Integrating drones into civil airspace in a safe manner has forced air navigation service providers to revise risk assessment procedures.
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A useful comparison is road traffic, where autonomous vehicles are introduced alongside traditional cars
For air navigation service providers (ANSP), drones will fundamentally change how risk is understood and how it is managed in controlled airspace.
Understanding why a system made a decision is critical in safety-critical environments
Andres Van Swalm, CEO, Unifly
Air traffic controllers are trained on manned aviation, where safety is based on certified aircraft, predictable behaviour and such well-defined concepts as fixed separation minima. Drones, especially at low altitude, introduce a far more context-dependent risk picture. In many cases the ground risk, such as flying over a busy highway, can be higher than the risk in the air.
“Simply restricting drone flights is not a safe solution,” says Andres Van Swalm, CEO, Unifly. “It does not remove demand, but pushes operations outside the system, leading to illegal flights, reduced visibility, and higher risk. Safety instead depends on risk-based airspace design, where drone flights can be automatically approved in areas with low air risk, supported by automation and minimal air traffic controller (ATCO) involvement.”
“Agility by design is the third component. A UTM platform cannot be deployed once and then left unchanged for months. A drone released today is already outdated tomorrow. The drone ecosystem evolves extremely fast, and UTM systems must be able to adapt and evolve at the same pace without compromising safety.”
Technological solutions
Retraining ATCOs
ATCOs will clearly be vital but the aim should not be turn them into drone controllers. ATCOs already operate in complex, high-workload environments, and adding routine drone traffic management tasks would increase, rather than reduce, safety risk.
Drone integration should therefore be designed so that ATCO involvement is minimal and strictly exception-based, for example during runway operations or when a drone poses a direct risk to manned aviation. According to Swalm, automation, smart airspace design and dedicated UTM services should handle the vast majority of drone operations without requiring continuous controller interaction. An ATCO is responsible to separate manned-manned flights.
“When controller action is needed, it should be simple and effective,” Swalm suggests. “A good example is a Dynamic Airspace Restriction (DAR) in U-space, where an ATCO can activate a temporary no-fly zone with a single action, ensuring that all drones automatically land or vacate the area. This approach protects safety while keeping controller workload to the minimum.”
The drone industry is still young and data and experience are being eagerly assembled. That is continuously improving how risks are understood and mitigated. For ANSPs and regulators, this also requires a cultural shift. Traditional system deployments are not well suited to a rapidly evolving drone ecosystem. UTM systems and the organisations operating them must adopt a more agile, learning-driven approach, while keeping safety as the non-negotiable foundation.
Safe integration also requires real-time tracking. ATCOs need to be able to identify drones and know where drones are flying, especially for beyond visual line of sight (BVLOS) operations.
But the technological innovation doesn’t end there. Automation and improved decision support will make a big difference. Digitised approval workflows, automated validation, and data-driven risk assessment can significantly reduce manual workload and operational variability, while analytics help anticipate safety needs as traffic scales.
That said, AI must be introduced carefully in aviation, says Swalm. Clear operational scope, robust validation, human oversight, strong cybersecurity and data-protection safeguards are essential, especially when AI is used for anomaly detection, conflict detection and resolution or prioritisation.
“Explainable AI is particularly important: understanding why a system made a decision is critical in safety-critical environments,” says Swalm. “AI should not replace safety thinking but support it. As with drone integration itself, the approach must be incremental. Deploy, monitor, learn, and improve so that trust, resilience, and operational confidence are ensured.”
Best practice
Staying ahead of the curve
Swalm concludes that best practice in UTM starts with automation by design. Drones are digital by nature, they operate on binary logic, so manual processes and human coordination should be avoided wherever possible. If a process cannot be automated, it will not scale safely.
“Interoperability by design is essential,” says Swalm. “UTM is always deployed as a system of systems. It must be able to integrate with drones, ATM systems, other UTM systems, as well as such systems as drone detection. This requires industry standards and open interfaces to ensure reliable information exchange and coordination.
Unmanned Aircraft System Traffic Management (UTM) was designed from the outset with automation, modularity, and agility in mind, which makes it well suited to accommodate new types of aircraft, including air taxis. Although these vehicles introduce additional parameters, the digital nature of UTM allows such changes to be integrated without fundamentally redesigning the system.
The greater challenge lies in integration with legacy ATM systems. Modern systems will initially adapt to existing infrastructure, but legacy systems must also be open to new entrants rather than resist them.
“This transition will be gradual,” says Swalm. “A useful comparison is road traffic, where autonomous vehicles are introduced alongside traditional cars. They coexist today, adapt step by step, and over time modern systems will increasingly shape how the overall ecosystem operates.”
