Free education for offensive and defensive AI security professionals and enthusiasts
Comprehensive courses covering offensive and defensive AI security
Build a solid foundation in AI security concepts, vulnerabilities, and best practices.
Learn to identify, analyze, and prioritize threats in AI systems.
Master offensive techniques to test and break AI systems ethically.
Evaluate and audit AI systems for security, compliance, and risk.
Secure autonomous AI agents and multi-agent systems.
Apply machine learning and AI techniques to solve real-world cybersecurity challenges.
Learn to secure AI/ML systems against adversarial attacks, prompt injection, and model abuse.
A comprehensive 12-week course applying machine learning and AI techniques to solve real-world cybersecurity challenges
This hands-on course teaches you how to leverage machine learning and AI to detect threats, classify malware, automate security operations, and defend AI systems themselves. Through weekly labs and a capstone project, you'll build production-ready security solutions using Python, scikit-learn, TensorFlow, and real security datasets.
Lab/Assignment: Setup environment (Python, Jupyter, security datasets); exploratory data analysis on a security dataset.
Lab/Assignment: Train baseline classifiers (logistic regression, random forest) on phishing or spam email datasets; evaluate precision/recall.
Lab/Assignment: Build anomaly/classification models on network flow / IDS datasets (e.g., port scans, brute force) and compare supervised vs. unsupervised approaches.
Lab/Assignment: Use static features (n-grams, metadata) for malware or malicious URL detection and discuss evasion challenges.
Lab/Assignment: Implement clustering and autoencoder-based anomaly detection for insider threat or unusual login behavior.
Lab/Assignment: Apply text embeddings and simple transformers to classify phishing messages or triage alerts.
Lab/Assignment: Build a prototype to prioritize alerts or generate incident summaries from logs, integrating with a basic playbook.
Lab/Assignment: Implement simple gradient-based evasion against a classifier and simulate data poisoning on a security dataset.
Lab/Assignment: Explore adversarial training, input validation, out-of-distribution detection, and monitoring for model abuse.
Lab/Assignment: Use or critique LLM-based assistants for threat hunting, detection engineering, or malware analysis, including prompt-injection and data-leakage risks.
Lab/Assignment: Discuss regulation, AI use policies in cyber operations, model accountability, and auditability.
Lab/Assignment: Students present an end-to-end AI-for-cyber project using real or realistic security data.
Hands-on implementation of ML models for various security use cases
Short, concept-focused assessments to reinforce key learning objectives
Theory + mini lab covering weeks 1-6
Report + code + presentation of end-to-end security AI solution
Build and evaluate an ML-based IDS for a specific network segment, comparing multiple algorithms and addressing false positive reduction.
Design a comprehensive phishing detection system using NLP and email metadata, with real-time classification capabilities.
Develop and harden a malware classifier against simple adversarial attacks, implementing defensive techniques learned in class.
Prototype an AI assistant to help SOC analysts triage SIEM alerts safely, with built-in safeguards against prompt injection and data leakage.
Supplement your learning with these industry-recognized courses and lab modules
Cohort-based course focused specifically on applying ML to security use cases, with six guided labs where you train intrusion detection models, classify malware, detect anomalies, and explore adversarial attacks using Python and scikit-learn.
Visit Course →Advanced, lab-heavy class (30+ hands-on labs) where you build neural networks for threat classification, autoencoders for outlier detection, CNNs for malware, and deep-learning pipelines on security data.
Visit Course →Focuses on both offensive and defensive use of AI in security; includes labs that walk through applying ML and AI tooling across threat detection and response scenarios.
Visit Course →Online training aligned to an "AI for cybersecurity" text, with built-in virtual labs targeting threat detection and prevention using AI techniques.
Visit Course →A set of open, real-world lab modules designed to be run directly in Google Colab; each topic has pre-lab, hands-on lab, and post-lab "add-on" work using open OWASP datasets for anomaly detection and other ML-for-security tasks.
View on GitHub →University course with homework and a final project where you implement k-means, regression, ensembles, TensorFlow models, and apply them to malware, spam, and anomaly detection in Python; the syllabus can guide you to build your own lab pipeline if you do not enroll.
