Richard Varley, Senior Land Sales Manager, Chess Dynamics explores the growing drone threat to airports and the role of integrated, AI-enabled surveillance in strengthening detection and response.
Recent drone sightings at airports in Denmark and Norway have once again highlighted how disruptive relatively low-cost unmanned aerial systems (UAS) can be to critical national infrastructure.
Flights were grounded, runways closed, passengers delayed and revenues hit because small, commercially available devices entered restricted airspace.
These incidents are symptomatic of a wider challenge facing security and operations teams.
Although the accessibility and affordability of drones have made them valuable tools for legitimate commercial, recreational and industrial use it has also made the technology available to those who wish to gather intelligence, cause disruption or pose a security threat.
So how can airport management and security teams effectively and accurately detect, identify and manage the increasing threat from drones?
The rapid rise of UAS technology has reshaped how we think about security in the air domain.
Within the last decade, drones have changed from being niche tools, expensive and limited in range to widespread, inexpensive and increasingly powerful in all respects.
Recent conflicts have necessitated rapid innovation in the drone ecosystem.
On the modern battlefield, drones are used for reconnaissance, targeting and offensive operations, with low-cost models capable of inflicting significant damage.
While airports are very different to operational military environments, the underlying threat scenario is similar; the sudden appearance of small and fast, aerial vehicles moving unpredictably with unknown intent and potential payloads.
This places pressure on existing surveillance systems. The modern airspace is three-dimensional and constantly shifting, demanding detection capabilities that can operate across multiple sensors and handle data inputs in real time.
The key challenge for airport surveillance teams is two-fold: Managing the ever-evolving technology and increasing information streams with human capacity.
Manual monitoring of airspace for small, fast-moving drones requires intense concentration.
Operators must scan for potential threats, differentiate between birds, aircraft and drones and determine the appropriate response, often within seconds.
This presents two problems. Firstly, when multiple drones appear simultaneously or in rapid succession, the workload quickly becomes unmanageable.
Secondly, when monitoring during long shifts operator fatigue sets in, reducing situational awareness and response times and increasing the potential for misclassification.
Reducing this cognitive burden is one of the most important objectives in modern airspace security and where advancements in artificial intelligence (AI) can play a central role.
Through continuous, automated tracking and classification, AI-augmented systems can detect, identify and monitor multiple objects even in complex and cluttered environments.
Rather than requiring operators to constantly search for threats, AI-driven systems handle much of the detection workload autonomously, alerting the operator only when it has identified a potential threat and action is required.
This not only improves both reaction time and effectiveness but significantly reduces the cognitive burden on security teams.
In turn, lower cognitive burden means operators can sustain focus for longer periods, with fewer errors and false alarms, which also translates to less wasted time and greater confidence in system alerts.
Crucially, however, AI-enabled detection does not remove the human from the loop.
The human operator remains central to the system maintaining responsibility and oversight on potential threats.
This approach also elevates the security team’s role moving them from manual scanning to active decision-making.
This shift helps maintain focus, can help team morale and ultimately enhance security outcomes.
While AI can reduce the human workload, an effective airport surveillance solution must combine three essential stages into a single and cohesive framework:
True effectiveness comes from integration, where radar, electro-optical and AI-enabled tracking systems operate as one networked capability.
These stages are equally important and need to operate together, as fragmented systems that detect but cannot classify or can classify but lack a chain of command and communication channels, will not provide the required capability.
The incidents in Denmark and Norway have shown how easily critical infrastructure can be disrupted by small, low-cost drones.
As technology continues to advance, these threats will only become more capable and more frequent.
For airports, a resilient surveillance capability is no longer optional.
It is essential to maintaining airport operations and passenger safety.
By combining sensors, data, automation and human expertise, airports can detect incursions earlier, classify them more accurately and coordinate responses more effectively.
Artificial intelligence can play a crucial role in this evolution, not only enhancing detection and decision-making but also supporting operators to stay alert and focused in high-pressure environments.
By implementing a persistent surveillance solution, airports can maintain their security team’s resilience and keep pace with the rapidly changing threat landscape.
Persistent, integrated surveillance systems are already helping defence and security operators maintain awareness and control in these evolving environments.
Ultimately, protecting the skies above airports is about more than preventing disruption, it’s about ensuring confidence, continuity and safety in an increasingly complex and congested airspace.
