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Automate AI Anomaly Detection & Response

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Automate AI Anomaly Detection & Response

This course is part of AI Security: Security in the Age of Artificial Intelligence Specialization

Instructors: Starweaver

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Apply machine learning techniques to detect anomalies in cybersecurity data such as logs, network traffic, and user behavior.
Automate incident response workflows by integrating AI-driven alerts with security orchestration tools.
Evaluate and fine-tune AI models to reduce false positives and improve real-time threat detection accuracy.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the AI Security: Security in the Age of Artificial Intelligence Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

An outage rarely starts with a red dashboard-it starts as a small anomaly: a spike in latency, a surge in failures, or a subtle change in traffic. The faster you detect and respond, the less damage (and stress) you create. In this course, you’ll build an end-to-end anomaly detection and response loop on Azure. You’ll instrument an app with Application Insights, detect unusual behavior with Azure Monitor smart detection, dynamic thresholds, and KQL time-series functions, and then turn alerts into action using action groups and Logic Apps (with optional Azure Functions for custom remediation). You’ll learn a practical workflow: choose the right signal, set guardrails to reduce noise, enrich alerts with context, and automate a consistent response-notify the right channel, capture evidence, and trigger a safe mitigation step.

This course is designed for IT professionals, including DevOps engineers, SREs, and Azure administrators, who want to learn how to automate anomaly detection and response workflows in Azure environments. Learners should be familiar with basic Azure Portal navigation, and JSON familiarity is helpful, along with basic monitoring concepts. No ML prerequisite. By the end, you’ll have a reusable blueprint (queries, alert rules, and automation) you can adapt to real systems to catch problems earlier and respond reliably.

Module details

This module introduces anomaly detection from the ground up: what an “anomaly” is, which signals to trust, and how Azure Monitor helps you detect unusual behavior without building a custom ML model. You’ll instrument a workload with Application Insights, explore built-in smart detection, and create your first alert rule using dynamic thresholds and action groups so the right people (or workflows) get notified fast.

What's included

4 videos2 readings1 peer review

4 videosTotal 26 minutes

Welcome to Automate AI Anomaly Detection & Response3 minutes
Signals 101: Metrics, Logs, Traces, and Baselines6 minutes
Built-in Anomaly Detection through Smart Detection and Dynamic Thresholds6 minutes
Instrument an App and Create Your First Alert11 minutes

2 readingsTotal 10 minutes

Welcome to the Course: Course Overview5 minutes
Quick Reference: Azure Monitor Alerts & Smart Detection5 minutes

1 peer reviewTotal 25 minutes

Hands-On-Learning: Creating an Anomaly Alert with an Action Group25 minutes

This module moves from “something is weird” to “what exactly changed and why.” You’ll learn KQL basics for beginners, then use time-series functions such as make-series and series_decompose_anomalies to detect spikes, dips, and seasonality-aware anomalies in logs. You’ll turn the query into a log alert rule and practice enriching alerts with anomaly scores, dimensions (region/role), and clear troubleshooting steps.

What's included

4 videos1 reading1 peer review

4 videosTotal 29 minutes

Turning Logs into a Time Series with KQL7 minutes
Anomaly Detection with KQL Through Series Decomposition6 minutes
Building a KQL Anomaly Query and Creating a Log Alert Rule11 minutes
Suppression, Dimensions, and What to Include6 minutes

1 readingTotal 5 minutes

Reference: KQL Anomaly Detection & Forecasting5 minutes

1 peer reviewTotal 20 minutes

Hands-On-Learning: Create a KQL-Based Anomaly Alert with Enriched Context20 minutes

This module turns detection into action. You’ll learn response patterns that are safe and repeatable, then wire Azure Monitor action groups to Logic Apps (and optionally Azure Functions) to notify, create tickets, capture evidence, and trigger a reversible mitigation. You’ll practice parsing the common alert schema so one automation can handle metric, log, and smart detection alerts.