Course Overview
The Certified Artificial Intelligence Security Professional (CAISP) training course provides participants with the knowledge and skills required to secure artificial intelligence systems throughout their life cycle.
The course covers AI-specific threats, vulnerabilities, governance, risk management, and security controls aligned with modern cybersecurity and AI best practices. Through a combination of theory and practical application, participants will learn how to protect AI models, data, infrastructures, and operations against emerging threats.
The course also addresses how AI can be leveraged as a defensive capability, covering AI-driven security monitoring, threat hunting, threat intelligence, and AI-assisted security analysis.
Why Should You Attend?
Artificial intelligence systems introduce security risks that extend well beyond traditional cybersecurity controls. Unlike conventional software, AI systems can be manipulated through their inputs, corrupted through their training data, compromised through their supply chains, and exploited through the tools and permissions they are granted often in ways that leave no trace in standard security logs. Security professionals who work with or around AI systems need a fundamentally different set of competencies from those that protect traditional IT environments, and this course is designed to build exactly those competencies.
In this course, participants will develop a thorough understanding of how AI systems are attacked, how they are defended, and how they are governed, moving from foundational AI security concepts through to hands-on practical application.
The training course includes lab exercises that give participants direct experience with real-world AI security tools and techniques, including prompt injection testing, AI red teaming, threat intelligence workflows, and malware analysis using AI-assisted reverse engineering tools. These labs are designed to reinforce the technical content of each section and ensure that participants leave the course with skills they can apply immediately in their professional roles.
By the end of the course, participants will be equipped to assess the security posture of AI systems, implement appropriate controls, respond to AI security incidents, and support their organization’s AI governance and compliance obligations.
Who Should Attend?
This training course is intended for:
AI and machine learning engineers responsible for developing, deploying, or maintaining AI systems
Cybersecurity and information security professionals looking to extend their expertise to AI-specific threats and defenses
SOC analysts and threat intelligence professionals working in AI-enabled security environments
IT security architects and consultants designing or reviewing AI system architectures
Risk management and compliance professionals working with AI governance frameworks and regulatory requirements
Data scientists and AI developers seeking to apply security practices throughout the AI development life cycle
Technology managers and technical leads responsible for AI security strategy and program management
Learning Objectives
Upon completing this training course, participants will be able to:
Explain the architecture and operation of machine learning, deep learning, LLMs, and AI agents, and identify risks arising from their design, training, and deployment
Analyze threats to AI and LLM systems using MITRE ATLAS and the OWASP LLM Top 10 across the AI attack life cycle
Apply AI security controls, monitoring strategies, and threat intelligence workflows to detect, contain, and investigate adversarial activity
Design and evaluate an AI security program covering incident response, supply chain governance, regulatory compliance, secure architecture, and red teaming
Educational Approach
This training course combines instructor-led lectures, hands-on lab exercises, and real-world scenarios to reinforce learning.
Hands-on lab exercises give participants direct experience with real-world AI security tools, reinforcing the theoretical content of each section.
The quiz structure within the course closely mirrors that of the certification exam, ensuring participants are well-prepared.
Prerequisites
Candidates are expected to have a solid foundation in core cybersecurity and information security concepts. Familiarity with general security principles, including threat modeling, access control, incident response, and security monitoring, is expected. A basic understanding of how machine learning systems work, including awareness of concepts such as model training, data pipelines, and API-based services, is also expected.
The course covers five days of content, with the certification exam on the fifth day. In the self-study format you cover the same material at your own pace.
Yes. The course fee includes the PECB certification and examination fees, plus one free exam retake within 12 months.
Candidates are expected to have a solid foundation in core cybersecurity and information security concepts. Familiarity with general security principles, including threat modeling, access control, incident response, and security monitoring, is expected. A basic understanding of how machine learning systems work, including awareness of concepts such as model training, data pipelines, and API-based services, is also expected.
It is available as self-study (English). Choose the format and language before adding the course to your cart.
There is no scheduled live class for this course at the moment. Organizations that want a dedicated live session can contact us at [email protected].
PECB issues the certificate. After passing the exam you can apply for the “PECB Certified Artificial Intelligence Security Professional” credential. Horus Academy is a PECB Authorized Silver Partner and does not issue the certificate itself.
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The “PECB Certified Artificial Intelligence Security Professional” exam fully meets the PECB Examination and Certification Program (ECP) requirements. It covers the following competency domains:
Domain 1: Security foundations for AI and ML
Domain 2: AI attack techniques and adversarial threats
Domain 3: AI security controls and defensive operations
Domain 4: AI enhanced security analysis and threat intelligence
Domain 5: AI governance, risk management, and secure architecture
After successfully passing the exam, you can apply for one of the credentials below. You will receive the certificate once you comply with all the requirements related to the selected credential.
The requirements for the “PECB Certified Artificial Intelligence Security Professional” certification are:
| Credential | Exam | Professional experience | Project experience | Other requirements |
|---|---|---|---|---|
| PECB Certified Provisional Artificial Intelligence Security Professional | PECB Certified Artificial Intelligence Security Professional exam or equivalent | None | None | Signing the PECB Code of Ethics |
| PECB Certified Artificial Intelligence Security Professional | PECB Certified Artificial Intelligence Security Professional exam or equivalent | Five years: two years in information or cyber security | 300 hours | Signing the PECB Code of Ethics |
The information or cyber activities should follow best practices and include the following:
Course agenda
Day 1: AI, ML, and deep learning fundamentals
Day 2: LLM architecture, training workflows, and attack tactics
Day 3: AI security controls and mitigation strategies
Day 4: Detection, response, and secure deployment
Day 5: Certification exam
