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Get the lowdown on using AI safely with practical tips to keep your projects responsible and on point.
This half-day course introduces the practical foundations of Safe AI: using, developing, deploying, and overseeing AI systems in ways that reduce avoidable harm while preserving their usefulness. Participants will examine common AI risks, including inaccurate outputs, inappropriate disclosure of sensitive information, bias, security threats, unsafe automation, excessive reliance on AI, and poorly controlled agentic behavior.

Learning Objectives:
After completing this course, participants will be able to:

  • Explain what “Safe AI” means in an organizational context.
  • Recognize that AI risk depends on the task, information, capabilities, users, and consequences, not simply on which model is being used.
  • Identify common risks associated with generative and agentic AI.
  • Recognize hallucinations, misleading outputs, and inappropriate levels of confidence.
  • Apply practical techniques for verifying AI-generated information.
  • Protect confidential, personal, proprietary, and otherwise sensitive information when using AI.
  • Recognize common security concerns involving AI systems and AI-enabled workflows.
  • Understand bias, fairness, and other risks associated with using AI-supported recommendations or decisions.
  • Distinguish between low-risk AI assistance and higher-risk AI actions or decisions.
  • Apply appropriate human oversight to AI-assisted work.
  • Understand the additional risks introduced when AI systems can access tools, files, applications, and external systems.
  • Use a simple risk-based framework to determine appropriate safeguards before using or deploying AI.

Curriculum:

  1. What Does “Safe AI” Mean?
  • What Safe AI means in practical terms
  • AI safety versus AI security versus responsible AI
  • Why AI safety concerns have increased
  • Generative AI, predictive AI, and agentic AI
  • The difference between AI producing information and AI taking actions
  • Safety as a shared responsibility
  • Different responsibilities for:
  • Everyday AI users
  • Managers and process owners
  • Developers and technical teams
  • Security, privacy, legal, compliance, and governance functions

2. Understanding AI Risk
Rather than treating all AI use as equally risky, participants learn to evaluate the context.

  1. What is the AI being asked to do?
  2. What information does it have access to?
  3. What can it do?
  4. Who will rely on the result?
  5. What happens if it is wrong?
  6. Hallucinations, Accuracy, and Reliability
  • What an AI hallucination is
  • Why fluent responses are not necessarily correct
  • Fabricated facts, citations, sources, or calculations
  • Missing context and incorrect assumptions
  • Out-of-date information
  • Confidence versus correctness
  • Why AI can produce different answers to similar questions
  • Risks of summarizing incomplete source material
  • Risks of relying on AI-generated analysis without verification
  • 3. Data Privacy and Confidential Information
  • Public versus internal versus confidential information
  • Personal and sensitive personal information
  • Customer and employee information
  • Intellectual property and trade secrets
  • Credentials, passwords, and security information
  • Contracts, legal material, and regulated information
  • Uploaded files and attachments
  • Connected applications and repositories
  • Organizational policies governing approved AI systems
  • 4. AI Security and Adversarial Risks
  • AI systems as a new organizational attack surface
  • Malicious or manipulated inputs
  • Prompt injection
  • Indirect prompt injection from documents, websites, and other sources
  • Malicious instructions embedded in content
  • Social engineering using AI
  • AI-generated phishing and impersonation
  • Unsafe code or commands generated by AI
  • Excessive permissions
  • Connecting AI systems to organizational tools
  • 5. Bias, Fairness, and AI-Supported Decisions
  • Where bias can enter AI systems
  • Bias in data
  • Bias in prompts and task definitions
  • Bias in model outputs
  • Automation bias in human reviewers
  • Why AI recommendations may appear objective even when they are not
  • Representativeness and missing populations
  • High-impact decisions
  • Appropriate human review
  • Documenting decision criteria
  • Testing AI systems across relevant scenarios
  • 6. Safe Use of AI Agents and Automation

The risks change when an AI system can act, not merely answer.
Agentic risk factors

  • Access to files
  • Access to email and messaging
  • Database access
  • Ability to modify records
  • Ability to execute code
  • Ability to make purchases
  • External communications
  • Repeated or automated execution
  • 7. Human Oversight and Avoiding Over-Reliance
  • AI as an assistant rather than an unquestioned authority
  • Confirmation bias
  • Loss of human expertise through excessive delegation
  • When human judgment matters most
  • Reviewing outputs versus merely approving them
  • Appropriate escalation
  • When subject-matter expertise is required
  • Maintaining accountability

Training material provided: Yes (Digital format)

Related topics

Software Development

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