This course introduces the core concepts of AI and data as they apply to cybersecurity, including how AI models are trained and prompted. It covers how to protect AI data and systems through threat modeling, security controls, access controls, and ongoing monitoring and auditing. Learners examine AI-related threats and attacks and how to apply compensating controls, and they see how AI can support security operations and automate security tasks while also being misused by attackers. The course closes with AI governance, risk, and compliance, including the organizational structures, policies, and regulations that guide how AI is used and developed.
The CY0-001 exam is sold separately for S$418 per voucher
| Exam Version | V1 | Exam Series Code | CY0-001 |
| Launch Date | February 17, 2026 | ||
| Exam Duration | 60 minutes | ||
| Number of Questions | Maximum of 60, multiple-choice and performance-based | ||
| Passing Score | 600 (on a scale of 100-900) | ||
| Languages | English | ||
| Retirement | Estimated 3 years after launch | ||
This course is designed for:
- Cybersecurity professionals who want to understand how AI affects their work
- Security analysts and SOC team members who use or plan to use AI-enabled tools
- IT professionals responsible for deploying, managing, or securing AI systems
- Risk, governance, and compliance professionals who oversee AI use in their organizations
- Anyone preparing to secure AI systems or defend against AI-enabled threats
By the end of the course, participants should be able to:
- Explain the core concepts of AI and how models are trained and prompted
- Protect AI data and systems through threat modeling and security controls
- Apply access controls, data security controls, and ongoing monitoring and auditing for AI systems
- Identify attacks on AI systems and apply compensating controls
- Use AI to support and automate security tasks
- Recognize how attackers misuse AI
- Understand the governance structures, risks, and compliance requirements that shape how organizations use and develop AI
1.0 Summarizing AI and Data Concepts for Cybersecurity
1.1 Explain AI Concepts for Cybersecurity
- Topic 1.1.1 Core AI Types
- Topic 1.1.2 Types of AI
- Topic 1.1.3 Generative AI
- Topic 1.1.4 Generative AI
- Topic 1.1.5 Machine Learning and Statistical Learning
- Topic 1.1.6 Detect Suspicious Activity using ML
- Topic 1.1.7 Transformers
- Topic 1.1.8 Deep Learning
- Topic 1.1.9 Natural Language Processing
1.2 Understand AI Model Training and Prompt Engineering
- Topic 1.2.1 AI Model Training
- Topic 1.2.2 Supervised Learning
- Topic 1.2.3 Unsupervised Learning
- Topic 1.2.4 Reinforcement Learning
- Topic 1.2.5 Federated Learning
- Topic 1.2.6 Model Training Techniques
- Topic 1.2.7 Introduction to Prompt Engineering
- Topic 1.2.8 System Roles and System Prompts
- Topic 1.2.9 System Roles and Prompts
- Topic 1.2.10 User Prompts
- Topic 1.2.11 Zero-Shot, One-Shot, Multi-Shot, and Templates
- Topic 1.2.12 Securing the Model
1.3 Secure AI Data
- Topic 1.3.1 Data Security Related to AI
- Topic 1.3.2 Data Security Considerations for AI
- Topic 1.3.3 AI Data Types
- Topic 1.3.4 Data Handling Techniques
2.0 Implementing Threat Modeling and Securing AI Systems
2.1 Use AI Threat Modeling
- Topic 2.1.1 Introduction to AI Threat Modeling
- Topic 2.1.2 AI Threat Modeling
- Topic 2.1.3 Utilizing AI Threat Resources
