How is AI rewriting the rules of engagement in modern warfare?
For decades, military strategists have imagined a future in which artificial intelligence would dictate the pace and outcome of warfare. That future is no longer theoretical. As tensions and confrontations between Iran and Israel have intensified in recent years, AI has quietly emerged as one of the most powerful forces operating behind the scenes of modern conflict.
The battlefield is no longer just a physical space of troops and artillery; it is a vast, invisible network of data, sensors, and machine learning models. In the current Iran-Israel conflict, AI is not just a support tool—it is the central nervous system of both offensive and defensive operations. Decisions that once required hours or even days of human analysis can now be made in milliseconds by algorithms. This transformation is clearly evident in modern air defence.
AI in Multi-Layered Air Defence
When Iran launched its massive, unprecedented salvos of Shahed drones, cruise missiles, and ballistic missiles at Israel, beginning with the April 2024 strikes and continuing through recent escalations, this sheer volume of attacks was designed to overwhelm traditional air defences. Human operators, no matter how highly trained, cannot simultaneously calculate the trajectories of hundreds of incoming projectiles in real-time. This proves that traditional air defence systems are insufficiently effective in such a high-combat situation.
This is where AI-driven defence systems become the ultimate barrier. Israel’s multi-tiered defence network, such as the Iron Dome, David’s Sling, and the Arrow 2 and Arrow 3 systems, relies heavily on machine learning algorithms to identify, classify, predict and prioritise threats as well as optimise real-time decision making.
Algorithmic Decision-Making in Air Defence
Detection and Classification
The first stage in any missile defence system is the detection of incoming objects. Radar systems continuously scan the surrounding airspace and detect objects by transmitting radio waves and receiving their reflections. The raw radar data must be processed to distinguish real threats from background noise such as birds, weather disturbances, or debris.
One of the most important tools used in radar signal processing is the Fast Fourier Transform (FFT). This algorithm transforms radar signals from the time domain into the frequency domain, allowing the system to detect motion and calculate the speed of incoming objects through the Doppler Effect.
Another important algorithm is the Constant False Alarm Rate (CFAR) detection method, which identifies potential targets while minimising false alarms caused by environmental noise.
Once an object is detected, machine learning models are used for classification. Algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest classifiers analyse features such as speed, size, and flight patterns. These models help determine whether the object is a drone, a flock of birds, or a high-velocity ballistic missile. Accurate classification is essential because it prevents unnecessary interception and ensures that resources are used efficiently.
Trajectory Prediction and Impact Assessment
Detecting a threat is only the first step. The next challenge is determining where it will land. Ballistic missiles follow trajectories influenced by gravity, atmospheric drag, and their initial launch speed.
Using mathematical equations, the system calculates the expected path of the projectile. To improve prediction accuracy, many missile defence systems use algorithms such as the Kalman Filter or Extended Kalman Filter (EKF). These algorithms continuously update predictions based on incoming radar measurements.
In complex scenarios where the motion of the object may be uncertain or non-linear (eg, Hypersonic missiles like Fatah-1 or Kinzhal), Monte Carlo-based algorithms are used. These techniques generate multiple possible trajectories and calculate the probability of impact at different locations. If the predicted landing point is an uninhabited area, the system may decide not to intercept the projectile, thereby conserving expensive interceptor missiles.
Interceptor Assignment
Interceptor assignment is one of the most critical decision-making functions in modern missile defence systems. When multiple threats are detected simultaneously, the defence system must quickly determine which interceptor missile or defence battery should engage each threat. Artificial Intelligence (AI) enables this process to occur within milliseconds by analysing threat data, evaluating possible responses, and selecting the most efficient interception strategy, as interceptor missiles are extremely expensive and available in limited quantities. Launching an interceptor unnecessarily could reduce the system’s ability to respond to more dangerous threats later in the attack. Algorithms such as dynamic programming, heuristic optimisation methods, and reinforcement learning techniques can be used to evaluate multiple engagement strategies simultaneously. These algorithms consider numerous potential engagement scenarios and select the one that minimises risk to defended areas.
The Danger of a Flash War
While the integration of AI into defence systems provides significant advantages, it also introduces new strategic risks. When both sides of a conflict deploy AI systems that react in milliseconds, the window for human intervention becomes increasingly narrow. There is a very real fear of a "flash war"—a scenario where an AI radar system misidentifies a threat, automatically launches a counter-measure, and triggers an algorithmic chain reaction of escalation before human leaders can even pick up the phone.
The Iran-Israel war demonstrated that AI-driven defence systems are no longer science fiction. We have handed the keys of warfare over to algorithms, prioritising speed and efficiency above all else.
The question now is whether humanity can build safety brakes into a machine that is designed to move faster than we thought.
Degree of Thought is a weekly community column initiated by Tetso College in partnership with The Morung Express. Degree of Thought will delve into the social, cultural, political and educational issues around us. The views expressed here do not reflect the opinion of the institution. Tetso College is a NAAC Accredited UGC recognised Commerce and Arts College. The editorial team includes Chubamenla, Asst. Professor Dept. of English and Rinsit Sareo, Asst. Manager, IT, Media & Communications. For feedback or comments please email: dot@tetsocollege.org
