AI for Security Engineering

A course for security engineers on AI in defensive work: log analysis, detection, incident response, and secure code review - plus the AI-specific threats they now defend against.

What this course covers

The course runs across 12 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Introduction to AI in Defensive Security — Understand the role of machine learning and large language models in modern defensive security engineering, including integration patterns and limitations.
AI-Powered Log Parsing and Structured Data Extraction — Use AI utility tools to ingest, normalize, and parse highly unstructured heterogeneous system logs into schema-compliant JSON.
Threat Detection: Identifying Anomalous Behavior with ML — Configure and tune machine learning models within a SIEM to detect baseline deviations and advanced persistent threat indicators.
Automated Detection Engineering and Rule Generation — Leverage LLMs to write, test, and refine Sigma and Yara detection rules based on newly published threat intelligence reports.
Incident Response Triage with LLM Assistants — Navigate a high-pressure alert storm using an AI assistant to summarize alerts, correlate entities, and determine escalation path.
Automated Incident Containment and Playbook Orchestration — Deploy AI-driven SOAR playbooks to dynamically isolate compromised hosts and revoke active sessions based on threat severity.
AI-Assisted Secure Code Review: Spotting OWASP Top 10 — Run AI-augmented static analysis tools to identify deep logical vulnerabilities, injection flaws, and hardcoded secrets in source code.
Generating and Verifying Secure Code Patches with AI — Prompt an AI assistant to generate secure remediation code patches, then compile and test them in a sandbox environment to ensure safety.
AI-Specific Threats: Prompt Injection Tactics and Defenses — Explore direct and indirect prompt injection attacks against LLM-integrated apps, and implement robust defensive sanitization layers.
Model Evasion and Data Poisoning Mitigation — Analyze techniques used to bypass ML classifiers (evasion) or corrupt training pipelines (poisoning), and implement validation defenses.
Securing LLM APIs and Orchestration Frameworks — Configure API gateways, rate limiters, and content filtering tools to secure LLM pipelines using LangChain or Semantic Kernel.
Designing Resilient AI Security Architectures — Evaluate architectural diagrams for AI-enabled applications to identify trust boundaries, data leakage risks, and apply fallback defenses.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick.

Who it is for

Security engineers, SecOps. The material is pitched at advanced level, and takes roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Function: Engineering track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, code lab, branching scenario exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

See how this training works for the whole function on AI training for Engineering.

Also in this track

Advanced AI-Assisted & Agentic Development — An advanced course on using agentic coding tools and AI across the whole SDLC at scale. Learners apply agentic coding for multi-file changes, integrate with CI/CD, review AI-generated code, and follow secure and reliable practices. The outcome is dependable AI-assisted engineering on real systems.
AI for Engineering & Software Development — This course helps software engineers accelerate development with AI coding assistants while keeping code secure and reviewed. Learners use AI for coding, review and debugging, tests and documentation, and apply secure-use practices. They leave shipping higher-quality code faster.
AI Across the Software Lifecycle Pathway — Apply AI to testing, documentation, operations, and security.

Frequently asked questions

How long does this course take?

It runs roughly 150 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code FUN-SECE.