# observability
標記為「observability」的 23 篇文章
AI System Log Analysis
AI system logging architecture for forensic investigation: inference logs, prompt and completion logs, tool call traces, embedding query logs, and logging infrastructure requirements.
Logging and Monitoring for Cloud AI Services
Implementing comprehensive logging and monitoring for cloud AI services including prompt/response capture, anomaly detection, and security-focused observability across AWS, Azure, and GCP.
AI Monitoring and Observability
What to monitor in AI systems, key metrics for detecting abuse and drift, alerting strategies, and observability architecture for LLM applications.
Runtime Monitoring & Anomaly Detection
Monitoring LLM applications in production for token usage anomalies, output pattern detection, behavioral drift, and using tools like Langfuse, Helicone, and custom logging.
Observability for AI Infrastructure
Building observability into AI infrastructure for security monitoring and incident detection.
Exfiltrating Data Through AI Telemetry and Logging
Using AI system telemetry, logging pipelines, and observability infrastructure as covert channels for data exfiltration
AI Observability for Security
Using observability platforms to detect security anomalies in AI system behavior.
Model Telemetry Poisoning
Manipulating model telemetry and observability data to hide attacks, create false positives, or undermine monitoring effectiveness.
Production Monitoring for LLM Security Events
Walkthrough for building production monitoring systems that detect LLM security events in real time, covering log collection, anomaly detection, alert configuration, dashboard design, and incident correlation.
AI Monitoring Setup
Step-by-step walkthrough for implementing AI system monitoring: inference logging, behavioral anomaly detection, alert configuration, dashboard creation, and integration with existing SIEM platforms.
Langfuse Observability Walkthrough
Complete walkthrough for using Langfuse to monitor AI applications for security anomalies: setting up tracing, building security dashboards, detecting prompt injection patterns, and creating automated alerts.
AI 系統記錄分析
為鑑識調查之 AI 系統記錄架構：推論記錄、提示與 completion 記錄、工具呼叫軌跡、embedding 查詢記錄，與記錄基礎設施要求。
章節評量：監控
15 題校準評量，測試你對 AI 系統監控與可觀測性的理解——異常偵測、行為基準與安全事件關聯。
Logging and Monitoring for Cloud AI Services
Implementing comprehensive logging and monitoring for cloud AI services including prompt/response capture, anomaly detection, and security-focused observability across AWS, Azure, and GCP.
AI 監控與可觀測性
於 AI 系統監控什麼、為偵測濫用與漂移之關鍵指標、警報策略，與 LLM 應用之可觀測性架構。
執行時監控與異常偵測
於生產中監控 LLM 應用之 token 使用異常、輸出模式偵測、行為漂移，並使用如 Langfuse、Helicone 與自訂記錄之工具。
Observability for AI Infrastructure
Building observability into AI infrastructure for security monitoring and incident detection.
Exfiltrating Data Through AI Telemetry and Logging
Using AI system telemetry, logging pipelines, and observability infrastructure as covert channels for data exfiltration
AI Observability for 安全
Using observability platforms to detect security anomalies in AI system behavior.
模型 Telemetry 投毒
Manipulating model telemetry and observability data to hide attacks, create false positives, or undermine monitoring effectiveness.
Production Monitoring for LLM 安全 Events
導覽 for building production monitoring systems that detect LLM security events in real time, covering log collection, anomaly detection, alert configuration, dashboard design, and incident correlation.
AI Monitoring Setup
Step-by-step walkthrough for implementing AI system monitoring: inference logging, behavioral anomaly detection, alert configuration, dashboard creation, and integration with existing SIEM platforms.
Langfuse Observability 導覽
Complete walkthrough for using Langfuse to monitor AI applications for security anomalies: setting up tracing, building security dashboards, detecting prompt injection patterns, and creating automated alerts.