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Jobs and careers
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:
Popular AI Security Roles:
LinkedIn Jobs
Professional network with extensive AI security listings
Indeed
Largest job board with comprehensive AI security positions
Wellfound (AngelList)
Startup jobs in AI security and ML safety
Glassdoor
Jobs with company reviews and salary insights
Company Career Pages
Direct applications to leading AI security teams
Research & Academia
Academic and research positions in AI security
Security-Focused Boards
Specialized cybersecurity job boards
Tips for AI Security Job Seekers:
Build Your Skills
Complete our courses in AI Red Teaming, Threat Modeling, and Risk Assessment to gain practical experience.
Stay Updated
Follow AI security research, attend conferences, and participate in CTF challenges to demonstrate expertise.
Network
Join AI security communities, contribute to open-source projects, and engage with professionals on LinkedIn.
Certifications
Consider relevant certifications like OSCP, CEH, or specialized AI/ML security training programs.
AI incident databases
AI Incident Database
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 DatabaseNotable Incident Categories:
Security Breaches
Data leaks, unauthorized access, model theft
Prompt Injection
Successful jailbreaks and prompt attacks
Misuse & Abuse
Malicious use of AI systems
Model Failures
Unexpected behaviors and vulnerabilities
Earlier course outlines
Two 12-week outlines published before the book-based tracks. They remain available as study plans and link to external courses from other providers.
AI for Security: 12-week outline
Course Overview
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.
Weekly Curriculum
Introduction to AI and Cybersecurity
Lab/Assignment: Setup environment (Python, Jupyter, security datasets); exploratory data analysis on a security dataset.
Supervised Learning for Spam/Phishing
Lab/Assignment: Train baseline classifiers (logistic regression, random forest) on phishing or spam email datasets; evaluate precision/recall.
Network Intrusion Detection with ML
Lab/Assignment: Build anomaly/classification models on network flow / IDS datasets (e.g., port scans, brute force) and compare supervised vs. unsupervised approaches.
Malware and URL Classification
Lab/Assignment: Use static features (n-grams, metadata) for malware or malicious URL detection and discuss evasion challenges.
Unsupervised Learning and Anomaly Detection
Lab/Assignment: Implement clustering and autoencoder-based anomaly detection for insider threat or unusual login behavior.
NLP for Security (Logs, Alerts, Phishing Text)
Lab/Assignment: Apply text embeddings and simple transformers to classify phishing messages or triage alerts.
AI for SOC and Incident Response Automation
Lab/Assignment: Build a prototype to prioritize alerts or generate incident summaries from logs, integrating with a basic playbook.
Adversarial ML: Evasion and Poisoning
Lab/Assignment: Implement simple gradient-based evasion against a classifier and simulate data poisoning on a security dataset.
Defending ML Systems
Lab/Assignment: Explore adversarial training, input validation, out-of-distribution detection, and monitoring for model abuse.
Generative AI in Cybersecurity
Lab/Assignment: Use or critique LLM-based assistants for threat hunting, detection engineering, or malware analysis, including prompt-injection and data-leakage risks.
Policy, Governance, and Ethics
Lab/Assignment: Discuss regulation, AI use policies in cyber operations, model accountability, and auditability.
Capstone Project Presentations
Lab/Assignment: Students present an end-to-end AI-for-cyber project using real or realistic security data.
Assessments
Weekly Labs (8-9)
Hands-on implementation of ML models for various security use cases
Quizzes
Short, concept-focused assessments to reinforce key learning objectives
Midterm Exam
Theory + mini lab covering weeks 1-6
Final Capstone Project
Report + code + presentation of end-to-end security AI solution
Example Capstone Project Ideas
ML-based Intrusion Detection System
Build and evaluate an ML-based IDS for a specific network segment, comparing multiple algorithms and addressing false positive reduction.
Phishing Detection Pipeline
Design a comprehensive phishing detection system using NLP and email metadata, with real-time classification capabilities.
Adversarially Robust Malware Classifier
Develop and harden a malware classifier against simple adversarial attacks, implementing defensive techniques learned in class.
AI-Powered SOC Assistant
Prototype an AI assistant to help SOC analysts triage SIEM alerts safely, with built-in safeguards against prompt injection and data leakage.
Related External Courses & Resources
Supplement your learning with these industry-recognized courses and lab modules
AI-Aided Cybersecurity
ELVTRCohort-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 CourseSEC595: Applied Data Science and AI/Machine Learning for Cybersecurity
SANSAdvanced, 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 CourseAI Cybersecurity Attack & Defend Course
Learning TreeFocuses 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 CourseArtificial Intelligence for Cybersecurity Training Course
uCertifyOnline training aligned to an "AI for cybersecurity" text, with built-in virtual labs targeting threat detection and prevention using AI techniques.
Visit CourseML for Cybersecurity (Google Colab Lab Modules)
Open SourceA 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 GitHubEE P 595: Hands-on Machine Learning for Cyber Security
University of WashingtonUniversity 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 CourseSecurity & Adversarial AI
FedLearnSelf-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 CourseAdversarial Machine Learning Course
Infosec SkillsShort 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 CourseGenerative AI for Cybersecurity Course
CodeRedPractical 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 CourseSecuring and Defending AI: 12-week outline
Course Overview
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.
