# Chapter 90 — Secure AI Platform Security Analytics & Threat Detection

Chapter 90 delves into the development of a Secure AI Platform Security Analytics & Threat Detection architecture. The chapter outlines the importance of runtime observability in generating security signals from a modern AI platform, which can consist of millions of events from various sources such as users, sessions, APIs, authentication, authorization systems, databases, object storage, AI models, RAG pipelines, agents, tools, containers, Kubernetes, networks, and cloud infrastructure.

The chapter emphasizes that simply collecting these events is insufficient; the security system must discern normal from suspicious events, correlate related events, identify potential attacks, determine affected assets, assess the severity of activity, and define appropriate responses. This chapter provides a comprehensive overview of a defensive security analytics architecture that encompasses detection engineering, behavioral analytics, threat intelligence, event correlation, risk scoring, automated detection, SOC integration, and continuous improvement.

Brief written by urgent.news from Dev.to's own syndicated text. Machine-written — may contain errors; check the original before relying on it.