Top Alerts in Enterprise RAG Agents and RAG Applications

Enterprise RAG agents require both real-time detection and comprehensive monitoring for security, privacy, cost, and performance.

Here are Top 50 Alerts in Enterprise RAG Agents and RAG applications.

Security & Privacy

These alerts help identify potential threats, data leakage, and access control issues.

Real-time Alerts:

PII/PHI Disclosure: Alert when the LLM response or intermediate data flow contains sensitive information that should have been masked or blocked.
Prompt Injection Attempts: Flag and alert on user input that attempts to bypass system prompts or security guardrails.
Unauthorized Data Access: Alert when an application component attempts to access data in Azure Blob Storage, S3, or the search index using an unauthorized IAM role or identity.
Content Safety Violations: Trigger alerts from Azure AI Content Safety or Amazon Bedrock Guardrails when generated content is flagged as harmful, hate speech, or violent.
Suspicious Usage Patterns: Flag unusually high request rates from a single user or IP address, potentially indicating a data exfiltration attempt or attack.
Broken Access Control: Alert if the system returns documents to a user that their security role should not have access to (requires metadata filtering/ACL enforcement).
Jailbreaking/Adversarial Input: Alert on inputs specifically designed to make the model behave contrary to its safety guidelines.

Offline Content Analysis & Reporting:
8. Data Poisoning Detection: Regularly scan ingested documents for anomalies that might indicate a data poisoning attack aimed at skewing model behavior.
9. Compliance Reporting (GDPR, HIPAA): Generate scheduled reports on data access logs and PII handling procedures to ensure regulatory compliance.
10. Vulnerability Assessments: Use tools like Amazon Inspector to conduct scheduled vulnerability scans of container images and Lambda functions used in the pipeline.
11. Access Control Audits: Periodically audit IAM roles and permissions to ensure the principle of least privilege is maintained across all RAG components.
12. Data Encryption Status: Offline checks to confirm data at rest in vector stores (Azure AI Search indexes, S3 buckets) is encrypted with customer-managed keys.

Cost Management

These items focus on tracking resource consumption to prevent unexpected cost spikes.

Real-time Alerts:
13. Token Usage Spike: Alert immediately if the input or output token count per query or per hour exceeds a predefined threshold.
14. High Compute Utilization: Trigger alerts if the underlying compute resources (e.g., Azure App Service, AWS Lambda, SageMaker endpoint) are maxed out, which can lead to auto-scaling events and increased costs.
15. Egress Data Transfer Warning: Alert on unusual data egress volumes, as inter-service or cross-cloud data transfer can be expensive.
16. API Call Rate Limit Approaching: Warn when the number of calls to the LLM or Azure AI Search API is approaching rate limits, indicating a potential need to scale up to a more expensive tier.

Offline Content Analysis & Reporting:
17. Cost Anomaly Detection: Utilize services like AWS Cost Explorer or Azure Cost Management to detect and report on significant, unexpected spending increases.
18. Token Cost Reduction Analysis: Scheduled analysis to evaluate the effectiveness of prompt engineering or chunking strategies in reducing overall token usage.
19. Resource Right-Sizing Recommendations: Periodic reports identifying opportunities to downgrade compute or storage tiers based on actual usage patterns (e.g., during off-peak hours).

Performance & Quality

These points help ensure the RAG pipeline is efficient, accurate, and provides a good user experience.

Real-time Alerts:
20. High Latency (P95+): Alert if the end-to-end response time for user queries exceeds acceptable latency thresholds (e.g., 2 seconds).
21. Retrieval Miss Ratio: Alert when the search component consistently fails to return relevant documents for a given query (low hit/miss ratio).
22. Low Groundedness Score: Use LLM-as-a-judge evaluators to real-time score responses and alert if the ‘groundedness’ (factual alignment with retrieved sources) drops below a set threshold.
23. Fallback Rate Threshold: Alert if the system frequently falls back to default responses or generic answers because it could not use the RAG system effectively.

Offline Content Analysis & Reporting:
24. Hallucination Rate Analysis: Scheduled evaluation using a test dataset to measure the model’s hallucination rate and track quality drift over time.
25. Evaluation Drift/Decay: Offline analysis comparing current performance metrics (relevance, accuracy) against a baseline dataset to detect if the system’s effectiveness is degrading in production.