Trusted by 10,000+ Learners
Certified AI Security Professional (CAISP)TM
Neutralize AI threats before attackers strike. Transform into an AI Security Pro who can detect LLM Top 10 vulnerabilities, block AI supply chain attacks, and implement MITRE ATLAS defenses that others miss. Organizations with Certified AI Security Professionals reduce AI vulnerabilities by 78%. Achieve the best AI security certification that puts you at the forefront of the AI security realm.
Self-paced learning
Browser-based lab access
24/7 instructor support
Self-paced learning mode
Browser-based lab access
24/7 instructor support
Self-paced learning mode
Browser-based lab access
24/7 instructor support
Trusted by top companies across industries, empowering thousands of professionals worldwide. Join the ranks of security leaders.
Course Chapters
“Here’s exactly what you’ll master in 7 hands-on chapters:”
Course Prerequisites
- Course participants should have knowledge of running basic Linux commands like ls, cd, mkdir, etc.
- Familiarity with any scripting language like Python, Golang, or Ruby helps. However, it’s not a necessity.
Chapter 1: Introduction to AI Security
- Course Introduction (About the course, syllabus, and how to approach it)
- About Certification and how to approach it
- Course Lab Environment
- Lifetime course support (Mattermost)
- An overview of AI Security
- Basics of AI and ML
- What is AI?
- History and evolution of AI
- Key concepts in AI
- Types of AI
- Narrow AI vs. General AI
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Natural Language Processing (NLP)
- Computer Vision
- Core Components of AI Systems
- Algorithms and Models
- Data
- Computing Power
- Introduction to Machine Learning
- What is Machine Learning?
- Differences between AI and ML
- Key ML concepts
- Retrieval Augmented Generation
- Basics of Deep Learning
- What is Deep Learning?
- Introduction to Neural Networks
- Brief overview of Convolutional Neural Networks (CNNs)
- Hands-On Exercise:
- Building a Chatbot Using an LLM
Chapter 2: Understanding and Attacking Large Language Models
- Introduction to Large Language Models
- Definition of Large Language Models
- How LLMs work
- Importance and impact of LLMs in AI
- Understanding LLMs
- GPT (Generative Pre-trained Transformer)
- BERT (Bidirectional Encoder Representations from Transformers)
- Training and Augmenting LLMs
- Foundational model and fine-tuned model
- Retrieval augmented generation
- Use Cases of LLMs
- Text Generation
- Text Understanding
- Conversational AI
- Attack Tactics and Techniques
- Mitre ATT&CK
- Mitre ATLAS matrix
- Reconnaissance tactic
- Resource development tactic
- Initial access tactic
- ML model access tactic
- Execution tactic
- Persistence tactic
- Privilege escalation tactic
- Defense evasion tactic
- Credential access tactic
- Discovery tactic
- Collection tactic
- ML attack staging
- Exfiltration tactic
- Impact tactic
- Real-World LLM attack tools on the internet
- XXXGPT
- WormGPT
- FraudGPT
- Hands-On Exercises:
- Creating a Simple Chatbot
- Exploring How Tokenizers Work
- Building a Summarizer Tool Using an LLM
- Building a Fine-tuned Model
- Building a Simple Website Scraper
- Building a RAG System
- Attacking an LLM Model using TextAttack
- Performing Sentiment Analysis Using an LLM
- Backdoor Attacks using BackdoorBox
- Building a Speech To Text System
Chapter 3: LLM Top 10 Vulnerabilities
- Introduction to the OWASP Top 10 LLM attacks
- Prompt Injection
- System prompts versus user prompts
- Direct and Indirect prompt injection
- Prompt injection techniques
- Mitigating prompt injection
- Insecure Output Handling
- Consequences of insecure output handling
- Mitigating insecure output handling
- Training Data Poisoning
- LLM’s core learning approaches
- Mitigating training data poisoning
- Model Denial of Service
- DoS on networks, applications, and models
- Context windows and exhaustions
- Mitigating denial of service
- Supply Chain Vulnerabilities
- Components or Stages in an LLM
- Compromising LLM supply chain
