The maritime industry has a dirty secret sailing across the world’s oceans. A growing armada of so-called “shadow fleet” vessels now operates largely outside international oversight, transporting sanctioned Russian, Iranian, and Venezuelan oil while evading the scrutiny faced by legitimate shipping. Estimates vary, but analysts now place the number of such vessels in the high hundreds to well over a thousand—many of them aging tankers, lightly regulated, poorly insured, and operating through opaque ownership structures.
This is not merely a sanctions-enforcement problem. It is an environmental and financial risk accumulating quietly at sea. Older vessels operating outside recognised insurance regimes pose outsized spill and collision risks, particularly as global sea lanes grow more congested. When accidents occur, cleanup and liability costs—often running into hundreds of millions of dollars—fall not on shipowners or insurers, but on coastal states and taxpayers.
Traditional monitoring has proven inadequate. Shadow vessels exploit gaps in oversight through ship-to-ship transfers in international waters, frequent reflagging, falsified documentation, transponder manipulation, and corporate structures designed to frustrate investigators. Human analysts, however capable, cannot process the sheer volume of maritime, financial, and corporate data required to track these operations in real time.
This is precisely where artificial intelligence offers transformative potential.
Repricing Sanctions Evasion
The economics of shadow shipping become clearer when viewed through the lens of option pricing. Operators are effectively purchasing a put option on regulatory enforcement. They accept higher operating costs—older hulls, dubious flags, opaque ownership, substandard insurance, and reputational risk—in exchange for the right to exploit sanctions arbitrage when market conditions allow.
That option’s value depends on two variables: the discount on sanctioned crude relative to global benchmarks, and the probability of detection and enforcement. When enforcement risk is low and price spreads are wide, the option sits comfortably in the money.
AI monitoring changes that calculus. By increasing the probability of detection—even imperfectly—algorithmic surveillance raises the effective premium shadow operators must pay. Each marginal increase in enforcement likelihood shifts the breakeven point. Some operators exit. Others demand deeper discounts. In aggregate, supply tightens and revenues flowing to sanctioned regimes decline.
Enforcement need not be perfect to be effective; it merely needs to be credible.
What the Technology Can Do
Modern AI systems can synthesise satellite imagery, automatic identification system (AIS) data, port calls, draft measurements, corporate registries, insurance records, and financial flows into coherent risk assessments. Machine-learning models excel at precisely the pattern-recognition tasks that overwhelm human teams: identifying suspicious loitering in known transfer zones, flagging inconsistencies between declared cargo and vessel draft, or linking shell companies across jurisdictions.
These tools are already deployed commercially for route optimisation and congestion management. Their application to sanctions enforcement is not a technological leap, but a political one.
With sufficient coordination, AI could identify likely ship-to-ship transfers before they occur, map ownership networks faster than they can be restructured, and flag anomalous vessel behaviour within hours rather than weeks.
Follow the Insurance
One of the most powerful—and underused—levers lies in maritime insurance. Roughly 90 percent of legitimate global tonnage is covered by the International Group of Protection and Indemnity (P&I) Clubs, which provide the liability coverage required for port access and coastal transit. Shadow vessels operate largely outside this system.
That does not mean they are uninsured, but that their coverage is often opaque, thinly capitalised, or provided by entities unable to meet claims at scale. When accidents occur, the liability gap becomes stark.
AI-enabled financial analysis can identify which insurers, brokers, banks, and reinsurers facilitate shadow operations—sometimes knowingly, sometimes not. Targeted pressure on these nodes may prove more effective than interdiction at sea. Making shadow shipping genuinely uninsurable would alter behaviour faster than patrol vessels ever could.
Where the IMO Fits
The International Maritime Organization has a quiet but consequential role to play. While the IMO is not an enforcement body, it sets the technical and safety standards—on vessel identification, safety management systems, and pollution prevention—on which enforcement depends. AI-enhanced monitoring can be aligned with existing IMO frameworks such as port state control, mandatory AIS carriage, and environmental compliance regimes.
Vessels repeatedly flagged by algorithmic risk models could face intensified inspections, denial of port services, or restrictions under existing international maritime law—without the need for new sanctions authorities. In this sense, AI does not bypass multilateral governance; it reinforces it, allowing existing rules to be applied consistently to vessels that currently operate in regulatory blind spots.
Trade Corridors at Risk
The shadow fleet problem cannot be separated from broader shifts in global connectivity. Projects such as the India–Middle East–Europe Economic Corridor rest on assumptions of predictable maritime security in precisely the waters where shadow activity concentrates.
