Collective intelligence and swarm AI have shaped recent explorations of distributed cybersecurity strategies. Many traditional approaches rely on centralized analytics or static signatures, and they sometimes fall short in dynamic and large-scale networks. This chapter provides a comprehensive examination of autonomous defense strategies that embed intelligence in distributed agents, use local detection mechanisms, and encourage emergent coordination through swarm principles. The text explains how these concepts, combined with reinforcement learning, can enable proactive threat detection and containment without complete reliance on centralized oversight. It reviews existing work on multi-agent systems, swarm optimization, and distributed anomaly detection, and it introduces detailed theoretical foundations for practical implementation. Empirical findings from simulations and operational settings illustrate how emergent coordination shortens detection time and enhances resilience.