- Mitigating supply chain vulnerabilities
- Sensitive Information Disclosure
- Exploring data leaks in various incidents
- Mitigating sensitive information disclosure
- Insecure Plugin Design
- Plugin/Connected software attack scenarios
- Mitigating insecure plugin design
- Excessive Agency
- Excessive permissions and autonomy
- Mitigating excessive agency
- Overreliance
- Understanding hallucinations
- Overreliance examples
- Mitigating overreliance
- Model Theft
- Stealing models
- Mitigating model theft
- Hands-On Exercises:
- Learning Prompt Injection Step by Step
- Working with User Prompts and System Prompts
- Extracting Sensitive Information Through an LLM
- LLM Hallucination Lab
Chapter 4: AI Attacks and Defenses Using DevOps
- Introduction to AI in DevOps
- Definition and Principles of DevOps and DevSecOps
- The Role of AI in Enhancing DevOps Practices
- Types of AI Attacks on DevOps Teams
- Model Creation and Deployment Process/Pipeline
- Attacks on Pipelines
- Cases of Attacks in DevOps and AI
- Hugging Face Artificial Intelligence (AI) Platform
- Notpetya Attack
- SAP AI Core Vulnerabilities
- DevSecOps Tooling and Defenses for AI Projects
- Software Composition Analysis for AI Projects
- Static Analysis of Models and Applications
- Dynamic Analysis of Models and Applications
- AI Firewalls for Guarding Models
- Hands-On Exercises:
- Analyzing and Fixing Vulnerabilities in Third-Party Components
- Finding and Fixing Weaknesses in AI Code
- Scanning a Malicious Pickle File using Picklescan
- Scanning an LLM for Agent Based Vulnerabilities
- Sanitizing Prompts with LLM Guard
- Guarding LLM Input and Output
Chapter 5: Threat Modeling AI Systems
- What is Threat Modeling
- Why Threat Model?
- Threat Modeling Challenges
- Threat Modeling Benefits
- The Threat Model Parlance
- What are Assets?
- Weaknesses and Vulnerability
- Risk Management Stages
- STRIDE Methodology
- Diagramming for Threat Modeling
- Data Flow Diagram
- DFD Components
- An LLM Application Architecture
- Simple LLM Architecture
- DFD for an LLM Architecture
- STRIDE Threats for LLM Applications
- AI Threat Libraries
- STRIDE
- OWASP LLM Top 10
- Mitre ATLAS
- BIML Risk Framework
- AI Risk Repository
- AI Incident Database
- AI Threat Map
- Rating and Managing Risks
- Risk Management Meets Threat Modeling
- Risk Management Strategies
- Example Risk Rating Methodology
- Hands-On Exercises:
- Threat Modeling an AI System
Chapter 6: Supply Chain Attacks in AI
- An overview of the Supply Chain Security
- Introduction to AI Supply Chain Attacks
- Data, model, and infrastructure-based attacks
- Abusing Generative AI for package masquerading
- Vetting Software frameworks
- Creating a vetting process
- Automating vetting of third party code
- Scanning for vulnerabilities
- Mitigating dependency confusion
- Dependency pinning
- Supply chain frameworks
- SLSA
- Software Component Verification Standard (SCVS)
- Transparency and Integrity in AI Supply Chain
- Generate a Software Bill of Materials
- SBOMs, Provenance, and Attestations
- Model Cards and MLBOMs
- Model Signing
- Hands-On Exercises:
- Editing Models Using ROME Technique
- Creating Trojanized Models
- Creating Trojanized Neural Network Models
- Scanning Models and Injecting Malicious Code
- Signing and Verifying Machine Language Models using Cosign
Chapter 7: Emerging Threats, Governance, and Compliance in AI
- Emerging Threats in AI
- Model-mediated supply chain attacks
- Self propagating AI model worms
- Backdoors in Fine-Tuning
- AI assisted evolving firmware
- Models without provenance
- AI Governance and Compliance
- Standards, Guidelines, Frameworks, Checklists for AI Security
- NIST RMF
- ISO/IEC 42001
- Other standards and guidelines
- AI Acts, Bills, and Legislations
- EU AI Act
- US Legislations
- Standards, Guidelines, Frameworks, Checklists for AI Security
- Hands-On Exercises:
- Working with AI Agents
- Abusing AI Agents
Practical DevSecOps Certification Process
- After completing the course, you can schedule the CAISP exam on your preferred date.
