AI is transforming maritime surveillance by enhancing real-time monitoring, dark vessel detection, and predictive capabilities, while raising concerns around cybersecurity, ethics, and implementation.
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Maritime surveillance, central to Maritime Domain Awareness (MDA), involves the monitoring, analysis, and forecasting of oceanic activities to detect and track potential threats. Traditional tools such as Automatic Identification System (AIS) and Radar remain essential for tracking vessel movement. However, their effectiveness is constrained by reliance on limited sensors, manual oversight, and frequent detection delays, making them inadequate in addressing modern security concerns. This is especially true given the vast maritime zones, stretching across coastal borders, Exclusive Economic Zones (EEZs), and strategic chokepoints, that must be monitored.
Human-led surveillance struggles with scale, fatigue, and monitoring gaps. These shortcomings are also evident when dealing with “dark vessels” — ships that deliberately disable their GPS-based AIS or operate without proper identification, making them nearly invisible to standard surveillance systems. Illicit and criminal activities like unregulated fishing, smuggling, and piracy are growing more complex, with a wide spectrum of actors, including state and non-state actors, increasingly using dark fleets to evade detection. For instance, Russia is estimated to operate over 1400 dark vessels to bypass international sanctions on its oil and liquefied natural gas exports. Such clandestine operations exploit vulnerabilities in standard maritime surveillance frameworks, underscoring the pressing need for advanced solutions that can detect and monitor activities at sea with greater accuracy and speed.
India is also undertaking AI-driven initiatives to bolster its maritime surveillance framework, such as integrating AI-enabled swarm drone systems that autonomously patrol vast areas, detect anomalies, and relay real-time data across ground-based systems, surface ships, and aerial platforms for enhanced situational awareness.
In this light, Artificial Intelligence (AI) offers new opportunities to close these gaps, enabling real-time data integration, anomaly detection, and predictive analysis to strengthen MDA. India is also undertaking AI-driven initiatives to bolster its maritime surveillance framework, such as integrating AI-enabled swarm drone systems that autonomously patrol vast areas, detect anomalies, and relay real-time data across ground-based systems, surface ships, and aerial platforms for enhanced situational awareness. However, AI adoption prompts important questions about its limitations, reliability, accountability, and cybersecurity, all of which must be carefully navigated to ensure effective deployment.
Foundational technologies for maritime surveillance typically include AIS and Radar systems. The former provides data to coastal authorities on a vessel’s identity, location, and movement, enhancing navigation safety and collision avoidance. However, its value for security is limited as AIS signals can be spoofed, manipulated, or switched off entirely, allowing smugglers, traffickers, and illegal fishers to operate undetected and evade enforcement. Similarly, radar systems track vessels by bouncing electromagnetic waves off objects, aiding in collision avoidance and navigation. Yet, their capacity to detect small or low-profile vessels remains inadequate, compounded by notable blind spots in close-range detection.
Both systems rely heavily on manpower and manual monitoring, and are thus prone to human error and fatigue. They fail to provide comprehensive coverage and real-time updates. Given the complex nature of threats, ranging from piracy and smuggling to drone, missile, and cyberattacks, delayed threat detection may have major repercussions, including endangering crew members, damaging cargo, disrupting shipping schedules, and jeopardising national security. Traditional measures often lag behind the evolving tactics of malicious actors, making sole reliance on human intervention insufficient. Addressing these challenges requires a shift towards more technologically advanced solutions to maritime security.
Geospatial AI is emerging as a powerful complement to existing surveillance tools. By fusing satellite imagery, radar, AIS data, and environmental inputs, it creates a dynamic, multi-layered picture of maritime activity.
AI is revolutionising maritime surveillance by offering advanced capabilities that go beyond the capacity of traditional tools. A major strength of AI is its ability to analyse extensive data in real-time. Machine learning (ML) algorithms identify trends and outlier events that often elude conventional human analysis. This capability enables continuous real-time monitoring and anomaly detection, identifying threats such as erratic vessel movements or suspicious activities gleaned from open-source intelligence. Through enhanced object recognition and tracking, AI offers enhanced precision in classifying maritime entities, distinguishing between vessel types, and detecting navigational hazards like debris or rogue waves.
