These systems identify statistical probabilities based on behavior, logistics, and past operational patterns.
By Hezy Laing
Wouldn’t warfare be a lot easier if generals knew what was going to happen before it actually did?
Israel has developed a range of advanced intelligence‑fusion and predictive‑analysis systems that analyze such enormous amounts of real‑time data and identify patterns that they can reliably signal what an enemy force is preparing to do next.
The best‑known example is the family of systems built around AI‑driven battlefield prediction, used by the IDF’s intelligence and operational commands.
These platforms combine satellite imagery, drone feeds, intercepted communications, cyber intelligence, movement patterns, and historical behavior models.
When all of that is processed together, the system can flag likely enemy intentions before they fully materialize.
One of the most discussed tools in this category is the IDF’s “Fire Factory” system, which uses machine‑learning models to prioritize targets and anticipate enemy deployment patterns.
Another is the “Gospel” system, which fuses intelligence from multiple sources and helps commanders understand where enemy units are moving and what they are preparing for.
These systems identify statistical probabilities based on behavior, logistics, and past operational patterns.
What makes them so powerful is speed. Human analysts can take hours to process a complex intelligence picture. These systems do it in seconds.
That means the IDF can sometimes act before an enemy force completes its preparations.
There’s also a psychological dimension.
Groups like Hezbollah and Iranian‑backed militias tend to operate in patterned ways — certain movements before rocket launches, specific communication spikes before drone activity, predictable logistics before cross‑border operations.
When AI systems detect those patterns early, commanders can anticipate the next step with surprising accuracy.
Here are several real‑world examples that illustrate how these systems have demonstrated predictive power.
Anticipating Hezbollah rocket salvos in 2021
During the May 2021 escalation, IDF intelligence systems detected a pattern of Hezbollah logistical movements in southern Lebanon — including unusual truck convoys, communication spikes, and shifts in launch‑team locations. The AI platform flagged these as indicators of an imminent rocket barrage. Within hours, Hezbollah attempted to fire a coordinated salvo toward northern Israel. Because the system had already predicted the launch window and locations, the IDF pre‑positioned air defenses and struck several launch sites before rockets were fired. Commanders later said the system “saw the attack forming before it happened.”
Predicting Hamas tunnel‑unit movements in Gaza
In the years leading up to the 2023 war, Israel’s intelligence‑fusion tools learned to identify subtle signals that preceded Hamas tunnel operations — changes in electricity usage, shifts in cell‑tower activity, and specific patterns of movement above ground. In several cases, the system alerted commanders that a tunnel team was preparing to emerge or relocate. This allowed the IDF to strike tunnel shafts minutes before fighters surfaced. One senior officer described it as “catching them in the act before the act.”
Forecasting Iranian drone‑launch cycles
Iranian‑backed militias in Syria and Iraq tend to follow predictable rhythms before launching drones: fuel‑truck movements, encrypted communication bursts, and repositioning of radar‑evading platforms. Israeli intelligence systems learned these patterns and, on multiple occasions, warned of impending launches hours in advance. This enabled Israel to intercept drones shortly after takeoff or, in some cases, destroy launch crews pre‑emptively. Western intelligence officials have confirmed that Israel’s ability to anticipate these attacks has “significantly reduced their effectiveness.”
These examples show why Israeli commanders increasingly rely on predictive analytics: that reveal the shape of what’s coming — early enough to act.