- The process of achieving Practical DevSecOps course certifications can be found on the exam and certification page.
Application Security (AppSec) Engineer
AI/LLM Red Teamer (Offensive Security Specialist)
AI Security Engineer / Architect
DevSecOps Engineer / Pipeline Architect
MLOps Engineer (Machine Learning Operations)
Master AI Security. Skills AI can’t replicate.
Career Outlook
What can I do with the Certified AI Security Professional Course?
With CAISP, you can find and fix LLM vulnerabilities, run AI threat models, audit AI supply chains, and build defenses against prompt injection and model poisoning. You’ll leave with skills you can apply on day one.
Who Should Take the Certified AI Security Professional Course?
AI is in production. These are the roles responsible for keeping it secure.
Application Security (AppSec) Engineer
LLMs are the new attack surface in every app your team ships. With CAISP, you can audit LLM integrations for prompt injection, insecure output handling, and plugin abuse. The same threat modeling discipline you already know, now applied to AI.
AI/LLM Red Teamer (Offensive Security Specialist)
Most red teams have no structured methodology for attacking AI systems. CAISP gives you MITRE ATLAS tactics, hands-on adversarial attack labs, and real techniques for model theft, backdoor injection, and data poisoning. You’ll run attacks, not just read about them.
AI Security Engineer / Architect
You’re the one signing off on whether an AI system is safe to ship. CAISP builds the technical depth to back that decision: threat modeling AI architectures with STRIDE, applying NIST RMF and ISO 42001, and writing security requirements that go beyond generic checklists.
DevSecOps Engineer / Pipeline Architect
Your CI/CD pipeline now ships AI models alongside application code. CAISP teaches you to scan models for malicious payloads, catch poisoned dependencies before deployment, and wire AI-specific security gates into your existing pipelines.
MLOps Engineer (Machine Learning Operations)
You manage the infrastructure that trains, serves, and monitors models. CAISP shows you where that infrastructure gets attacked: supply chain compromises, model signing gaps, SBOMs for ML, and runtime defenses. Security becomes part of your operational workflow, not someone else’s problem.
97%
Of organizations hit by an AI security breach had no AI access controls in place. IBM’s 2025 Cost of a Data Breach Report studied 600 organizations. The gap between AI adoption and AI security governance is already costing enterprises millions.
$235K+
base salary for a Principal AI Security Engineer at a public tech or financial services company in 2026, with total compensation exceeding $400,000 when equity is included.
Understanding the above numbers
These figures reflect industry-wide trends from ZipRecruiter, SalaryExpert, the Bureau of Labor Statistics, and market research. Actual salaries depend on your experience, location, industry, and how effectively you apply your skills. We provide the training. The results are yours to build.
And you’ll learn it the right way, through hands-on experience.
What you’ll learn from the Certified
AI Security Professional Course?
Understanding and Attacking Large Language Models
- Learn how LLMs like GPT and BERT work
- Explore MITRE ATT&CK and ATLAS frameworks
- Execute adversarial attacks on AI chatbots
LLM Top 10 Vulnerabilities
- Identify OWASP Top 10 LLM vulnerabilities
- Recognize prompt injection and data poisoning
- Practice real-world LLM attack scenarios
AI Attacks and Defenses Using DevOps
- Analyze attacks on AI deployment pipelines
- Implement DevSecOps security tooling for AI
- Conduct poisoned pipeline attack exercises
Threat Modeling AI Systems
- Apply STRIDE methodology to model AI threats
- Leverage AI threat libraries and frameworks
- Perform risk rating with IriusRisk tools
Supply Chain Attacks in AI
- Discover AI supply chain attack methods
- Implement SLSA and SCVS security frameworks
- Generate SBOMs and model signatures
Emerging Threats, Governance, and Compliance in AI
- Defend against AI worms and backdoor threats
- Apply NIST RMF and ISO/IEC 42001 standards
- Learn the EU AI Act and US AI legislation
Exploit LLMs. Patch the Gaps. No Setup Required.
