Length: 2 Days
The Certified AI Security Control Assessor (CASCA™) certification is designed to certify professionals in assessing, auditing, and improving the security controls of AI systems. This certification aims to address the unique security challenges posed by AI technologies and ensure these systems are robust against threats.
Objectives:
- To provide a deep understanding of the security risks associated with AI systems and how to mitigate them.
- To equip professionals with the skills to conduct comprehensive security assessments of AI systems.
- To promote best practices in AI security, ensuring compliance with industry standards and regulations.
- To enhance the ability to communicate AI security risks and recommendations to stakeholders.
Target Audience:
- IT security auditors and assessors specializing in AI systems.
- Cybersecurity professionals aiming to specialize in AI security.
- AI developers and engineers focused on building secure AI solutions.
- Risk management professionals in organizations utilizing AI technologies.
Certification Modules:
Module 1: Foundations of AI Security
- Introduction to AI technologies and their security implications.
- Overview of common AI vulnerabilities and attack vectors.
Module 2: AI Security Control Frameworks
- Standards and best practices for AI security controls.
- Frameworks for assessing AI security, such as NIST and ISO guidelines.
Module 3: Assessing AI System Security
- Techniques and tools for conducting security assessments of AI systems.
- Evaluating the effectiveness of security controls in AI environments.
Module 4: Risk Management in AI
- Identifying and analyzing risks in AI systems.
- Strategies for risk mitigation and management in AI deployments.
Module 5: Compliance and Legal Considerations in AI Security
- Regulations and compliance requirements related to AI security (e.g., GDPR, CCPA).
- Legal implications of AI security breaches and control failures.
Module 6: Ethical Considerations in AI Security Assessment
- Ethical challenges in AI security assessments.
- Balancing security, privacy, and ethical considerations in AI systems.
Module 7: Practical Applications and Case Studies
- Hands-on exercises and simulations in assessing AI security.
- Case studies of AI security assessments and lessons learned.
Module 8: Certification Exam Preparation
- Comprehensive review of AI security assessment principles.
- Mock assessments and exam preparation exercises.
Exam Domains:
- Principles of AI Security
- AI Risk Assessment and Management
- Compliance and Regulatory Frameworks for AI
- AI Security Control Implementation and Testing
- Reporting and Communicating AI Security Findings