AI for Cybersecurity

Anomaly detection, phishing, prompt injection and model abuse, for defenders.

recorded hours: 8.5
modules, in order: 5
deployed projects: 3
graded checkpoints: 4

Who it is for, and what you receive.

Security people or Python developers who want AI tools they can defend a network with.

Before you start

Some security or Python background.

Skills

anomaly detection
classification
LLM security
SOC

1
The recorded sessions
Taught by an instructor who does the work, taken in order at your own pace, yours for twelve months.
2
Graded work after every module
Exercises and projects marked by Nova against a rubric you can read, with what you did well and what to fix.
3
The projects
Deployed and graded, each one on your record with its repository.
4
Your hiring plan
The same six-part plan the bootcamps use, run with the career agent.
5
The record and the certificate
Every grade at square1ai.com/u/{handle}; a credential ID that resolves at square1ai.com/verify.
6
Nova as your tutor
Help that is about your actual work, because it has read all of it.

The content plan, module by module.

8.5 recorded hours from an instructor, taken in order at your own pace. Nova grades the work at the end of each module.

1
AI in the security stack
- Where AI helps
- Where it does not
- Vendor claims
Graded
Nothing to submit; watch and take notes
60 min
2
Anomaly detection on logs
- Log features
- Detectors
- False positives
Graded
A detector on real logs
120 min
3
Phishing and fraud detection
- Classifiers
- Evals
- Drift
Graded
A phishing classifier with an eval
90 min
4
Attacking and defending LLM apps
- Prompt injection
- Guardrails
- Reporting
Graded
A red-team report on a lab app
120 min
5
Project: an AI-assisted SOC workflow
- Triage
- Enrichment
- Deploy
Graded
The workflow, deployed
120 min

The projects.

Each is deployed and graded by Nova against a rubric you can read before you start.

1
A log anomaly detector
Find the anomalies in real logs.
You hand in
- Detector
- False-positive analysis
The rubric requires
Seeded anomalies found at a stated false-positive rate.
2
A phishing classifier
Classify phishing with an eval.
You hand in
- Classifier
- Eval
The rubric requires
Precision and recall reported.
3
An AI-assisted SOC workflow
Triage and enrich alerts with AI, deployed.
You hand in
- Workflow
- Deployment
The rubric requires
Every AI suggestion carries its evidence.

What you can do at the end.

AI tools you can defend a network with, and three graded projects on your record.

1
Place AI in the security stack honestly.
2
Build anomaly detection on logs.
3
Build phishing and fraud classifiers with evals.
4
Attack and defend an LLM application.
5
Build an AI-assisted SOC workflow.

Roles this prepares you for

SOC analyst
Security engineer (AI)

No placement rate is shown, because there are no graduates to count yet. The roles above are what the projects are built for.

Your record at the end.

Every exercise and project is graded by Nova against a rubric you can read, and every grade is kept on one page an employer can open and run. This is what the programme writes to it.

Graded, line by line: Nova reads every submission against the brief and the rubric and returns a score, what you did well and what to fix.
Module by module: Each module ends in graded work; the next opens when you are ready, on your own schedule.
Nova remembers: Help is about your actual work, because the tutor has every submission and every failed exercise of yours.
One page an employer can run: Every grade and project at /verify. An employer opens it and runs the code.

Record, AI for Cybersecurity

Example

Project
A log anomaly detector
Seeded anomalies found at a stated false-positive rate.
Graded
Project
A phishing classifier
Precision and recall reported.
Graded
Project
An AI-assisted SOC workflow
Every AI suggestion carries its evidence.
Graded
Every module
4 graded checkpoints
Each module ends in work Nova grades line by line against a rubric you can read.
Graded
After
Your hiring plan
Target roles, the gap map, the proof to send, weekly actions and an interview log.
Kept

The entries, not the grades: those are yours to earn. The page lives at /verify and an employer needs no account to open it.

5 entries, at your own pace. One email when this course opens.

How we help you find a job.

Proof, not a certificate: the projects you deployed are the thing you show, and the tools below are yours to use.

1
Your hiring plan
The same six-part plan the bootcamps use: target roles, the gap map, the proof to send, weekly actions, an interview log, the outcome. You run it with the career agent.
2
A record an employer can run
Your graded projects on /verify. An employer opens it and runs the code.
3
The career agent
Paste a real job posting at /career and it maps the role to your graded work and what to do next.
4
The roles directory
Every role we prepare people for, what it pays and what it asks, at /roles.
5
A path to the live cohort
If you want the instructor, the gates and the hiring sprint, the bootcamp on the same subject is one waitlist away.

One email when this course opens.

Recorded by an instructor who does this work, graded by Nova. The waitlist hears the date and the price first, and nothing is charged before it exists.