No VM setup. No tool installations. You get 30+ guided labs running directly in your browser, covering prompt injection, LLM vulnerability exploitation, AI supply chain attacks, adversarial model attacks, and MITRE ATLAS defenses. Each lab mirrors a real AI Security attack scenario. You run the exploit, then build the fix.
We have provided training and presented at numerous industry events.
Here’s what graduates are saying. Click their profiles. Verify the results.
Hear from our learners
Explore the global impact of our Practical DevSecOps Certifications through our learners’ testimonials.
Frequently asked questions
What are the prerequisites for enrolling in the Certified AI Security Professional Course?
You should have a basic knowledge of running Linux commands and also be familiar with any scripting language like Python, Golang, or Ruby. It will definitely be helpful.
What’s included in the AI Security Professional course package?
You will receive 3 years of access to the videos, 60 days of browser-based labs, a PDF manual, checklists, 30+ Guided Exercises, 24/7 student support through a dedicated Mattermost channel, and one exam attempt.
Do the Labs for the AI Security Professional Course Start Immediately after enrollment?
No, the course does not begin automatically upon enrollment. After purchasing, students will have the opportunity to select their preferred start date. Course access will be provided from the chosen start date.
Does the AI Security Professional Course come with CPE points?
What is the Exam Format for the AI Security Course?
The exam is a task-oriented exam where you will have to solve 5 challenges in 6 hours and have an additional 24 hours to complete the report and submit it for evaluation. For more information, visit this link.
Should I go to an exam center, or is the exam online?
No, it is an online exam. You can take the exam from the comfort of your home or office.
How long is the AI Security certification valid?
The AI Security Professional Certification is a lifetime credential; therefore, there is no need to worry about renewals. Once you get it, it will be valid for the rest of your career.
What career advancement and salary benefits can I expect from completing the Certified AI Security Professional course?
Currently, traditional cybersecurity engineers make $95,000–$130,000. But AI Security Engineers? They start at $152,773, and with CAISP certification, you’re looking at $175,689–$213,882. That’s a 15-40% bump just for having the right certification. Meanwhile, professionals without it stay stuck around $110,000–$120,000, usually in support roles watching others get promoted.
Here’s what makes this different: the AI security market is racing toward $234 billion by 2032, but there are barely any certified professionals to fill these roles. Companies are desperate. They’re implementing AI everywhere but have no idea how to protect it. When you walk in with CAISP certification, you’re not just another candidate—you’re the solution to their biggest nightmare.
The Certified AI Security Professional isn’t just a piece of paper either. It’s comprehensive, hands-on training that actually teaches you to secure AI systems. Security engineers, red teamers, and AppSec professionals are all pivoting into AI security because they see what’s coming—this will be baseline knowledge for every security professional within five years.
Why Certified AI Security Professional course from Practical DevSecOps?
Practical DevSecOps delivers the industry-leading AI security certification built on real-world attack scenarios. The hands-on labs provide practical experience mitigating LLM vulnerabilities, preventing AI supply chain attacks, and implementing MITRE ATLAS defenses. Learn from industry veterans who’ve secured AI systems at Fortune 500 companies, mastering techniques deployable immediately.
You’ll learn to:
- Detect and neutralize adversarial attacks targeting LLMs, including prompt injection and model theft.
- Implement robust AI security controls using model signing, SBOMs, and dependency verification.
- Apply MITRE ATLAS framework and STRIDE-GPT methodologies to identify AI-specific vulnerabilities.
- Create effective incident response playbooks for AI system breaches and model compromise events.
Unmatched practical focus
Expert-crafted curriculum
Get real-world insights from experienced Security Experts.
Practical exam
Take a 6-hour examination to show what you have learned.
24/7 expert support
Future-Proof Your Career with AI Security Professional Training
Unlock your potential with AI Security Professional Training! Our Certified AI Security Professional Course equips you with job-ready skills. Conquer the 6-hour exam with confidence and open doors to exciting opportunities and challenges.