CompTIA SecAI+ (CY0-001) CertMike's Certification

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Coursera
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Paid Course
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English
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Effort

15 hours
Sessions

Self-Paced
Level

Intermediate
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English

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Overview

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This course offers an in-depth exploration of AI security, focusing on model training, data protection, and real-world attack mitigation. Ideal for professionals looking to enhance their AI security expertise, it prepares learners for the CompTIA SecAI+ certification. In the ever-evolving world of artificial intelligence, securing AI systems has become crucial for organizations. This course dives deep into the essentials of AI security, exploring foundational concepts like machine learning, data protection, and attack prevention strategies. The focus is on equipping learners with the knowledge required to safeguard AI systems from threats while ensuring compliance with industry standards. As you progress through the course, you will develop a robust understanding of key AI security principles, including model risk assessment, data privacy, and securing AI lifecycle processes. With hands-on lessons on AI attack types, including data poisoning, model poisoning, and adversarial attacks, you will gain practical experience in identifying and mitigating risks. By the end of the course, you will be prepared for the CompTIA SecAI+ certification exam and possess the skills needed to implement secure AI systems in real-world scenarios. With clear, structured modules, learners will embark on a journey that builds confidence in AI security. Whether you're an AI engineer or cybersecurity professional, this course is designed to enhance your career by providing cutting-edge AI security knowledge. This course is designed for intermediate-level professionals in AI, cybersecurity, or cloud computing. Ideal for AI developers, cybersecurity professionals, and cloud security engineers, it will help you gain the expertise required to protect AI systems. Familiarity with basic machine learning and cybersecurity principles is recommended but not mandatory. The course follows a hands-on, interactive approach, offering practical examples and real-world use cases. It is structured to build your knowledge progressively, allowing you to apply concepts immediately. Throughout, you'll learn the theory behind AI security and how to implement it in real-world applications. This course is based on CompTIA SecAI+ (CY0-001) CertMike's Complete Certification Course, by Mike Chapple, Ph.D. and Fred Nwanganga, Ph.D.. This video is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Introduction
- This module provides an overview of the course structure, key learning goals, and the expected outcomes for learners. It sets the foundation for the content that follows and helps students understand the value of the course.
The SecAI+ Exam
- This module provides an overview of the SecAI+ exam, explores career opportunities in AI security, and highlights the importance of certification. It also offers study resources and strategies to help learners prepare effectively. By the end, learners will have a clear understanding of how to pursue a career in AI security and succeed in the certification process.
Passing the SecAI+ Exam
- This module provides learners with essential insights into the SecAI+ exam format, including in-person and at-home testing procedures, question types, and effective strategies for success. It covers key elements such as identification requirements, performance-based questions, and post-exam steps. Learners will gain practical knowledge to prepare for and navigate the exam confidently.
Domain 1: Basic AI Concepts Related to Cybersecurity
- This module introduces foundational AI concepts and their relevance to cybersecurity. Learners will explore how AI technologies interact with security frameworks and understand key considerations in AI development for secure systems.
Types of AI
- This module provides an in-depth overview of various types of artificial intelligence, including machine learning, deep learning, natural language processing, and generative AI. It explores their applications, differences, and relevance in cybersecurity. Learners will gain a foundational understanding of AI systems and their practical implementations.
Model Training Techniques
- This module explores various AI training techniques, including supervised, unsupervised, and reinforcement learning, as well as federated learning, model validation, and fine-tuning. Learners will gain an understanding of how these methods are applied in real-world scenarios, particularly in cybersecurity. The module also covers best practices and limitations of each approach.
Prompt Engineering
- This module explores the fundamental concepts of prompt engineering, including the roles of prompts in AI systems and various prompting strategies such as zero-shot, one-shot, and multi-shot. Learners will gain an understanding of how to structure effective prompts for improved AI interaction and performance in cybersecurity applications.
Data Processing
- This module covers essential concepts in data processing for AI systems, including data types, cleansing, verification, integrity, lineage, augmentation, balancing, and watermarking. Learners will gain practical skills in managing and securing data to improve model performance and reliability.
Retrieval-Augmented Generation (RAG)
- This module explores key aspects of Retrieval-Augmented Generation (RAG) including securing knowledge stores, maintaining data integrity, and addressing privacy concerns. Learners will gain a deep understanding of best practices for building secure and reliable RAG systems. The content emphasizes practical strategies for managing data in AI-driven applications.
Security in the AI Lifecycle
- This module explores the critical security considerations throughout the AI lifecycle, from data collection to deployment and ongoing maintenance. Learners will gain insights into aligning AI projects with business goals and understanding the risks at each stage. It emphasizes best practices for securing AI systems in real-world applications.
Human-Centric AI Design
- This module explores the principles and practices of designing AI systems that prioritize human needs and values. Learners will gain insight into how human involvement enhances AI reliability, ethics, and user trust. Key topics include human-in-the-loop mechanisms, oversight, and validation processes.
