Securing Data Endpoints in Google Cloud: A Data-Driven Approach to Anomaly Detection and Threat Mitigation

Using Google Cloud requires a complete defensive plan combining data-based anomaly alerts with early threat response systems to protect confidential information. Modern cloud environments require active security monitoring because their complexity continues to increase while cyber threats become more complex. Traditional security practices fail to handle the fluid characteristics of cloud-based data breaches because organizations must move towards security systems based on data intelligence. The research presents an analysis of advanced anomaly detection methods through statistical analysis, machine learning algorithms, and behavior analytics for observing and responding to abnormal data access patterns and security incidents in Google Cloud infrastructures. Organizations that deploy robust anomaly detection systems create better capabilities for threat detection, along with threat mitigation, and ensure compliance with strict regulatory requirements. Companies require integrated security platforms because cyber dangers combine with financial problems and system breakdowns within a single detection foundation.