MyHackerZone

Intro to AI Security

Introduction to AI Security provides foundational knowledge of the security risks, vulnerabilities, and protective measures associated with artificial intelligence systems. Designed for beginners, this course explores AI security fundamentals, common threats such as prompt injection and data poisoning, model security, secure AI application development, and AI security monitoring. It also introduces responsible AI practices and relevant security frameworks.

Through practical exercises, you’ll learn how to identify potential attacks against AI systems, recognize weaknesses in AI-powered applications, apply basic security controls, and evaluate AI outputs for potential risks. By the end of the course, you’ll have a strong understanding of AI security principles and the practical skills needed to assess and protect AI-enabled systems, preparing you for more advanced training in AI security and cybersecurity.

Course Objectives

By the end of this course, students will be able to:

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Understand AI security fundamentals and essential terminology.
Identify common threats and vulnerabilities affecting AI systems.
Explain prompt injection, data poisoning, model misuse, and sensitive information disclosure.
Recognize security risks in large language models and AI-powered applications.
Apply basic security controls to protect AI inputs, outputs, and data.
Understand AI security testing, monitoring, and incident identification.
Evaluate AI-generated outputs for potential security and privacy risks.
Apply responsible AI principles and foundational AI security best practices.

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The Course Curriculam

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Introduction to AI Security
AI system architecture and security fundamentals
AI threat landscape and attack surfaces
Prompt injection and jailbreak attacks
Training data poisoning and data manipulation
Adversarial machine learning fundamentals
Sensitive information disclosure and privacy risks
Model theft, misuse, and unauthorized access
Securing large language models and AI applications
Input validation, output filtering, and access controls
AI security testing and vulnerability assessment
AI security monitoring and threat detection
Responsible AI and security governance
AI security frameworks and best practices

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Exploring AI Security Risks: Examine a sample AI application and identify potential security concerns.
Prompt Injection Awareness: Analyze controlled examples of prompt injection and understand how untrusted instructions can influence AI behavior.
Identifying Unsafe AI Outputs: Evaluate AI-generated responses for sensitive information disclosure, unsafe recommendations, and policy violations.
Understanding Data Poisoning: Explore a simulated dataset and observe how manipulated training examples can affect model behavior.
Securing AI Application Inputs: Apply input validation and separate trusted instructions from untrusted user-provided content in a sample application.
Implementing AI Output Validation: Evaluate model responses against predefined rules before displaying or processing them.
Protecting Sensitive Information: Identify sensitive data in sample prompts and implement basic data-handling safeguards.
AI Security Monitoring: Analyze sample AI application logs to identify suspicious prompts, repeated attack attempts, and unusual usage patterns.
AI Security Assessment: Perform a basic security review of a sample AI application and document findings, risks, and recommended mitigations.
Responsible and Secure AI: Evaluate an AI use case against security, privacy, transparency, and responsible-use requirements.

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Course Features

Duration

2 Weeks/25 Hrs
Students

(43)
Lessons

(3)
Enroll

35 Sutdens

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