A Canadian government evaluation validated Blind Tiger’s cellular control-plane approach for selectively denying commercial and AI-enabled drones while simultaneously enforcing prison-style managed access for mobile phones—all without RF jamming, payload access, or collection of personally identifiable information.
The trial was preceded by a rapid RF and cellular environment characterization using Blind Tiger’s Periscope survey capability. Periscope performed a wide-area, standards-compliant scan of the operational environment to identify incumbent cellular networks, supported bands, broadcast parameters, and observable control-plane behavior without interacting with user payloads.
Within minutes, Periscope identified multiple commercial LTE and 5G networks operating across several bands, including overlapping coverage zones, heterogeneous broadcast configurations, and dynamically varying signal conditions influenced by surrounding structures. This survey established the baseline RF geometry and network topology against which subsequent drone and device behavior was evaluated.
Survey results highlighted dense multipath conditions and significant spatial variability in reference signal quality—conditions increasingly exploited by modern drones. AI-enabled platforms demonstrated adaptive behaviors, including dynamic beamforming, opportunistic cell selection, and use of environmental reflections and obstructions to maintain connectivity while minimizing detectability.
Using Periscope-derived parameters, Blind Tiger instantiated controlled cellular trust anchors aligned with the observed environment. Rather than relying on brute-force interference, the system operated at the cellular control plane to identify, classify, and influence devices based on network behavior and signaling patterns.
In all observed cases, cellular-enabled drones were denied network access before reaching their intended targets. Mitigation occurred preemptively, without disrupting adjacent lawful services, demonstrating the effectiveness of network-level control against adaptive, AI-assisted platforms.
A central outcome of the trial was the clear operational separation between aerial threats and human-operated mobile devices. Cellular-enabled drones were selectively denied service based on behavior and policy, while mobile phones were managed under a prison-style access model informed by Periscope survey data.
All non-whitelisted phones were denied connectivity. Approved and vetted devices—representing leadership or authorized personnel—were permitted to place calls, send text messages, and access the internet normally. This selective enforcement mirrored real correctional operating requirements and validated granular, policy-driven control rather than blanket denial.
Throughout the evaluation, no RF jamming was employed, no payload content was accessed, and no personally identifiable information was collected or stored. All outcomes were achieved using standards-compliant cellular signaling, preserving regulatory compliance and lawful commercial cellular services.
Lessons from the trial reinforce a broader conclusion: as commercial drones increasingly leverage AI, beamforming, and environmental awareness to defeat traditional countermeasures, effective defense depends on selective, network-level control informed by real-time environmental survey rather than indiscriminate RF effects. The combination of Periscope survey and control-plane enforcement provides a scalable framework applicable to both counter-UAS and correctional security missions.