E026 is a three-unit course in WGU's Bachelor of Science, Cloud and Network Engineering and its AWS, Azure and Cisco versions. It examines where artificial intelligence and network security meet.
The guidebook says it emphasizes how AI techniques can strengthen security practices, automate tasks, optimize a network and improve threat detection and response. Students use AI tools and also judge their output.
That second skill is the harder one. An AI tool will always produce an answer, and the course asks you to decide whether it is correct, useful and suited to the network in front of you.
Course at a Glance
| Item | Details |
|---|---|
| University | Western Governors University (WGU) |
| Course | E026, AI for IT Automation and Security |
| Competency units | 3 |
| Level | Undergraduate |
| Programs | BS Cloud and Network Engineering and its AWS, Azure and Cisco versions |
| Prerequisites | None stated in the course description |
What E026 Covers
| Competency (WGU wording, summarized) | What it asks of you |
|---|---|
| Apply AI-enhanced security practices for threat prevention, detection and remediation | Use AI in the security cycle of a network |
| Evaluate the output of an AI tool against automation and optimization needs | Judge whether results are accurate and useful |
| Perform network modeling and scalability using AI tools | Use AI to model growth and capacity |
Key Concepts Explained
AI in Detection
Machine learning can learn what normal traffic looks like and flag unusual behavior. The strength is spotting patterns people miss; the risk is false alarms and blind spots.
Judging Tool Output
Two simple measures help. Precision asks how many flagged events were truly threats. Recall asks how many real threats the tool caught.
Illustration: A tool flags 200 events in a week. Review shows 40 were genuine threats, and the team knows of 10 further threats the tool missed. Precision is 40 / 200 = 20%. Recall is 40 / 50 = 80%. The tool catches most threats but buries analysts in noise, so tuning or a ranking step is needed before relying on it.
Modeling and Scalability
AI tools can forecast traffic growth and suggest capacity changes. You still need to check the assumptions, such as the period of data used and whether a coming change could break the pattern.
Typical Assignments and How to Approach Them
| Task element | What it tests | How to approach it |
|---|---|---|
| Applying AI to security | Practical use | Describe the task, the tool and how results feed prevention or response |
| Evaluating output | Critical judgment | Check results against known facts and the stated needs |
| Modeling scalability | Analytical thinking | State inputs, run the model, interpret and note limits |
A Checklist for Evaluating an AI Result
Use the same checklist for every tool output so your evaluation is consistent. Record the checks you made and what you concluded.
| Check | Question to ask |
|---|---|
| Accuracy | Does it match what you know or can verify? |
| Fit | Does it meet the stated automation or optimization need? |
| Data | Was the input representative and recent? |
| Risk | What happens if the tool is wrong, and who reviews it? |
A Study Approach for Each Competency
For AI-enhanced security, pick one realistic use such as anomaly detection on network traffic and write down what the tool is given, what it produces and how a person would act on the result. Doing this once in detail helps you answer most task variations.
For evaluation, build the habit of verifying output against a second source and of noting where the result could be wrong.
For modeling and scalability, keep a short record of the data you supplied, the settings you used and the prediction you received. Then compare the prediction with a simple expectation, such as straight-line growth, to see whether the AI result is reasonable. Always finish by stating what decision the result supports.
Weak and Strong Evaluations
Evaluation writing is where many answers fall short. Compare the two styles.
| Weak | Stronger |
|---|---|
| The AI tool worked well and found threats. | The tool flagged 200 events and 40 were genuine, so most alerts were false positives. It suits first-pass triage if an analyst reviews the output, but not automatic blocking. |
| The model predicts traffic will double. | The model used six months of data that included a one-off event. Excluding it suggests growth of about a third, so capacity should be planned in stages. |
Where Students Get Stuck
- Accepting output at face value. The evaluation competency exists to prevent this.
- Describing the tool, not the need. Tie your judgment to the network's stated needs.
- Skipping limits. Say where a model could mislead.
- No human in the loop. Explain who reviews decisions that matter.
Study Tips for E026
- Keep a log of prompts and results so you can describe what you did and why.
- Learn the basic measures for judging classification results.
- Always compare tool output with at least one independent check.
- Match your headings to the rubric.
How We Help with E026
Send the task, your tool outputs and the rubric. A writer can explain how to judge AI results, prepare a model evaluation, or review your draft. This is study support only: you run your own tools and complete your own work under WGU's academic integrity policy.
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Frequently Asked Questions
The guidebook lists 3.
The BS Cloud and Network Engineering and its AWS, Azure and Cisco versions.
The competencies concern applying AI tools and evaluating their output, not building models from scratch.
Checking whether results are accurate and suited to the network's automation and optimization needs.
Check your task instructions for the tools required.
Yes. We check the checks you made, the reasoning and the match to the rubric, and explain what to improve.