AI is transforming cybersecurity on both offense and defense. Attackers use AI to scale attacks; defenders use AI to detect threats faster. This course covers both sides — the tools, the techniques, and the new risks AI itself introduces.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on cybersecurity + ai you can find — even without producing a single minute of custom video.
This course covers AI as a defensive tool (threat detection, log analysis) and as an attack surface (prompt injection, adversarial attacks). Most courses cover only one side.
Day 2 walks through LLM-powered log analysis with real log samples. You build the query patterns, not just read about them.
AI attacks evolve fast. This course covers the threat landscape as of 2026 with links to the best sources for staying current.
Each day is designed to finish in about an hour of focused reading plus hands-on work. No live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
AI-powered attacks, AI-assisted defense, and the state of the arms race. The threat categories that matter most in 2026.
Using AI for network anomaly detection, SIEM log analysis, and pattern recognition at scale. The LLM log analysis workflow that surface threats faster.
Building prompt-based log analysis workflows. How to write queries that surface IOCs, investigate alerts, and triage incidents using AI.
AI tools for finding vulnerabilities in code and infrastructure. How to use LLMs for security code review. The false positive problem.
The new attack surface AI systems introduce: prompt injection, training data poisoning, model extraction, and adversarial examples. How to defend against them.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
How AI is transforming both attack and defense in cybersecurity — current tools, techniques, and threat categories.
Using AI and LLMs for security log analysis — moving beyond regex rules to intelligent threat pattern recognition.
How prompt injection attacks work, real examples, and the defenses that prevent AI systems from being hijacked.
AI-powered static analysis and code review tools that find security vulnerabilities faster than traditional scanners.
Adversarial examples, model extraction, and data poisoning — the ML-specific attacks your AI systems need to defend against.
How security operations centers are using AI tools to accelerate threat investigation and reduce analyst fatigue.
The best way to go deeper on any topic is to read canonical open-source implementations. These repositories implement the core patterns covered in this course.
Open-source vulnerability management platform. The production tool where AI-discovered vulnerabilities get tracked and managed.
Application monitoring and error tracking. Shows how production systems detect and alert on anomalous behavior.
The OWASP Top 10 — the canonical list of web application security risks that AI code review tools are trained to find.
IBM's Adversarial Robustness Toolbox — the canonical library for testing and defending against adversarial AI attacks.
You work in security and want to understand how to use AI tools to do your job faster and how to defend against AI-powered attacks.
You build AI systems and need to understand the new attack surface you're creating — prompt injection, data poisoning, and model security.
You are responsible for cybersecurity strategy and need to understand both the AI security tools available and the AI risks to manage.
The 2-day in-person Precision AI Academy bootcamp covers AI security, cybersecurity tools, and threat landscape — hands-on with Bo. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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