Basic Concept: Managing security risks in AI model training requires a comprehensive framework specifically designed for AI risk identification, assessment, and mitigation across the entire AI lifecycle including data collection, training, and deployment. CompTIA SecAI+ Study Guide identifies NIST AI RMF as the primary resource for AI-specific risk management.
Why A is Correct: The NIST AI Risk Management Framework is purpose-built for managing risks throughout the AI lifecycle. It provides structured guidance for identifying, assessing, and mitigating risks specific to AI systems including training data quality, model bias, data poisoning, and training pipeline vulnerabilities. Its AI-specific scope makes it the most appropriate framework for managing model training security issues.
Why B is Wrong: ISO 27001 is an information security management system standard focused on general IT security controls and risk management. It does not specifically address AI model training risks, data pipeline integrity, or ML-specific vulnerabilities.
Why C is Wrong: The OECD provides high-level AI governance principles and policy recommendations at an international level. It offers ethical and policy guidance but does not provide operational risk management guidance for securing AI model training processes.
Why D is Wrong: GDPR is a European data protection regulation focused on personal data privacy, consent, and individual rights. While relevant to training data governance, it does not address the technical security risks of model training pipelines or ML system vulnerabilities.
