Fundamentals of AI

SecAI Domain


1.0: Basic AI Concepts Related to Cybersecurity
4.0: AI Governance, Risk, and Compliance

SecAI+ Objectives

1.1: Compare and contrast various AI types and techniques used in cybersecurity.

1.2: Explain the importance of data security in relation to AI.

1.3: Explain the importance of security throughout the life cycle of AI.

4.2: Explain risks associated with AI.

 

Overview

Fundamentals of AI Lab is a hands-on training course that introduces the essential capabilities and responsible applications of artificial intelligence in today’s workplace. This immersive lab guides you through using AI tools, exploring ethical scenarios, and analyzing real-world tasks that require critical thinking. Over the two-hour session, you will participate in guided exercises, interactive demonstrations, and practical workplace simulations. By completing this course, you will develop the foundational skills needed to work efficiently, assess AI outputs for bias and accuracy, and integrate AI workflows into your daily operations.

Learning Objectives

  • Identify the capabilities and limitations of AI tools.

  • Apply ethical standards to professional AI use.

  • Evaluate AI-generated outputs for accuracy and bias.

  • Integrate AI workflows into business processes.

  • Develop strategies for responsible and sustainable AI adoption.

Courses

Key terms and descriptions

Artificial Intelligence (AI)
The simulation of human intelligence processes by machines, especially computer systems, to perform tasks such as learning, reasoning, and self-correction.
Machine Learning (ML)
A subset of AI that enables systems to learn and improve from experience without explicit programming, using algorithms to analyze data and identify patterns.
Neural Network
A computational model inspired by the human brain, consisting of interconnected nodes ('neurons') that process information in layers to recognize relationships in data.
Algorithm
A step-by-step procedure or set of rules used by computers to perform tasks, solve problems, or make decisions.
Natural Language Processing (NLP)
A branch of AI focused on enabling computers to understand, interpret, and respond to human language in a meaningful way.
Bias
Unfair or prejudiced outcomes produced by AI systems caused by incomplete, inaccurate, or imbalanced training data, leading to systematic errors.
Ethics in AI
Guidelines and principles for designing, implementing, and using AI responsibly, ensuring fairness, transparency, privacy, and accountability.
Automation
The use of technology, including AI, to perform tasks automatically, reducing the need for human intervention and improving efficiency.
AI Workflow
The series of steps that outline how AI systems are implemented and integrated into business processes, from data input to decision-making and output.
Responsible AI Adoption
Strategies and practices to deploy AI in ways that are sustainable and ethical, considering long-term impacts on society, business, and individuals.