Purpose: This document provides a template for creating a Continuous Monitoring Plan for an AI system. A static risk assessment at deployment is insufficient. AI systems exist in dynamic environments where data distributions shift, user behavior changes, and new adversarial techniques emerge. This plan establishes a proactive framework for detecting and responding to these changes, ensuring the system remains secure, fair, and performant over its entire lifecycle.
1. Plan Overview
This section defines the scope and objectives of the monitoring activities. It sets the stage for the detailed technical and procedural components that follow.
- System Identifier: [e.g., “Customer Churn Prediction Model v2.1”]
- System Description: [e.g., “A gradient-boosted tree model deployed as a REST API endpoint to predict the likelihood of a customer churning within the next 30 days.”]
- Deployment Environment: [e.g., “AWS SageMaker, Production VPC”]
- Primary Business Owner: [e.g., “Director of Customer Retention”]
- Primary Technical Owner: [e.g., “Lead ML Engineer, Core Analytics Team”]
- Plan Version & Date: [e.g., “v1.0, 2023-10-26”]
2. Monitoring Domains, Metrics, and Thresholds
Effective monitoring requires tracking specific, measurable indicators across different domains of system health. A threshold breach is an explicit trigger for an alert and subsequent investigation. Vague metrics lead to inaction; concrete thresholds drive process.
| Domain | Metric | Description | Threshold (Trigger) | Frequency |
|---|---|---|---|---|
| Performance | Model Accuracy | Percentage of correct predictions on a live, labeled data stream. | Drops below 88% over a 24-hour rolling window. | Hourly |
| Data Drift | Population Stability Index (PSI) | Measures distribution shift for key input features (e.g., `last_login_days`). | PSI > 0.25 for any key feature. | Daily |
| Security | Adversarial Input Detection Rate | Rate of inputs flagged by a pre-inference defense mechanism (e.g., outlier detector). | Spikes > 5 standard deviations above the 7-day moving average. | Real-time |
| Fairness | Demographic Parity Difference | Difference in positive outcome rates between privileged and unprivileged groups. | Absolute value > 0.10. | Weekly |
| Operational | P95 Inference Latency | 95th percentile of API response time for prediction requests. | Exceeds 300ms. | Every 5 mins |
3. Data Sources and Tooling
This section identifies the specific technologies and data streams used to collect the metrics defined above. The goal is to create a clear map from metric to data source.
- Production Logs: API gateway logs containing request/response payloads, timestamps, and source IPs. [Tool: Splunk, ELK Stack]
- Model Inference Store: A dedicated database (e.g., S3 bucket, DynamoDB) storing model inputs and outputs for delayed analysis and ground-truth labeling.
- Infrastructure Metrics: CPU/GPU utilization, memory usage, network I/O. [Tool: Prometheus, Grafana, AWS CloudWatch]
- AI Monitoring Platform: Specialized service for drift detection, explainability, and performance tracking. [Tool: Fiddler, Arize, Seldon Deploy]
- Security Tooling: Web Application Firewall (WAF) logs and Intrusion Detection System (IDS) alerts. [Tool: AWS WAF, Snort]
4. Alerting and Escalation Procedure
A defined procedure ensures that alerts are handled consistently and efficiently, preventing alert fatigue and ensuring critical issues are addressed by the right people. The process must be clear, from initial automated detection to potential incident response activation.
5. Roles and Responsibilities
Clear ownership is non-negotiable for an effective monitoring program. Every potential alert type should have a designated primary responder.
- MLOps Engineer (On-call): First responder for operational and performance alerts (e.g., latency, error rates). Responsible for initial triage and system health checks.
- Data Scientist / ML Engineer: Responsible for investigating data drift, concept drift, and model performance degradation alerts. Leads the analysis for potential model retraining.
- AI Security Specialist / Red Teamer: Responds to security-specific alerts, such as spikes in adversarial input detection or unusual inference patterns. Initiates deeper investigation or targeted testing.
- Security Operations Center (SOC): Monitors for network-level anomalies and correlates AI system alerts with broader security events.
- Risk & Compliance Officer: Reviews weekly and monthly reports on fairness and bias metrics. Responsible for escalating ethical concerns to governance committees.
6. Reporting and Review Cadence
Monitoring data is only useful if it informs action. A structured reporting and review process ensures that insights are communicated to the relevant stakeholders and that the monitoring plan itself is periodically re-evaluated.
- Daily Automated Dashboard: A real-time dashboard displaying all key metrics. Accessible to all technical stakeholders. [Tool: Grafana/Kibana]
- Weekly Performance & Security Summary: An automated report emailed to the AI development and security teams, highlighting trends, significant events, and any alerts from the past week.
- Monthly Stakeholder Review: A meeting with business owners, technical leads, and risk officers to discuss long-term trends, the business impact of model performance, and any necessary strategic changes (e.g., prioritizing a full model rebuild).
- Quarterly Plan Review: A formal review of this continuous monitoring plan to update metrics, thresholds, and procedures based on new knowledge, system changes, or emerging threats discovered through red teaming.