Stream A from AIMA framework

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Maturity Level 1

Maturity Level 2

Maturity Level 3

O-EM-A-1

O-EM-A-1: Is there informal or occasional monitoring and detection of events in AI systems?

Manual Detection: Events are identified manually, often after impact is observed.
No Anomaly Detection: No structured methods for identifying drift, outliers, or degradation.
Reactive Approach: Monitoring is not proactive or automated.

Manual Detection: Events are identified manually, often after impact is observed.
No Anomaly Detection: No structured methods for identifying drift, outliers, or degradation.
Reactive Approach: Monitoring is not proactive or automated.

O-EM-A-2

O-EM-A-2: Are event responses informally conducted and sporadically documented?

Basic Monitoring: Latency, availability, and accuracy metrics are tracked.
Initial Anomaly Detection: Basic drift and outlier detection introduced. -Alerting Setup: Manual or threshold-based alerting in place.

Basic Monitoring: Latency, availability, and accuracy metrics are tracked.
Initial Anomaly Detection: Basic drift and outlier detection introduced. -Alerting Setup: Manual or threshold-based alerting in place.

O-EM-A-3

O-EM-A-3: Are events systematically monitored and consistently detected through defined processes?

Real-Time Monitoring: Continuous monitoring with dashboards and alerting tools.
ML-Driven Detection: Advanced analytics and machine learning detect anomalies and drift proactively.
Proactive Alerts: Intelligent alerting reduces false positives and accelerates response.

Real-Time Monitoring: Continuous monitoring with dashboards and alerting tools.
ML-Driven Detection: Advanced analytics and machine learning detect anomalies and drift proactively.
Proactive Alerts: Intelligent alerting reduces false positives and accelerates response.