Integrating drones into civil airspace in a safe manner has forced air navigation service providers to revise risk assessment procedures.
Understanding why a system made a decision is critical in safety-critical environments
Andres Van Swalm, CEO, Unifly
Swalm concludes that best practice in UTM starts with automation by design. Drones are digital by nature, they operate on binary logic, so manual processes and human coordination should be avoided wherever possible. If a process cannot be automated, it will not scale safely.
“Interoperability by design is essential,” says Swalm. “UTM is always deployed as a system of systems. It must be able to integrate with drones, ATM systems, other UTM systems, as well as such systems as drone detection. This requires industry standards and open interfaces to ensure reliable information exchange and coordination.
Best practice
“Agility by design is the third component. A UTM platform cannot be deployed once and then left unchanged for months. A drone released today is already outdated tomorrow. The drone ecosystem evolves extremely fast, and UTM systems must be able to adapt and evolve at the same pace without compromising safety.”
Air traffic controllers are trained on manned aviation, where safety is based on certified aircraft, predictable behaviour and such well-defined concepts as fixed separation minima. Drones, especially at low altitude, introduce a far more context-dependent risk picture. In many cases the ground risk, such as flying over a busy highway, can be higher than the risk in the air.
“Simply restricting drone flights is not a safe solution,” says Andres Van Swalm, CEO, Unifly. “It does not remove demand, but pushes operations outside the system, leading to illegal flights, reduced visibility, and higher risk. Safety instead depends on risk-based airspace design, where drone flights can be automatically approved in areas with low air risk, supported by automation and minimal air traffic controller (ATCO) involvement.”
For air navigation service providers (ANSP), drones will fundamentally change how risk is understood and how it is managed in controlled airspace.
Technological solutions
Safe integration also requires real-time tracking. ATCOs need to be able to identify drones and know where drones are flying, especially for beyond visual line of sight (BVLOS) operations.
But the technological innovation doesn’t end there. Automation and improved decision support will make a big difference. Digitised approval workflows, automated validation, and data-driven risk assessment can significantly reduce manual workload and operational variability, while analytics help anticipate safety needs as traffic scales.
That said, AI must be introduced carefully in aviation, says Swalm. Clear operational scope, robust validation, human oversight, strong cybersecurity and data-protection safeguards are essential, especially when AI is used for anomaly detection, conflict detection and resolution or prioritisation.
“Explainable AI is particularly important: understanding why a system made a decision is critical in safety-critical environments,” says Swalm. “AI should not replace safety thinking but support it. As with drone integration itself, the approach must be incremental. Deploy, monitor, learn, and improve so that trust, resilience, and operational confidence are ensured.”
Retraining ATCOs
ATCOs will clearly be vital but the aim should not be turn them into drone controllers. ATCOs already operate in complex, high-workload environments, and adding routine drone traffic management tasks would increase, rather than reduce, safety risk.
Drone integration should therefore be designed so that ATCO involvement is minimal and strictly exception-based, for example during runway operations or when a drone poses a direct risk to manned aviation. According to Swalm, automation, smart airspace design and dedicated UTM services should handle the vast majority of drone operations without requiring continuous controller interaction. An ATCO is responsible to separate manned-manned flights.
“When controller action is needed, it should be simple and effective,” Swalm suggests. “A good example is a Dynamic Airspace Restriction (DAR) in U-space, where an ATCO can activate a temporary no-fly zone with a single action, ensuring that all drones automatically land or vacate the area. This approach protects safety while keeping controller workload to the minimum.”
The drone industry is still young and data and experience are being eagerly assembled. That is continuously improving how risks are understood and mitigated. For ANSPs and regulators, this also requires a cultural shift. Traditional system deployments are not well suited to a rapidly evolving drone ecosystem. UTM systems and the organisations operating them must adopt a more agile, learning-driven approach, while keeping safety as the non-negotiable foundation.
A useful comparison is road traffic, where autonomous vehicles are introduced alongside traditional cars
Staying ahead of the curve
Unmanned Aircraft System Traffic Management (UTM) was designed from the outset with automation, modularity, and agility in mind, which makes it well suited to accommodate new types of aircraft, including air taxis. Although these vehicles introduce additional parameters, the digital nature of UTM allows such changes to be integrated without fundamentally redesigning the system.
The greater challenge lies in integration with legacy ATM systems. Modern systems will initially adapt to existing infrastructure, but legacy systems must also be open to new entrants rather than resist them.
“This transition will be gradual,” says Swalm. “A useful comparison is road traffic, where autonomous vehicles are introduced alongside traditional cars. They coexist today, adapt step by step, and over time modern systems will increasingly shape how the overall ecosystem operates.”
