The AI-Driven Surveillance Resilience Platform uses a network of interconnected sensors and edge-computing devices to monitor sensor health and coverage in real-time.

It employs predictive analytics to foresee maintenance needs and potential failures, automatically rerouting resources or activating standby sensors to cover gaps instantaneously.

The system also integrates with existing surveillance arrays to provide a seamless handover and maintains a central dashboard that alerts human operators to necessary interventions only when manual action is needed.

This solution offers a proactive approach to surveillance by using AI to preemptively address potential blind spots, ensuring no downtime.

It reduces the costs associated with redundant infrastructure and manual interventions while greatly enhancing operational security and reliability.

The technology for real-time data processing and AI-driven analytics is mature, yet integration with current surveillance systems and ensuring robust cyber-security are technical challenges.

Initial costs may be high given the need for edge devices and AI training but will become more competitive with scaling.

Rigorous testing for malfunction scenarios and continuous updates will ensure regulatory compliance and system reliability.