A company is investigating an increase in its AWS monthly bill.

For the earliest detection of abnormal spend, the most direct and purpose-built capability isAWS Cost Anomaly Detection. It continuously evaluates spend patterns and uses machine learning to identify unexpected cost increases soon after they begin, then sends alerts based on the configured thresholds. By creating acost monitor(for example, for linked accounts, services, or cost categories) and configuring an alert to publish toSNS, the security team can receive near-real-time notifications when costs deviate beyond an expected baseline. This is well suited for detecting compromise-driven spend (such as abused EC2 instances serving phishing pages, crypto-mining, or data exfiltration patterns that raise transfer costs).

Option A introduces significant operational overhead and delays because billing/usage exports are not guaranteed to be updated on an hourly cadence in a way that consistently outperforms the native anomaly detector, and the company would be maintaining custom analytics logic. Option C is explicitly slower (daily manual review). Option D may detect suspicious traffic but is indirect for “earliest cost increase” detection and depends on building/operating a separate analytics pipeline; also, not all cost anomalies are visible from flow logs. Therefore, Cost Anomaly Detection provides the fastest and lowest-overhead alerting for unexpected spend.