Certified AI Security Control Assessor (CASCA™)

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Length: 2 Days

Certified AI Security Control Assessor (CASCA™)

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:

  1. Fundamentals of AI Security:
    • Understanding AI technologies and their security implications.
    • Recognizing common AI security threats and vulnerabilities.
    • Knowledge of AI-specific security controls and best practices.
  2. Risk Assessment and Management in AI Systems:
    • Conducting risk assessments specific to AI systems.
    • Identifying and prioritizing AI-related risks.
    • Developing risk mitigation strategies tailored to AI environments.
  3. Security Controls for AI Development and Deployment:
    • Implementing security controls throughout the AI development lifecycle.
    • Ensuring secure deployment and operation of AI systems.
    • Securing AI training data, models, and algorithms.
  4. Compliance and Regulatory Considerations for AI Security:
    • Understanding relevant regulations and compliance frameworks.
    • Ensuring AI systems adhere to legal and regulatory requirements.
    • Addressing ethical considerations in AI security assessments.

Question Types:

  • Multiple Choice: Assessing knowledge and understanding of concepts.
  • Scenario-based Questions: Presenting real-world situations to evaluate problem-solving skills.
  • True/False: Testing understanding of factual statements related to AI security.
  • Matching: Matching security controls with their appropriate use cases or descriptions.
  • Short Answer/Essay: Allowing candidates to demonstrate in-depth understanding and critical thinking.

Passing Criteria:

  • Minimum Score: Candidates must achieve a minimum passing score, typically set at 70% or higher.
  • Comprehensive Understanding: Candidates should demonstrate a comprehensive understanding of AI security concepts across all exam domains.
  • Ethical Considerations: Candidates must show awareness of ethical considerations and their importance in AI security assessments.
  • Ability to Apply Knowledge: Candidates should exhibit the ability to apply security controls and risk management strategies to various AI scenarios.