Catalog
concept#Analytics#AI / ML#Data#Observability

Anomaly Detection

Identifying unusual patterns in data to detect failures, fraud, or security incidents early. Includes statistical techniques, rule-based approaches and machine learning.

Anomaly detection identifies unusual patterns in data to detect failures, fraud, or security incidents early.
Established
High

Classification

  • High
  • Technical
  • Design
  • Intermediate

Technical context

SIEM or security platform (e.g., Splunk)Monitoring and observability stack (e.g., Prometheus)ML platforms / model serving (e.g., SageMaker)

Principles & goals

Define measurable metrics (precision/recall, FPR)Iterative approach: prototype → validate → productionFavor transparency and explainability for alerts
Run
Domain, Team

Use cases & scenarios

Compromises

  • Alert fatigue from too many false alarms
  • Privacy and compliance issues with sensitive data
  • Costs from compute and operational overhead
  • Start with simple rules and metrics
  • Prioritize alerts and include business context
  • Plan drift metrics and automated retraining

I/O & resources

  • Raw data (time series, logs, events)
  • Feature engineering and contextual attributes
  • Annotations or labels for validation
  • Anomaly score per entity
  • Alert messages and prioritizations
  • Reports and dashboards for analysis

Description

Anomaly detection identifies unusual patterns in data to detect failures, fraud, or security incidents early. The concept covers statistical techniques, rule-based systems and machine learning, including operations, evaluation and adaptation to concept drift. Deployment requires data preparation, model validation and continuous monitoring. Trade-offs include sensitivity, false-positive rate and compute costs.

✔

  • Early detection of failures and security incidents
  • Reduction of damage and downtime
  • Supports root-cause analysis and proactive measures

✖

  • Dependence on data quality and sufficient history
  • High false-positive rates without careful tuning
  • Concept drift requires continuous maintenance and adaptation

  • Precision

    Share of correctly detected anomalies among all alerts.

  • Recall

    Share of detected anomalies relative to all actual anomalies.

  • False positive rate

    Share of incorrect alerts relative to all evaluated cases.

Fraud detection in credit card transactions

Combination of statistical rules and ML scoring to detect suspicious transactions with reduced false-positive rate.

Early detection of machine faults in manufacturing

Sensor-based anomaly detection reduces unplanned downtime and enables condition-based maintenance.

Security monitoring of user access

Detecting unusual login patterns and privilege changes to support incident response.

1

Define problem scope and success criteria

2

Create data inventory and implement preprocessing

3

Test baseline methods, evaluate and validate ML models

4

Set up production-ready deployment with observability

5

Establish continuous monitoring and drift management

⚠️ Technical debt & bottlenecks

  • Hardcoded thresholds without documentation
  • Insufficiently versioned feature transformations
  • Missing tests for drift detection and alert scenarios
data-qualitylabel-scarcitymodel-drift
  • Treat every deviation automatically as an error
  • Send unprioritized alerts to all stakeholders
  • Validate models only on historical, non-representative data
  • Ignoring seasonal effects
  • Wrong assumptions about data stationarity
  • Lack of explainability hampers triage
Statistics and data analysisMachine learning engineeringData engineering and streaming pipelines
Latency requirements for alertingData volume and scalabilityExplainability and traceability of decisions
  • • Limited number of labeled anomalies
  • • Privacy requirements and access controls
  • • Compute capacity for real-time analysis