Master the Future of AI:AI Security Essentials

Released !

Earn your CAISE (Certified AI Security Expert) certification and lead the secure deployment of artificial intelligence.

Certification

CAISE

Certified AI Security Essentials

Level

Beginner

Beginner Level

Duration

~30 Hours

10 hours course + 20 hours labs + quizzes

Format

Self-paced, blended learning

videos + quizzes + optional labs

Overview

Why This Course?

Build a strong foundation in AI security, governance, and emerging AI threats.

Artificial Intelligence is transforming industries, enabling powerful capabilities in automation, healthcare diagnostics, financial analysis, and content generation. However, the rapid adoption of AI introduces new security risks and governance challenges that organizations are often unprepared to manage.

The AI Security Essentials course introduces learners to the fundamental principles of securing AI systems. It explains how AI technologies work, where vulnerabilities arise, and how organizations can design, deploy, and govern AI responsibly.

Participants will explore security risks across the entire AI ecosystem including:

machine learning models

large language models (LLMs)

agentic AI systems

MCP ecosystems and tool integrations

deepfake and disinformation threats

Shadow AI usage within enterprises

The course bridges technical security, governance, and risk management, helping professionals understand how to integrate security and responsible AI practices throughout the AI lifecycle.

Course Overview

This course provides a comprehensive introduction to AI security risks and governance frameworks.

Participants learn how to:

understand how AI systems are built

identify vulnerabilities in AI architectures

recognize emerging AI threats

apply AI security frameworks in practice

implement responsible AI governance

The course connects foundational knowledge to AI Security Academy’s advanced certification tracks, including:

Certified AI Security Red Teamer

Certified Agentic Security Practitioner

Who Should Attend

This course is designed for professionals seeking to understand AI security risks and governance requirements.

Recommended for:

Security engineers and architects

AI and ML developers

DevSecOps professionals

Risk and compliance teams

CISOs and security leaders

policy and governance professionals

educators and students exploring AI security

No prior AI expertise is required.

What You Will Learn

Participants completing this course will be able to:

explain how AI systems are structured (ML, LLM, Agentic)

identify key AI security vulnerabilities

recognize deepfake and synthetic media threats

understand Shadow AI risks in enterprises

apply OWASP AI security frameworks

understand regulatory and governance frameworks for AI

connect AI security fundamentals to advanced AI security practices

Hands-On Labs

The course includes 17 hands-on labs designed to reinforce key concepts.

Participants will practice:

dataset poisoning attacks

federated learning attacks

prompt injection attacks

hidden prompt injection techniques

RAG vulnerabilities

agent tool misuse

model stealing attacks

improper output handling exploitation

Each lab demonstrates how AI systems can be manipulated and how security controls can mitigate these risks.

Course Modules

MODULE 1

Module 1: Foundations of Artificial Intelligence

Participants build foundational literacy in AI technologies.

Topics include:

evolution of artificial intelligence

machine learning paradigms

deep learning and neural networks

natural language processing and generative AI

the evolution from ML to LLM to agentic AI

AI system lifecycle and data pipelines

MODULE 2

Module 2: AI Threat Landscape

This module explores the complete attack surface of AI systems.

Topics include:

adversarial machine learning attacks

prompt injection and LLM data leakage

agentic AI vulnerabilities

Shadow AI risks in organizations

deepfake and synthetic media threats

AI supply chain vulnerabilities

MODULE 3

Module 3: OWASP Machine Learning Security Top 10

Participants explore the OWASP ML Top 10 security risks.

Topics include:

data poisoning attacks

model inversion and extraction

adversarial example crafting

secure MLOps pipelines

explainability and fairness metrics

Hands-on labs demonstrate real ML security failures.

MODULE 4

Module 4: OWASP LLM Security Top 10

This module focuses on vulnerabilities in large language models.

Topics include:

prompt injection attacks

jailbreak techniques

data exfiltration risks

input and output validation

LLM guardrails and safety mechanisms

Hands-on labs demonstrate prompt exploitation techniques.

MODULE 5

Module 5: Agentic AI Security

Participants explore security challenges introduced by autonomous AI agents.

Topics include:

agent architectures and orchestration frameworks

excessive autonomy risks

tool and function abuse

memory poisoning attacks

RAG architecture and vulnerabilities

observability and telemetry for agent workflows

MODULE 6

Module 6: Model Context Protocol (MCP) Security

This module introduces the Model Context Protocol (MCP) ecosystem.

Topics include:

MCP architecture

tool discovery and integration

MCP threat models

security risks mapped to OWASP frameworks

foundational MCP security principles

MODULE 7

Module 7: Secure AI Development Lifecycle

Participants learn how to integrate security across the AI lifecycle.

Topics include:

data security in AI pipelines

secure preprocessing and labeling workflows

encryption and anonymization techniques

DevSecOps practices for AI systems

protecting data integrity and lineage

MODULE 8

Module 8: Deepfakes and Disinformation

This module explores the growing risks of synthetic media.

Topics include:

generative adversarial networks

diffusion models

deepfake creation techniques

fraud and impersonation attacks

detection technologies and watermarking

Participants complete a deepfake detection challenge.

MODULE 9

Module 9: Regulations and Governance

Participants learn how to navigate global AI governance frameworks.

Topics include:

EU AI Act

NIST AI Risk Management Framework

ISO/IEC 42001 AI management systems

ethical AI principles

compliance and risk management

MODULE 10

Module 10: AI Red Teaming

The course concludes with an introduction to AI red teaming practices.

Topics include:

fundamentals of AI adversarial testing

reconnaissance and attack planning

exploitation techniques across AI systems

incident detection and response

vulnerability disclosure for AI systems

Hands-on labs simulate AI attack scenarios and response workflows.

Course Format

Delivery includes:

video lessons

guided exercises and quizzes

hands-on security labs

practical demonstrations

interactive challenges

Total labs: 17+

Estimated completion time: 35+ hours

Why This Course Matters

Artificial Intelligence is rapidly becoming embedded in enterprise platforms, decision systems, and digital services.

However, many organizations deploy AI capabilities without fully understanding the security, governance, and ethical risks they introduce.

Security professionals must now understand how AI systems operate and how attackers can exploit them.

This course equips learners with the foundational knowledge needed to identify AI security risks, apply industry frameworks, and support responsible AI adoption within their organizations.