Proven skills, education, and/or applicable certifications:
AI Governance, Risk, and Compliance (GRC) Regulatory Frameworks: Deep familiarity with the EU AI Act, NIST AI Risk Management Framework (RMF), Canadian AI & Data Act (AIDA), and ISO/IEC 42001
AI Ethics & Bias Mitigation: Knowledge of algorithmic fairness metrics (e.g., disparate impact, equalized odds) and tools for detecting bias in datasets and model output
Risk Assessment Methodologies: Ability to design risk classifications for AI use cases
Generative AI & Large Language Model (LLM) Security: OWASP Top 10 for LLMs, Hallucination Management, RAG Architectures,
Agentic AI: Understanding of risks and guardrails
Data Governance & Cybersecurity: Technical knowledge of GDPR, CCPA, and PII masking/anonymization techniques within AI pipelines
Cybersecurity for AI: Understanding of model inversion attacks, adversarial robustness, and secure API management
Infrastructure Security: Knowledge of cloud security (Azure, AWS, or GCP) and how to audit “Black Box” third-party AI vendors
MLOps & Model Monitoring: Model Observability, Explainable AI, and AI Lifecycle Management