- Topic 2.1.4 Prerequisites for Performing AI Threat Modeling
- Topic 2.1.5 Process of AI Threat Modeling
- Topic 2.1.6 AI Threat Modeling Frameworks
2.2 Implement Security Controls for AI Systems
- Topic 2.2.1 Overview of AI Security Controls
- Topic 2.2.2 Model Specific Controls
- Topic 2.2.3 Model Guardrails
- Topic 2.2.4 Prompt Template
- Topic 2.2.5 Gateway and Interface Controls
- Topic 2.2.6 Gateway Controls and Guardrails
- Topic 2.2.7 Usage and Quota Limitations
- Topic 2.2.8 Testing Security Controls
3.0 Installing Access Controls for AI
3.1 Deploy Access Controls for AI
- Topic 3.1.1 Access Control Principles for AI
- Topic 3.1.2 AI Access Control Models
- Topic 3.1.3 Threat Landscape of AI Systems
- Topic 3.1.4 Model Access
- Topic 3.1.5 Access Control for AI Systems
- Topic 3.1.6 Data and Agent Access
- Topic 3.1.7 Network and API Access
3.2 Apply Data Security Controls for AI Security
- Topic 3.2.1 Overview of AI Data Security Controls
- Topic 3.2.2 Encryption of AI Data
- Topic 3.2.3 Data Safety Measures
3.3 Perform Monitoring and Auditing for AI Systems
- Topic 3.3.1 Prompt and Log Monitoring
- Topic 3.3.2 Performance and Cost Monitoring
- Topic 3.3.3 AI Cost Monitoring
- Topic 3.3.4 Quality and Compliance Auditing
4.0 Distinguishing AI-Related Threats and Compensating Controls
4.1 Demonstrate the Importance of Security in the AI Life Cycle
- Topic 4.1.1 Exploring the AI Life Cycle
- Topic 4.1.2 Data Security Considerations
- Topic 4.1.3 AI Life Cycle Security Considerations
- Topic 4.1.4 The Human Role in AI Security
- Topic 4.1.5 Ethical Considerations in AI Design
4.2 Analyze AI System Attacks and Utilize Compensating Controls
- Topic 4.2.1 Analyzing AI Attacks
- Topic 4.2.2 Backdoor and Trojan Attacks
- Topic 4.2.3 Model and Data Poisoning
- Topic 4.2.4 Model Inversion and Model Theft
- Topic 4.2.5 AI Attacks Analysis and Controls
- Topic 4.2.6 Applying Compensating Controls
5.0 Leveraging AI in Security and Understanding Its Misuse
5.1 Use AI-Enabled Tools for Security Tasks
- Topic 5.1.1 AI Tools in Security Operations
- Topic 5.1.2 AI Use Cases: Detection and Analysis
- Topic 5.1.3 AI Security Use Cases
- Topic 5.1.4 AI Use Cases: Testing and Management
- Topic 5.1.5 AI and Incident Management
5.2 Summarize AI-Enabled and AI-Enhanced Attack Vectors
- Topic 5.2.1 AI for Deception and Social Engineering
- Topic 5.2.2 AI for Reconnaissance and Data Correlation
- Topic 5.2.3 AI for Automated Attacks
- Topic 5.2.4 AI Attack Vectors
5.3 Use AI to Automate Security Tasks
- Topic 5.3.1 AI for Security Scripting and Content Summarization
- Topic 5.3.2 AI in Security Workflows
- Topic 5.3.3 Automate Security Tasks
- Topic 5.3.4 AI in DevSecOps
6.0 Understanding AI Governance, Risk, and Compliance
6.1 Classify Organizational Governance Structures for AI
- Topic 6.1.1 Establish AI Governance
- Topic 6.1.2 Important Roles in AI
- Topic 6.1.3 AI Governance
6.2 Define the Risks Associated with AI
- Topic 6.2.1 Principles of Responsible AI
- Topic 6.2.2 Identifying Risks Unique to AI
- Topic 6.2.3 Putting Principles into Practice
- Topic 6.2.4 Common AI Risks
- Topic 6.2.5 Discuss Risks with AI
6.3 Explain the Impact of Compliance on Business Use and Development of AI
- Topic 6.3.1 Common Themes in AI Regulation
- Topic 6.3.2 Important AI Compliance Frameworks
- Topic 6.3.3 Organizational AI Policies
- Topic 6.3.4 External Compliance Impacts
