This CTO guide explains why securing AI systems requires a fundamentally new approach as large language models, autonomous AI agents, and Model Context Protocol servers become core infrastructure. Unlike traditional applications, agents reason, retain memory, and take autonomous actions by chaining API calls across internal and third-party systems, dramatically expanding the attack surface. The paper outlines risks such as prompt injection, memory-based data leakage, MCP manipulation, and large-scale API abuse that legacy security tools cannot detect. It argues that APIs form the execution layer and nervous system of agentic AI, making behavioral API visibility, agent attribution, memory governance, and real-time anomaly detection essential. The guide concludes that organizations cannot se

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