The solution employs a decentralized AI framework where each defense unit is equipped with a local AI agent that can perform real-time learning based on environmental inputs and adversarial patterns.

These agents leverage distributed ledger technology, specifically a secure blockchain, to register and retrieve verified learning updates that can be trusted even in disconnected environments.

It incorporates federated learning to update AI models without direct data sharing, thus maintaining security.

The system operates within predefined safety protocols critical for mission safety, allowing real-time adaptation through validated micro-updates that aggregate across units upon re-establishing connection with central command when possible.

This solution enables autonomous defense systems to respond in real time to evolving threats without reliance on cumbersome network connectivity.

By ensuring secure and validated updates through blockchain, it provides a robust safeguard against adversarial manipulations during update processes.

The capability to adapt decentralizes threat response, increasing mission safety and reducing latency in action, thus maintaining operational superiority.

The technical readiness leverages existing technologies such as blockchain and federated learning, which have scalability potential.

Cost barriers include integration with existing systems and ensuring compliance with defense-grade security standards.

Although regulatory issues are high, collaboration with defense agencies during development could mitigate this.

Competitors include solutions focusing on edge AI, but none may integrate blockchain for secure updates as proposed here.