Artificial Intelligence Enabled Drones – What Does it Mean for C-UAS?

Artificial Intelligence (AI) has emerged as a game-changer across many industries, revolutionising the way we conduct our lives, and the drone industry is certainly seen as an early adopter of AI technology enhancements.

The integration of AI technology within commercial drones has unlocked unparalleled possibilities across multiple industry sectors including agriculture, surveillance & security, and logistics as well as search & rescue operations. AI has truly empowered drones with an ability to operate with enhanced autonomy and intelligence within complex environments. The weaponisation of AI empowered drones is also now firmly established as a military capability and we need look no further than Ukraine to see credible and indeed deadly examples of this technology in use.

So, an important question is, what are the most effective countermeasure options? How do we stop a hostile AI enabled drone attack in, say, city centres??

Well, thank goodness for jamming technology, right?

In truth, jamming by itself is quickly becoming less effective as a drone mitigation capability, and I’ll explain why. Jamming is the intentional transmission of powerful RF signals that cause interference with communication signals and disrupt the control link in some way between, in this case, the drone and its pilot. The rapid pace of drone AI technology development, however, could enable AI drones to possibly circumvent the effect of RF jamming signals.

To defend against the effects of jamming, modern drone software could possibly include AI algorithms that analyse, interpret, and categorise incoming signals, allowing them to detect the presence of jamming signals and take evasive action. AI-enabled drones could thereby adapt to
these changing environments and dynamically adjust.

AI enhanced drones could achieve this through advanced signal analysis capabilities enabling them to differentiate between normal communication and jamming signals. By analysing the frequency, power and patterns of incoming signals, AI algorithms could classify and identify jamming attempts with ever increasing accuracy. This classification would allow the drone to respond dynamically and autonomously change their flight path to bypass or even fly though RF jamming interference. What could this mean for the future of C-UAS?

By leveraging AI algorithms for signal analysis and dynamic route planning, drones can effectively detect, avoid, or fly through jamming signals. This ensures their uninterrupted operation on route to mission success, be that hostile or otherwise.

This represents an emerging threat, and the mitigation pendulum has already swung away from more traditional technologies. There are already reports of AI-enabled drones attempting to outmanoeuvre law enforcement agencies’ direct RF jamming operations, and we can only expect AI technology to grow exponentially in this area. RF jamming could rapidly become subordinate to modern mitigation technology capable of staying ahead of rapidly evolving software driven technology, such as AI.

It’s time to rapidly reevaluate effective drone mitigation tools within the C-UAS toolbox and in Part 2 of this blog we’ll do just that: What is the role of software-based cyber mitigation solutions capable of taking full control of even the most sophisticated AI enhanced drones?