Recent disruptions in the Red Sea have shown how quickly maritime insecurity cascades through global supply chains. Shadow vessels operate under different risk incentives: their cargoes are already outside legal commerce, and their operators may accept hazards legitimate shipping cannot. That asymmetry undermines corridor reliability and raises risk premiums for infrastructure investments from ports to rail links.
The Demand-Side Constraint
None of this eliminates the underlying political reality: shadow shipping exists because major buyers remain willing to purchase sanctioned oil. Countries such as China and India have dramatically increased imports of discounted crude, prioritising energy security and price stability over Western sanctions compliance.
This limits what supply-side enforcement can achieve. But it does not render it futile. Raising transaction costs still matters. If sanctions evasion becomes more complex, more expensive, and more visible, discounts widen and volumes shrink. Revenues fall even if trade does not disappear.
Governing the Algorithmic Eye
Critics rightly warn that surveillance systems rarely remain confined to their original purpose. Tools built to track sanctions evasion could be misused to monitor legitimate commerce or gather competitive intelligence. False positives could disrupt lawful trade.
These concerns warrant safeguards, transparency, and oversight—but not paralysis. Financial markets made a similar transition after 2008, when AI-driven anti-money-laundering systems became standard. Maritime enforcement is following a comparable path.
Table: How AI Monitoring Changes the Shadow Fleet Enforcement Equation
Dimension
Status Quo (Pre-AI)
AI-Enhanced Monitoring Impact
Vessel detection
Fragmented, reactive, analyst-led
Continuous, probabilistic, real-time risk scoring
Ship-to-ship transfers
Identified after the fact
Predicted in advance via pattern recognition
Ownership opacity
Manual tracing, slow
Network analysis across jurisdictions
AIS manipulation
Hard to prove intent
Behavioural anomalies flagged algorithmically
Insurance enforcement
Binary (insured / uninsured)
Granular mapping of insurers and reinsurers
Port state control
Random inspections
Risk-weighted inspections under IMO rules
Financial sanctions
Ex post investigations
Pre-emptive identification of payment flows
Cost of evasion
Low and predictable
Higher, volatile, increasingly non-linear
Environmental risk
Latent, poorly priced
Explicitly incorporated into enforcement
Incentives
Shadow shipping profitable
Arbitrage margins compressed
For the Middle East and North Africa, the stakes are particularly high. The region sits astride the world’s most strategically congested maritime chokepoints—the Strait of Hormuz, Bab el-Mandeb, and the Suez Canal—through which both legitimate trade and shadow shipping transit. Several MENA states are simultaneously energy exporters, logistics hubs, and investors in next-generation connectivity corridors linking Asia, Africa, and Europe. This creates an asymmetric risk profile: while the region benefits from high volumes of maritime traffic, it bears disproportionate environmental and reputational costs when poorly insured shadow vessels operate in its waters. AI-enabled maritime monitoring offers MENA governments a way to strengthen port state control, protect coastal ecosystems, and reassure global investors—without aligning explicitly with any single sanctions regime. Framed as maritime safety, environmental protection, and infrastructure risk management, algorithmic oversight allows MENA states to harden standards while preserving strategic autonomy in an increasingly fragmented global order.
AI will not eliminate the shadow fleet. But by repricing the risks that sustain it, algorithmic monitoring can push sanctions evasion from a profitable arbitrage into a marginal gamble.
If the technology exists to bring these ships into the light—and it does—the danger lies in leaving it unused, waiting for the next spill, the next disruption, and the next reminder that opacity at sea carries costs far beyond the horizon.
Looking ahead, the evolution from analytical AI to agentic AI could further alter the enforcement landscape. Agentic systems do not merely flag risks; they can act within defined constraints—initiating data pulls, updating risk scores dynamically, coordinating alerts across agencies, and recommending specific interventions as conditions change. In maritime monitoring, this could mean AI agents that continuously adjust patrol priorities, escalate inspections under port state control regimes, or trigger financial compliance reviews when vessel behaviour crosses predefined thresholds. Properly governed, such systems would not replace human judgment but compress decision cycles that currently stretch for weeks into hours. The risk, of course, is delegation without accountability. Agentic AI requires strict human-in-the-loop oversight, auditable decision logs, and clear legal boundaries. But if designed responsibly, it offers a way to move from passive surveillance to adaptive enforcement—matching the speed and flexibility of the shadow fleet itself. In an environment defined by rapid arbitrage and regulatory evasion, static monitoring is no longer enough; enforcement, like evasion, is becoming dynamic.