Visit Course →Self-paced course focused on attacks against ML systems (poisoning, Trojans, backdoors, evasion, inference) and defenses such as adversarial training, outlier detection, and differential privacy, oriented toward DoD-style use cases.
Visit Course →Short online course (about 1.5 hours) with guided exercises using FGSM on Keras models and black-box attacks against a hosted ML-as-a-service image classifier.
Visit Course →Practical Colab-based labs on fine-tuning open-source LLMs with security datasets, implementing secure GenAI-powered apps, and using AI tools to enhance application security.
Visit Course →A comprehensive course on securing AI/ML systems and defending against adversarial attacks, prompt injection, and model abuse
This advanced course covers how AI and machine learning are applied to cybersecurity (detection, response, automation) and how to secure and defend AI/LLM systems themselves against adversarial attacks, prompt injection, and model abuse. Students will gain hands-on experience with real-world attack and defense scenarios using industry frameworks like OWASP LLM Top 10 and MITRE ATLAS.
By the end of the course, students will be able to:
Lab/Assignment: Environment setup (Python, Jupyter, security datasets); exploratory data analysis on a simple security dataset.
Lab/Assignment: Train baseline classifiers on phishing or spam email datasets; evaluate precision/recall and discuss false positives.
Lab/Assignment: Build supervised and unsupervised models for detecting port scans, brute force, or other intrusion events.
Lab/Assignment: Implement malware or malicious-URL classifiers using static features; discuss evasion strategies and limits of static ML.
Lab/Assignment: Use clustering or autoencoders for detecting outliers in authentication or endpoint data.
Lab/Assignment: Apply embeddings and simple transformer models to classify phishing messages or prioritize alerts.
Lab/Assignment: Prototype an AI-assisted pipeline for alert triage or incident summarization; connect concepts to SOC workflows.
Lab/Assignment: Implement basic evasion and poisoning attacks against a security classifier; analyze impact on detection performance.
Lab/Assignment: Explore adversarial training, input validation, and monitoring for model misuse; evaluate trade-offs between robustness and performance.
Lab/Assignment: Use an LLM lab (e.g., AI Security Training Lab) to perform prompt injection and jailbreaks; implement simple guardrails and filters.
Lab/Assignment: Discuss AI risk management, security standards (OWASP LLM Top 10, MITRE ATLAS) and organizational controls.
Lab/Assignment: Students present end-to-end AI-for-cyber or AI-security projects, including threat models, experiments, and lessons learned.
Hands-on implementation of security ML models and adversarial techniques
Short conceptual checks on AI security principles and techniques
Concepts + mini lab covering weeks 1-6
Report, code, and presentation of comprehensive AI security solution
Build and evaluate an ML-based intrusion detector with evaluation under realistic noise and data imbalance scenarios.
Create a phishing detection system using NLP and metadata, with model monitoring and drift detection over time.
Develop a malware classifier hardened against simple adversarial attacks including evasion and obfuscation techniques.
Build a secure LLM assistant for SOC analysts, including prompt-injection threat modeling and basic guardrails.
Comparative analysis of different defenses (adversarial training vs. anomaly detection) for a chosen security task.
Industry-recognized courses focused on securing and defending AI systems
Hands-on class covering GenAI/LLM threat modeling, OWASP LLM Top 10, MITRE ATLAS, prompt injection, RAG/agent abuse, and defensive patterns for production AI apps.
Learn More →Cohort-based course on applying machine learning to security use cases, with labs for intrusion detection, malware classification, anomaly detection, and adversarial behavior.
Visit Course →Lab-heavy course where students build models for threat classification, anomaly detection, and malware analysis using classical ML and deep learning.
Visit Course →Focuses on offensive and defensive applications of AI in cybersecurity, including practical labs on threat detection and automated response.
Visit Course →Focused on using and securing generative AI in cyber operations, including labs on building GenAI-powered tools and mitigating their risks.
Visit Course →Self-paced course on adversarial attacks (evasion, poisoning, Trojans) and corresponding defensive techniques like adversarial training and outlier detection.
Visit Course →Graduate-level course that integrates AI techniques into cybersecurity practice; includes applied projects and comprehensive learning outcomes.
