters. Source: Windward Early Detection

What stood out was the presence of three additional vessels in these meetings — none previously flagged for smuggling. Their participation alongside already risky vessels instantly elevated them to new inspection priorities. This kind of detection — finding fresh targets through their associations — is a critical capability for uncovering illicit networks that might otherwise remain invisible to enforcement agencies.

One of these new targets illustrates the importance of looking beyond a single risk category. It is a Marshall Islands–flagged bulk carrier owned by a Chinese company. While it was initially classified as low risk for smuggling, the vessel carries a high compliance risk due to its associations with three sanctioned regimes — Iran, Venezuela, and Russia. Such multi-regime connections, when coupled with unexplained participation in commodity transfers involving higher-risk vessels, signal the need for enhanced scrutiny and potential interdiction.

By surfacing these hidden connections, Early Detection expands the scope of actionable intelligence — moving beyond known actors to uncover new vessels, entities, and networks that can be monitored, inspected, or interdicted before they engage in further illicit activity.

From a Single Vessel to a Network

Identifying a single high- or moderate-risk vessel is rarely the end of the story. In maritime smuggling, one vessel often points to a larger network of related ships, owners, and operators. These connections can reveal patterns, shared tactics, and broader illicit operations that would be invisible if viewed in isolation.

Returning to the Venezuelan anomaly, it is good investigative practice to check whether the vessels involved share ownership or operational ties. In the same way that risky vessels often adopt methods used by other bad actors, they frequently belong to fleets that operate with similar tactics and routes.

In this case, the six smuggling-risk vessels involved in ship-to-ship commodity transfers in Venezuela revealed exactly such a link:

Two moderate-risk bulk carriers shared the same owner — a high-risk, Venezuela-based company operating a fleet of four bulk carriers, all of which are classified as moderate smuggling risk.

This insight turns a single data point into a fleet-level intelligence lead. The company’s entire fleet is now worth monitoring. By saving the full set of vessels as a “vessels of interest” list, analysts can run targeted queries to identify additional meetings, suspicious port calls, or high-risk behaviors over a defined period — such as the past 30 days.

This approach enables agencies to:

Expand from tracking a single suspicious vessel to monitoring a connected fleet.
Map where meetings and illicit transfers tend to occur, creating geographic risk indicators.
Identify known accomplices and potential facilitators.
Build a watchlist that prioritizes enforcement resources toward an entire operational network.

By moving from the individual vessel to the network, enforcement gains the ability to track not just a single incident, but the methods, infrastructure, and partners that enable smuggling operations — increasing the likelihood of meaningful, long-term disruption.

A Repeatable Pattern

Every smuggling incident is an opportunity to learn. By studying specific cases, enforcement agencies can uncover the methods and sequences of activity used by illicit actors — then watch for other vessels adopting the same playbook. This shift from case-by-case interdiction to pattern-based detection multiplies the impact of each investigation.

Consider the case of the Sweet Miri. In March 2024, the Nigerian Navy intercepted the vessel carrying approximately 2 million liters of suspected stolen crude oil without proper documentation or approval. The crew of 13 was detained, and the vessel was impounded for investigation. The interdiction came amid the Navy’s intensified anti-oil theft campaign, which has included new verification procedures at Nigeria’s five major export terminals, expanded patrol capabilities, and a two-year crackdown resulting in 76 vessel arrests and the destruction of more than 800 illegal refining sites.

Smuggling methods are rarely unique — successful tactics are often repeated. In the Sweet Miri case, the sequence was clear: dock at Bonny Port → go dark (AIS off) → reappear in Nigerian waters. This distinctive behavioral pattern can be used as a template to detect other potential oil theft attempts.

Running a search for tankers displaying this same sequence in the 30 days prior to August 11, 2025, Windward identified two high-risk vessels:

Vessel 1, a Guyana flagged sanctioned tanker called at Bonny Port on July 22, 2025, then reported a draft increase – from 11.3 to 21.4 – before beginning a three-day period of dark activity while in Nigerian waters. The vessel is owned by a Hong Kong–based company sanctioned by the U.S. Office of Foreign Assets Control (OFAC) under the Global Terrorist designation due to its association with the Iranina regime. Previous owners were also OFAC-sanctioned entities.

Vessel 2, another Guyana-flagged tanker, is sanctioned by OFAC under the Global Terrorist program in November 2022. Open-source reporting links the vessel to sanctions evasion and illicit oil trading as part of China’s “dark fleet” — purchasing Iranian oil in violation of U.S. sanctions. The ship was reported to have conducted a ship-to-ship transfer with a sanctioned National Iranian Tanker Company vessel in March 2024. In July 2025, it called at Bonny Port, and went dark on July 25 — where it remains at the time of reporting.

Key Takeaways:

Reverse engineering smuggling methods can surface new investigative leads and uncover other vessels using the same tactics.
Smuggling-risk vessels are often multi-threat targets, potentially linked to other illicit activities such as sanctions evasion, terrorism financing, or environmental crimes.
Pattern-based detection turns one successful interdiction into a scalable detection model, applicable across fleets, regions, and timeframes.

By connecting historical cases to real-time monitoring, enforcement agencies can spot threats earlier, target inspections more effectively, and disrupt operations before they succeed.

Conclusion and Key Takeaways

Maritime smuggling presents a challenge defined by scale, complexity, and adaptability. With thousands of vessels moving across global waters each day, and illicit actors continuously refining their methods, traditional enforcement approaches struggle to keep pace. Artificial Intelligence provides the tools to close that gap — not by replacing human expertise, but by multiplying its reach, speed, and impact.

The case studies and examples in this paper demonstrate how AI-powered capabilities — from Smuggling Risk models to Early Detection and pattern-based analysis — can transform border security operations:

Prioritize What Matters Most – Narrow thousands of inbound vessels to a manageable, data-driven shortlist for inspection, focusing resources where they are most likely to yield results.
Act Before Threats Arrive – Detect anomalies and vulnerabilities early, including “unknown unknowns” that fall outside existing watchlists, to enable proactive interdiction.
Expand from the Individual to the Network – Move beyond single vessels to identify connected fleets, owners, accomplices, and operational networks, enabling broader disruption.
Turn Incidents into Intelligence – Reverse engineer specific smuggling cases to uncover repeatable patterns and detect other vessels adopting the same tactics.
Connect the Dots Across Risks – Recognize that smuggling vessels may also present compliance, sanctions, or geopolitical risks, requiring a multi-dimensional approach to enforcement.

Ultimately, the shift is from reactive detection to predictive prevention. By integrating AI-driven insights into daily workflows, agencies can continuously learn from each case, adapt to evolving threats, and apply scarce enforcement resources where they will have the greatest impact.

The stakes are high: every missed opportunity to detect and intercept illicit activity is a win for transnational crime and a loss for national security. But with AI-driven maritime intelligence, governments can see more, act faster, and enforce smarter — closing the gap between maritime domain awareness and operational action.

Detect to Protect. See Windward in Action.

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