Geospatial AI is emerging as a powerful complement to existing surveillance tools. By fusing satellite imagery, radar, AIS data, and environmental inputs, it creates a dynamic, multi-layered picture of maritime activity. Advanced algorithms detect anomalies such as unauthorised fishing in protected zones and vessel loitering. A particularly transformative application lies in enhancing dark vessel detection. These so-called “ghost ships” are a key instrument in carrying out covert activities, such as illegal fishing, smuggling, and piracy. AI-enhanced data sources can support their detection by examining movement patterns and predicting suspicious behaviour. Real-world applications of AI-driven platforms are already demonstrating their transformative potential. The table below summarises key developments:
| AI Application | Maritime Surveillance Dimension | Company & Country | Details |
| Seagull Surveillance | Situational awareness & anomaly detection | BrainCreators, The Netherlands | Integrates AIS data, weather, and voyage records; operational in Scheveningen Harbour; expanding to the UK |
| SatShipAI | Vessel tracking using satellite imagery | NodalPoint Systems, Greece | Uses satellite data to track vessel movements and detect illegal activity; remains in development, pilot-stage without formal adoption |
| Skylight System | Dark vessel detection | Allen Institute for AI, USA | Analyses data from NASA, European Space Agency, and Maxar Technologies to detect illegal activity via behavioural pattern analysis and predictive modelling |
| Artificial Intelligence Retraining in Space (AIRIS) | Satellite-based object identification | Mitsubishi Heavy Industries (MHI), Japan | Captures images of Earth’s surface and autonomously identifies areas containing target objects. Currently set for demonstration aboard JAXA’s RAISE-4 satellite |
| Global Fishing Watch | Illegal Fishing Monitoring | International NGO (Global Fishing Watch) | Tracks over 65,000 vessels using AI software to process radar and optical satellite imagery, including non-AIS vessels, for real-time detection of illegal fishing. |
Apart from these, advancements in AI/ML also allow for the development of sophisticated autonomous vessels. Unmanned Surface Vessels (USVs) and Autonomous Underwater Vehicles (AUVs) perform a variety of crucial tasks such as underwater surveillance, mapping, and data collection in high-risk or hard-to-reach areas. As AI models continue to evolve with feedback loops and real-time retraining mechanisms, dark fleet detection is set to improve, strengthening efforts against illegal activity and enhancing ocean governance.
India has also made strides in integrating AI into its maritime surveillance architecture. The Ministry of Defence, in December 2023, signed a Rs 1,600 crore agreement for six AI-equipped patrol vessels for the Indian Coast Guard, recognising the importance of AI integration in addressing maritime challenges. Equipped with modern technology, these ships will be instrumental in improving surveillance, enforcing maritime laws, conducting search and rescue missions, and providing humanitarian assistance. In parallel, the Defence and Research Development Organisation (DRDO) is actively developing AUVs and USVs for the Indian Navy’s surveillance missions.. The Navy is also investigating the use of AI to enhance maritime surveillance via swarm drone technology and hardware such as tripwires and AI-based cameras for object detection, facial and voice recognition, and perimeter surveillance. Public-private partnerships (PPPs) are also underway, with Bharat Electronics and Blurgs Innovations jointly developing the AI-powered TRIDENT system, leveraging multi-sensor fusion, real-time analytics, and predictive modelling for effective monitoring of anomalous activity in seawaters by the Navy and the Coast Guard. Nonetheless, India’s operational use of this technology remains in the early stages. Further incorporation of AI would enable the country to transition from a reactive to a predictive maritime security posture.
India’s operational use of this technology remains in the early stages. Further incorporation of AI would enable the country to transition from a reactive to a predictive maritime security posture.
AI integration poses several challenges to the maritime industry. At the forefront is the increased vulnerability to cyberattacks. AI-powered tools can detect and mitigate threats, but adversaries can also harness the technology to execute highly sophisticated attacks, including automated cyberattacks, exploiting software dependencies, and manipulating the training data of these AI models. Thus, strengthening cybersecurity measures becomes critical to safeguard maritime infrastructure from newer and evolving threats. AI adoption also raises key ethical and regulatory concerns, particularly around accountability for decisions made by unsupervised autonomous vessels, including navigation, route planning, and collision avoidance. With limited human oversight, assigning responsibility in the event of an error becomes complex. Additionally, overdependence on automation may lead to operator complacency, reducing their ability to respond effectively during AI system failures or unforeseen scenarios.
This underscores the need for comprehensive training of maritime personnel to ensure they can effectively manage and complement these technologies, thereby minimising overreliance. Operational challenges may also hinder effective AI usage, as extreme weather, ocean currents, and underwater terrain may disrupt system performance. This underscores the need to improve sensors, navigation systems, and algorithms for greater reliability and functionality. Addressing these challenges, the Indian Navy is actively equipping AI/ML training to its personnel. The path forward for India must include sustained investment in domestic research and development. Equally important is strengthening collaborations with international partners to share best practices and shape regulatory standards. As maritime threats grow more complex, the strategies to address them must evolve accordingly, embracing innovation, adaptability, and advanced technological solutions.
Anusha Guru is a Research Intern at the Observer Research Foundation.
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