How do you monitor access logs to detect security anomalies in cloud-native applications?

Monitoring access logs involves collecting and analyzing data on user or system entry requests to cloud-native applications. In cloud-native environments such as Kubernetes, it is crucial for real-time detection of security anomalies (e.g., unauthorized access or malicious requests). Application scenarios include containerized application protection, compliance auditing, and reducing the risk of data breaches.

Core components include log agents (e.g., Fluent Bit), storage (e.g., Elasticsearch), and intelligent analysis tools. The principle is to use pattern recognition and machine learning to establish access baselines for detecting anomalous behaviors (e.g., SQL injection or unusual IPs). In practical applications, it enhances security visibility, shortens response times, strengthens defense resilience, and impacts the overall resilience and compliance of the cloud-native ecosystem.

Implementation steps: 1. Configure log output (integrate Kubernetes tools like Prometheus). 2. Aggregate data to a central platform (using Elastic Stack). 3. Apply AI analysis to build detection models (e.g., rules or machine learning). 4. Set up real-time alerts. Typical scenarios: Monitoring abnormal traffic peaks or suspicious activities. Business value: Reducing attack losses, ensuring data protection, improving operational efficiency, and meeting regulatory requirements.