AI Foundations & Risk for IT Professionals Training

AI Foundations & Risk for IT Professionals

• Why AI Is an IT Concern
  • How AI has shifted from experimentation to infrastructure
  • Differences between consumer AI tools and enterprise AI systems
  • Why AI adoption often bypasses IT governance
  • The evolving role of IT in AI-enabled organizations
• How Modern AI Systems Behave
  • Overview of machine learning and large language models
  • Training vs inference in operational systems
  • Probabilistic outputs and non-deterministic behavior
  • Why AI systems behave differently than traditional software
• Understanding AI Failure Modes
  • Hallucinations and fabricated outputs
  • Inconsistent behavior across similar inputs
  • Overconfidence and false authority
  • Data leakage and context misuse
• AI Risk Categories in Enterprise Environments
  • Operational risk (reliability, supportability, maintenance)
  • Security risk (data exposure, prompt injection, misuse)
  • Compliance and regulatory risk
  • Reputational and trust risk
• Security and Privacy Considerations
  • What AI systems can “see” and access
  • Data boundaries, permissions, and exposure
  • Risks of external AI services
  • Managing sensitive, proprietary, and regulated data
• Human Accountability and Governance
  • Why AI does not eliminate responsibility
  • Decision authority vs decision support
  • Human-in-the-loop principles
  • Auditability and traceability
• Evaluating AI Use Cases
  • Identifying appropriate AI applications
  • Red flags for high-risk use cases
  • When AI adds value—and when it adds risk
  • Aligning AI use with organizational maturity
• Preparing IT for AI Adoption
  • Establishing shared language and expectations
  • Defining roles and responsibilities
  • Setting guardrails without blocking innovation
  • Coordinating across IT, security, legal, and business teams
• Foundations for Responsible AI Systems
  • Principles for safe and maintainable AI use
  • Early governance decisions that matter later
  • Preparing for workflow-based and agentic AI systems
  • Setting the stage for implementation and architecture training
• Summary and Next Steps
  • Key risk and behavior insights
  • Applying foundations in IT decision-making
  • Transitioning to AI workflow and architecture design
  • Preparing for hands-on implementation courses

Before Taking this Class

General technical literacy and familiarity with IT systems and enterprise environments. No prior AI implementation experience is required.

Software/Setup For this Class

A laptop or desktop computer with a modern web browser and reliable internet access is recommended for accessing course materials and participating in discussions. No programming tools or AI software access is required.