Learning Objectives
By the end of the course, students will be able to:
- Explain core AI and ML concepts relevant to cybersecurity (supervised/unsupervised learning, deep learning, NLP)
- Apply ML to security problems such as phishing detection, intrusion detection, and malware classification
- Evaluate model performance and limitations on real security data, including issues like imbalance and drift
- Design and implement basic adversarial attacks and defenses against ML and LLM-based systems
- Use AI to enhance SOC workflows (alert triage, log analysis, playbook automation)
- Discuss ethical, legal, and policy implications of AI in cyber operations and AI system deployment
Weekly Curriculum
Introduction to AI & Cybersecurity
Lab/Assignment: Environment setup (Python, Jupyter, security datasets); exploratory data analysis on a simple security dataset.
Supervised Learning for Phishing/Spam
Lab/Assignment: Train baseline classifiers on phishing or spam email datasets; evaluate precision/recall and discuss false positives.
Network Intrusion Detection
Lab/Assignment: Build supervised and unsupervised models for detecting port scans, brute force, or other intrusion events.
Malware and URL Classification
Lab/Assignment: Implement malware or malicious-URL classifiers using static features; discuss evasion strategies and limits of static ML.
Unsupervised Learning & Anomaly Detection
Lab/Assignment: Use clustering or autoencoders for detecting outliers in authentication or endpoint data.
NLP for Security (Logs, Alerts, Phishing Text)
Lab/Assignment: Apply embeddings and simple transformer models to classify phishing messages or prioritize alerts.
AI for SOC & Incident Response
Lab/Assignment: Prototype an AI-assisted pipeline for alert triage or incident summarization; connect concepts to SOC workflows.
Adversarial Machine Learning: Attacks
Lab/Assignment: Implement basic evasion and poisoning attacks against a security classifier; analyze impact on detection performance.
Defending ML Systems
Lab/Assignment: Explore adversarial training, input validation, and monitoring for model misuse; evaluate trade-offs between robustness and performance.
Securing LLMs: Prompt Injection & RAG
Lab/Assignment: Use an LLM lab (e.g., AI Security Training Lab) to perform prompt injection and jailbreaks; implement simple guardrails and filters.
Governance, Policy, and Ethics
Lab/Assignment: Discuss AI risk management, security standards (OWASP LLM Top 10, MITRE ATLAS) and organizational controls.
Capstone Projects
Lab/Assignment: Students present end-to-end AI-for-cyber or AI-security projects, including threat models, experiments, and lessons learned.
Assessment Model
Weekly Labs & Homework
Hands-on implementation of security ML models and adversarial techniques
Quizzes
Short conceptual checks on AI security principles and techniques
Midterm Exam
Concepts + mini lab covering weeks 1-6
Final Capstone Project
Report, code, and presentation of comprehensive AI security solution
Example Capstone Project Topics
ML-based Network Intrusion Detector
Build and evaluate an ML-based intrusion detector with evaluation under realistic noise and data imbalance scenarios.
Phishing Email Detection Pipeline
Create a phishing detection system using NLP and metadata, with model monitoring and drift detection over time.
Hardened Malware Classifier
Develop a malware classifier hardened against simple adversarial attacks including evasion and obfuscation techniques.
Secure LLM-powered SOC Assistant
Build a secure LLM assistant for SOC analysts, including prompt-injection threat modeling and basic guardrails.
Defense Comparison Study
Comparative analysis of different defenses (adversarial training vs. anomaly detection) for a chosen security task.
Core AI Security Courses
Industry-recognized courses focused on securing and defending AI systems
AI SecureOps: Attacking & Defending AI Applications
DEF CON / Commercial TrainingHands-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 MoreAI-Aided Cybersecurity
ELVTRCohort-based course on applying machine learning to security use cases, with labs for intrusion detection, malware classification, anomaly detection, and adversarial behavior.
Visit CourseSEC595: Applied Data Science and AI/Machine Learning for Cybersecurity
SANSLab-heavy course where students build models for threat classification, anomaly detection, and malware analysis using classical ML and deep learning.
Visit CourseAI Cybersecurity Attack & Defend Course
Learning TreeFocuses on offensive and defensive applications of AI in cybersecurity, including practical labs on threat detection and automated response.
Visit CourseGenerative AI for Cybersecurity Course
CodeRedFocused on using and securing generative AI in cyber operations, including labs on building GenAI-powered tools and mitigating their risks.
Visit CourseSecurity & Adversarial AI
FedLearnSelf-paced course on adversarial attacks (evasion, poisoning, Trojans) and corresponding defensive techniques like adversarial training and outlier detection.
Visit CourseAI for Cybersecurity
Johns Hopkins University (EP)Graduate-level course that integrates AI techniques into cybersecurity practice; includes applied projects and comprehensive learning outcomes.
View CourseOpen-Source AI Security Labs
Free, hands-on GitHub repositories for practicing AI security techniques
AI Security Training Lab
GitHubOpen-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.
AISafetyLab
GitHubResearch-oriented framework for AI safety attacks, defenses, and evaluation on multiple datasets, suitable for running custom adversarial experiments and benchmarking robustness.
Awesome-AI-Security
GitHubCurated list of AI security resources, including courses, standards (OWASP, NCSC), tools, and additional labs, useful as a broader reading and tooling hub.