Internal IT Auditor, Senior

The Senior Internal IT Auditor leads and executes audit engagements, serving as a subject matter resource and ensuring high-quality audit delivery across technology and cybersecurity domains.

• Requires a Bachelor’s degree or High School Diploma/GED and 4 years of additional relevant experience in lieu of a degree
• Requires a minimum of 5 years of prior relevant experience
• Basic competence and knowledge with support from others of: Financial Accounting and Finance Concepts, Managerial Accounting, Regulatory, Legal and Economics, Quality Framework, Ethics and Fraud, Information Technology, Governance, Risk and Controls, Organizational Theory and Behavior
• Working knowledge of AI tools, models, and platforms (e.g., generative AI, ML systems), including associated risks, controls, and governance consideration
• Strong analytical and problem-solving skills
• Typically, requires advanced knowledge of auditing typically obtained through advanced education combined with experience. May have practical knowledge of project management
• Familiarity with frameworks such as COBIT, NIST, ISO, or HIPAA
• Certified Information Systems Auditor (CISA), Advanced in AI Audit (AAIA), Certified Information Systems Security Professional (CISSP), Certified Internal Auditor (CIA) and/or Certified Secure Software Lifecycle Professional (CSSLP) desired
• Experience in software development, including SDLC methodologies (Agile, DevOps, CI/CD) and modern engineering practices
• Familiarity with secure coding practices, code management tools (e.g., Git-based repositories), and application security controls
• Understanding of data governance and data protection principles, including privacy, confidentiality, regulatory compliance, and intellectual property management
• Experience auditing or implementing AI governance frameworks (e.g., NIST AI RMF, internal AI governance programs)
• Experience with AI/ML model risk management, including validation, bias detection, explainability, and monitoring
• Familiarity with secure AI development practices and emerging AI risk domains (prompt injection, data poisoning, model leakage, etc.)
• Experience in the management, governance, and control of Artificial Intelligence (AI) use
• Experience in regulated environments (e.g., healthcare)