View Course →Free, hands-on GitHub repositories for practicing AI security techniques
Open-source lab with OWASP-LLM-aligned exercises; each lesson includes attack and mitigate scripts that demonstrate prompt injection, output manipulation, and other LLM vulnerabilities using both hosted and local models.
Research-oriented framework for AI safety attacks, defenses, and evaluation on multiple datasets, suitable for running custom adversarial experiments and benchmarking robustness.
Curated list of AI security resources, including courses, standards (OWASP, NCSC), tools, and additional labs, useful as a broader reading and tooling hub.
Professional certifications to validate your AI security expertise
The GIAC AI Security Professional certification validates practitioners' knowledge of securing AI/ML systems, covering threat modeling, adversarial attacks, and defensive strategies.
Format: 75-115 questions, 3 hours
Prerequisites: Experience in security and AI/ML
Comprehensive certification covering AI security principles, LLM security, prompt injection defenses, and responsible AI implementation.
Format: Online exam, self-paced learning
Prerequisites: Basic security knowledge
While focused on Azure AI services, this certification includes significant coverage of AI security, responsible AI, and secure AI deployment practices.
Format: Exam AI-102
Prerequisites: Azure fundamentals recommended
While not AI-specific, OSCP is highly valuable for AI red teamers, teaching penetration testing methodologies applicable to AI system security testing.
Format: 24-hour hands-on exam
Prerequisites: Strong Linux and networking knowledge
Comprehensive program covering AI/ML engineering with modules on security, scalability, and production deployment of AI systems.
Format: 6 courses, self-paced online
Prerequisites: Basic programming knowledge
Gold-standard security certification with coverage of emerging technologies including AI security, risk management, and security architecture.
Format: 125-175 questions, 4 hours
Prerequisites: 5 years security work experience
Demonstrates proficiency in building ML models with TensorFlow, including security considerations for model development and deployment.
Format: 5-hour coding exam
Prerequisites: Python and ML basics
Validates expertise in building, training, and deploying ML models on AWS with emphasis on security, monitoring, and operational excellence.
Format: 65 questions, 180 minutes
Prerequisites: AWS and ML experience recommended
Select certifications that align with your desired role: offensive (red team), defensive (blue team), or AI engineering with security focus.
Pair AI-specific certifications with general security certs (CISSP, OSCP) for comprehensive expertise.
Complement certifications with practical experience through our labs, CTF challenges, and real-world projects.
AI security evolves rapidly. Maintain certifications through continuing education and stay updated with latest threats.
Stay updated with the latest research, tools, events, and news
Find your next career opportunity in AI security
The AI security field is rapidly growing with high demand for professionals skilled in securing AI systems, red teaming LLMs, and ensuring responsible AI deployment. Browse current opportunities across major job boards:
Professional network with extensive AI security listings
Largest job board with comprehensive AI security positions
Startup jobs in AI security and ML safety
Jobs with company reviews and salary insights
Direct applications to leading AI security teams
Academic and research positions in AI security
Specialized cybersecurity job boards
Complete our courses in AI Red Teaming, Threat Modeling, and Risk Assessment to gain practical experience.
Follow AI security research, attend conferences, and participate in CTF challenges to demonstrate expertise.
Join AI security communities, contribute to open-source projects, and engage with professionals on LinkedIn.
Consider relevant certifications like OSCP, CEH, or specialized AI/ML security training programs.
Learn from real-world AI security incidents and failures
The AI Incident Database catalogs real-world harms and near-misses caused by AI systems. Study these incidents to understand risks and improve AI security practices.
Visit AI Incident DatabaseData leaks, unauthorized access, model theft
Successful jailbreaks and prompt attacks
Malicious use of AI systems
Unexpected behaviors and vulnerabilities
AI Security Academy is a free, open-source educational platform dedicated to advancing the knowledge and skills of AI security professionals and enthusiasts worldwide.
We believe that AI security education should be accessible to everyone. As AI systems become increasingly integrated into critical infrastructure and everyday applications, the need for skilled security professionals who understand both offensive and defensive AI security has never been greater.
This is a community project. We welcome contributions from AI security researchers, practitioners, and enthusiasts. Visit our GitHub repository to contribute content, report issues, or suggest improvements.