Multi-Modal Contrastive Learning Bridges Generalization Gap for AI Agents in Cybersecurity — Works on Novel Attack Families

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Research demonstrates that ML cybersecurity models trained on labeled datasets fail to generalize to new attack families — the core problem preventing agent-based security tools from catching novel threats. Multi-modal contrastive learning across network traffic, log data, and threat intelligence representations bridges this gap without requiring labeled examples of new attack patterns. Directly applicable to building agent security tooling that remains effective as attacker techniques evolve rather than degrading on first exposure to new campaigns.