This monitoring strategy is insufficient due to its lack of timeliness. An adversarial prompt causing a recursive loop can lead to a resource exhaustion or Model Denial of Service attack, incurring massive costs in minutes or hours. Relying on a monthly billing report introduces a detection latency of up to 30 days, by which time catastrophic financial damage would have already occurred. Effective mitigation requires real-time or near-real-time monitoring of resource consumption metrics (e.g., CPU, memory, API call frequency) with automated alerting and circuit-breaker mechanisms to terminate runaway processes immediately.

This is a Yes/No question; therefore, there are no other incorrect options to evaluate.

1. OWASP Foundation. (2023). OWASP Top 10 for Large Language Model Applications. LLM04: Model Denial of Service. This document explicitly states that attackers can cause resource-heavy operations, leading to high costs, and recommends implementing strict resource usage controls and monitoring.

2. Palanisamy, V., & Liu, L. (2017). Effective cloud resource provisioning for distributed data stream processing. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), 101-110. While focused on data streams, the paper underscores the necessity of real-time resource monitoring and auto-scaling in cloud environments to manage costs and performance, a principle that applies directly to AI workload security. https://doi.org/10.1109/BigData.2017.8257916

3. MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). (2023). Security of advanced AI. Course materials often emphasize that security monitoring for AI systems must operate at machine speed, as attacks can be automated and execute far faster than human review cycles like monthly reports.