Domain 2: Securing AI Systems
- This module explores fundamental strategies for securing AI systems, including threat modeling, access controls, and monitoring techniques. Learners will gain an understanding of best practices for integrating security into AI development and deployment. The content emphasizes practical approaches to maintaining system integrity and protecting against potential threats.
Data and Training Attacks
- This module explores various types of attacks on AI systems, including data and model poisoning, biases, transfer learning vulnerabilities, and backdoor attacks. Learners will gain an understanding of how these threats impact system security and learn to identify and mitigate them effectively.
Prompt and Input Manipulation Attacks
- This module explores various AI security vulnerabilities, including prompt injection, guardrail circumvention, and input manipulation. Learners will gain an understanding of how these attacks work and how to identify and manage security risks in AI models. The content emphasizes practical strategies for securing language models against malicious inputs.
Model Extraction and Information Leakage Attacks
- This module explores various types of model extraction and information leakage attacks, such as model inversion, membership inference, and model theft. Learners will understand how these attacks can compromise AI systems and what risks they pose to data confidentiality and intellectual property. The module provides practical insights into the mechanics and implications of these security threats.
Integration and Operational Attacks
- This module explores the security risks associated with AI-driven applications, including supply chain vulnerabilities, insecure integration practices, and risks from output handling and overreliance on AI systems. Learners will gain an understanding of common attack vectors and strategies to mitigate them.
AI Security Controls
- This module explores essential strategies for securing AI systems, including conducting model risk assessments, implementing guardrails, and using prompt templates. Learners will gain practical knowledge on testing and validating security measures to ensure ethical and reliable AI operations.
AI Access Controls
- This module explores various security measures for AI systems, including prompt firewalls, access controls, rate limiting, and network protections. Learners will gain an understanding of how to implement and manage these controls to enhance AI security. The content provides practical insights into safeguarding AI models and their data.
Encryption and Data Safety
- This module covers essential techniques for protecting sensitive information in AI systems, including encryption, data classification, minimization, redaction, masking, and anonymization. Learners will gain practical knowledge on how to secure data while maintaining privacy and compliance.
AI Monitoring and Auditing
- This module covers essential practices for monitoring and auditing AI systems, including security, accuracy, bias, and compliance. Learners will gain skills in detecting issues, managing costs, and ensuring ethical and effective AI operations.
Domain 3: AI-assisted Security
- This module introduces the role of artificial intelligence in modern cybersecurity, covering how AI enhances threat detection, analysis, and response strategies. Learners will explore key applications of AI in security frameworks and understand its impact on organizational protection. The content provides practical insights into leveraging AI tools for improved security outcomes.
AI-Assisted Security Tools
- This module explores how artificial intelligence enhances security tools across various platforms, including IDEs, browsers, CLI interfaces, chatbots, and MCP servers. Learners will gain an understanding of AI integration in coding environments and security practices, as well as how AI improves productivity and incident response.
AI Security Use Cases
- This module explores the application of artificial intelligence in various aspects of cybersecurity, including threat detection, secure coding, penetration testing, incident response, and language-driven security operations. Learners will gain insights into how AI enhances security practices and supports efficient cyber operations. The module emphasizes practical use cases and real-world implementations.
AI-Enabled Attacks
- This module explores the use of artificial intelligence in modern cyberattacks, covering techniques such as deepfakes, adversarial networks, reconnaissance, social engineering, and automated attack generation. Learners will gain insight into how AI is leveraged in offensive security and the tools used to detect and mitigate these threats.
Automating Security Tasks
- This module explores how artificial intelligence is used to streamline and enhance various cybersecurity and IT operations. Learners will gain insights into automating tasks such as document synthesis, incident response, and change management using AI tools. The module also covers the integration of AI in CI/CD pipelines to improve security practices.
Domain 4: AI Governance, Risk, and Compliance
- This module explores the essential concepts of AI governance, risk management, and compliance. Learners will gain insight into ethical and legal frameworks that ensure responsible AI use in organizations. It provides the foundational knowledge needed to navigate AI-related challenges in real-world scenarios.
AI Governance
- This module explores the essential elements of AI governance, including the importance of governance, organizational structures, policy frameworks, and team roles. Learners will gain an understanding of how to implement effective governance strategies to ensure the safe and responsible deployment of AI systems.
AI Risks
- This module explores the key risks associated with artificial intelligence, including bias, data leakage, reputational damage, and intellectual property concerns. Learners will gain an understanding of how to identify, assess, and mitigate these risks in real-world AI systems. The module also emphasizes the importance of responsible AI practices and organizational accountability.
AI Compliance
- This module explores key regulatory frameworks and standards for AI compliance, equipping learners with the knowledge to navigate global AI governance requirements, manage risks, and implement responsible AI practices in organizations.
What's Next?
- This module guides learners through essential strategies for preparing for the SecAI+ certification exam, focusing on effective study techniques and exam readiness. It equips students with the knowledge to structure their review and approach the test confidently.