Certified DefenAI Professional (CDAIP) - AI Security Professionals & Engineers

Duration: 5 Days

Modes: In-Person, Live Online, On-Demand, On-Site

AI is transforming every industry. Attackers know it. This 100% hands-on programme equips you to identify, exploit, and defend against adversarial AI attacks across LLMs, deep learning models, and enterprise AI infrastructure. Learn to protect your organisation's most valuable AI assets before threat actors compromise them.

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Course Objectives

Understand the concepts and techniques used to exploit AI modules, including adversarial attacks, data poisoning, and model inversion attacks

Identify potential vulnerabilities in AI-powered systems and develop strategies to prevent exploitation by malicious actors

Implement effective defence mechanisms to protect AI modules from attacks launched by other AI systems

Develop a comprehensive understanding of the AI security landscape, including the latest threats, trends, and best practices in AI defence

Learn how to perform threat modelling specifically for AI systems

Understand the AI development lifecycle and where security vulnerabilities are introduced

Master techniques for jailbreaking LLMs and diffusion models to understand their weaknesses

Expected Outcomes

Understand the different attacks on Large Language Models (LLMs), Deep Learning Models (DLMs), and Tree-Ensemble Models

Identify and execute different types of AI exploitation techniques, including model inversion, adversarial examples, data poisoning, and model extraction

Analyse the risks and vulnerabilities associated with AI systems and develop mitigation strategies

Design and implement effective defence mechanisms to protect AI modules from adversarial attacks

Perform reconnaissance and vulnerability scanning on AI infrastructure

Exploit vulnerabilities in AI APIs and interfaces

Conduct membership inference attacks and model extraction attacks

Build secure LLM and GenAI applications with adversarial robustness

Training Modules

1. Introduction to AI and Machine Learning

1. Overview of AI and Machine Learning concepts
2. Types of AI models and architectures
3. AI development lifecycle and workflows
4. AI ethics and responsible AI principles

2. Introduction to AI Security and Attack Vectors

1. Overview of AI security landscape
2. Common attack vectors on AI models
3. Threat modelling for AI systems
4. AI security best practices and frameworks

3. Attacks on AI Models and Data Sources

1. Attacks on Large Language Models (LLMs)
2. Attacks on Deep Learning Models
3. Attacks on Tree-Ensemble Models and Forecasting
4. Data poisoning and manipulation attacks

4. Attacks on AI Infrastructure, APIs, and Jailbreaking LLMs

1. Reconnaissance and vulnerability scanning on AI infrastructure
2. Exploiting vulnerabilities in AI infrastructure
3. Attacks on AI APIs and interfaces
4. Jailbreaking LLMs and Diffusion Models

5. Advanced AI Attack Techniques and Defences

1. Membership inference attacks
2. Model inversion and extraction attacks
3. Adversarial defences and robustness
4. Course recap and assessment

Certification & Accreditation

Certification Path 1: GlobalACE Certification

The GlobalACE certification is designed to align with internationally recognised Knowledge, Skills, and Attitudes (KSA) standards for Information Security Professionals. Candidates are assessed through a combination of multiple choice questions, practical assessments, assignments, and case studies. Examinations are conducted at authorised centres across participating member countries, and successful candidates are eligible to apply as Associate or Professional Members under the GlobalACE framework, recognised in 64+ countries.

Certification Path 2: CyberKnights Certification

The CyberKnights certification is conducted through the KALAM platform, a purpose-built cybersecurity examination and skills validation system. Candidates take a 25-question MCQ exam within 60 minutes, with a pass mark of 70%. Exam fees are inclusive in the course fees. All certified candidates receive complimentary membership access to the KALAM Cybersecurity Collaboration and Community Skills Validation Platform, giving them access to an active community of security professionals.

Frequently Asked Questions

Do I need to be an AI engineer to attend this course?

No. The course is designed for a broad audience including security professionals, penetration testers, AI developers, data scientists, and anyone working with AI systems. Basic cybersecurity knowledge and familiarity with AI concepts is sufficient.

What types of AI models will I learn to attack and defend?

The course covers attacks on Large Language Models (LLMs), Deep Learning Models (DLMs), Tree-Ensemble Models, and Diffusion Models. You will also learn to exploit AI APIs, infrastructure, and perform data poisoning attacks.

Is this relevant if my organisation is just starting to adopt AI?

Absolutely. Understanding AI security risks before deployment is far more cost-effective than responding to incidents after the fact. This course helps you build security into your AI strategy from the ground up.

Is Python programming required?

Familiarity with Python is beneficial but not mandatory. The hands-on labs are guided, and the course builds foundational knowledge before advancing to technical exploitation exercises.

How is this different from a general cybersecurity course?

CDAIP focuses exclusively on the intersection of AI and security. It covers AI-specific attack vectors like adversarial examples, model inversion, LLM jailbreaking, and data poisoning that are not covered in traditional cybersecurity programmes.

What certification do I receive upon completion?

You receive a globally recognised certification through either the GlobalACE framework (recognised in 64+ countries) or the CyberKnights KALAM platform. The exam is a 2-hour hands-on assessment with a 70% pass mark.

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