AI Agent Security in Practice: From Prompt Injection to Privilege Misuse

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

AI agents can fail due to various reasons, including data errors, security threats, increasing costs, and system integration issues. This course examines major obstacles and failure cases that occur during the AI agent implementation process and explains practical strategies for design, inspection, and operation to prevent them. Through this, you can understand the risks and limitations of AI agents and establish appropriate implementation directions and stable operational standards for your organization.

As the digital transformation of the manufacturing industry accelerates, production facilities, control systems, and industrial networks are becoming increasingly connected to external systems. Along with this, the possibility of cyberattacks leading to production shutdowns, quality degradation, technology leaks, and safety accidents is also growing. The [OT (Manufacturing) Security] Master Class for 4 Major Manufacturing Industries is a course that examines OT security threats that can occur in the automotive, semiconductor, chemical, and food/pharmaceutical industries, focusing on real-world manufacturing site cases. Participants will understand how production processes and facility environments differ across each industry and systematically learn about key security threats, causes of accidents, and response strategies specific to each sector. The course covers a wide range of topics in a single program, including smart factory and supply chain security in the automotive industry, protection of core processes and technical information in the semiconductor industry, control systems and safety accident response in the chemical industry, and quality, hygiene, and GMP data protection in the food and pharmaceutical industries. Rather than simply listing complex technologies or attack techniques, the course explains "what problems can occur," "why they occur," and "how to respond on-site" in a way that is easy for even beginners to understand. Through this lecture, you can compare the characteristics of the four major manufacturing industries and the differences in OT security, and learn the basic direction of security inspections and accident response required at manufacturing sites.

We will examine the major OT security threats that can occur at production sites in the food and pharmaceutical industries, focusing on real-world cases. You will understand what security issues can arise in smart factories, production facilities, quality control systems, and traceability management processes, and learn basic response methods to prevent production shutdowns and data falsification in a way that is easy for beginners to follow.

This course examines major security threats and incident cases that can occur in the process control environment of the chemical industry, providing an easy-to-understand approach to AI-based anomaly detection and response. Participants will learn practical OT security, focusing on risks unique to the chemical industry such as production shutdowns, manipulation of process values, and hazardous substance leaks.

The semiconductor industry is a representative manufacturing sector where production facilities, control systems, partners, and data are closely interconnected. For this reason, even a small security incident or system failure can lead to production shutdowns, quality issues, delivery delays, and technology leaks. This lecture explains the OT security threats that can occur at semiconductor manufacturing sites in a way that even beginners can easily understand. Rather than focusing on complex technical explanations, it centers on what security problems can arise in the actual field, why the semiconductor industry is a target for attacks, and how security incidents impact companies. Furthermore, it examines how security threats are evolving as manufacturing environments become more intelligent with the spread of AI and smart factories.

In the era of smart factories and autonomous driving, is our production line truly safe? The remarkable advancement of Artificial Intelligence (AI) has brought innovative productivity and efficiency to the automotive manufacturing industry, but it is simultaneously creating unprecedented security threats to the Operational Technology (OT) environment within factories. This lecture breaks down "OT Security"—which can often feel vague and difficult—in a clear and easy-to-understand manner through vivid, real-world examples from the automotive industry.

"Throw away the cliché AI theories and chatbot examples scattered all over YouTube. This is Korea's one and only '7 Major Industry Integrated Manufacturing AI Bible' that understands the language of manufacturing and pierces through the essence of the process." Hello, I am Byte Detective, your knowledge sharer. Currently, countless companies and practitioners are calling for 'Manufacturing DX (Digital Transformation)' and 'AI Adoption.' However, the majority of courses on the market are limited to vague theories from IT developers or consultants who have no understanding of the harsh practical realities of the manufacturing field. This course is not a lecture for the sake of theory. By organically combining the actual domain knowledge of Korea's top 7 representative manufacturing industries with the latest AI frameworks, it provides a 'Practical Manufacturing DX Scenario Architecture' that can be applied to the field starting tomorrow.

[Introduction to Security Incident Response: Learning through Scenarios of Web Server Hacking, Ransomware, and Account Takeover] This course examines why various information security incidents—such as web server hacking, ransomware, account takeover, and internal penetration—occur and how they spread, categorized by incident type. Students will follow each incident scenario to understand the attack flow, analyze the core causes of the incident, and learn how to resolve issues and prevent recurrence through security architectures such as firewalls, DMZ, access control, privilege management, backup, and log monitoring. Rather than a simple explanation of security concepts, this is a scenario-based introduction to information security that provides a comprehensive understanding from root cause analysis to resolution strategies.

"Understanding Manufacturing Security in the AI Era through Field Cases" is a course designed to provide an easy understanding of OT security, which is growing in importance within smart factories and AI-based manufacturing environments, focusing on real-world field cases. This lecture explains core concepts frequently encountered in manufacturing sites, such as OT, ICS, PLC, SCADA, HMI, and MES, and compares the differences between IT security and OT security. Furthermore, it examines security threats that can occur on the manufacturing floor—such as ransomware, remote access exploitation, partner account hijacking, production line shutdowns, equipment malfunctions, and data forgery—through case studies. Through this course, students will understand the basic structure of OT security in manufacturing and identify the security perspectives necessary to protect production facilities and industrial control systems. Additionally, it provides a common foundation for further learning in industry-specific manufacturing security courses.

Beyond the era of simply using AI, we are now in an era that requires people who can design AI. This course is a master roadmap that systematically organizes the entire design map of AI agents, from "Introduction → Structure → Design → Operation." Based on RAG, Tool Calling, Memory, and Multi-Agent structures, you will learn how to design and scale AI agents from an architect's perspective. If you want to grow into someone who designs AI systems rather than just someone who "uses" AI, this course will provide the direction.

This course is a specialized program that covers practical application strategies for utilizing artificial intelligence (AI) in the construction and civil engineering fields. In the construction industry where digital transformation is accelerating, AI is driving innovation across design, construction, safety, and quality management. This course focuses on learning practical application cases that can be utilized in actual field sites, including smart construction, digital twins, AI-based equipment control, safety monitoring, and quality verification.

The shipbuilding and maritime industry is a representative field of traditional heavy industry, but it is securing new competitiveness through the combination of digital transformation and artificial intelligence (AI) technology. This course is structured around specific application cases actually used in the field, such as AI-based shipyard production management, ship design automation, maritime logistics optimization, and predictive maintenance system construction. With a field-centered approach, you can understand not just simple theory but **'how AI actually works in shipyards'**. Additionally, it covers data infrastructure construction, model training, and constraints specific to the marine environment that must be considered during the AI technology implementation phase.

"Smart Factory—it sounds cool, but can't quite picture it in your head?" The news talks about the 4th Industrial Revolution and AI factories every day, but when you actually look for information, it's full of "alien languages" like PLC, MES, ERP, and Edge Computing. What non-majors need isn't a complex circuit diagram, but a 'map' that shows how the whole system works. This course strips away difficult engineering jargon and explains everything about smart factories through intuitive illustrations and analogies that anyone can understand.

"The Fourth Industrial Revolution, AI factories... The terms are familiar, but can't you visualize the structure?" Smart Factory, often called the future of manufacturing. While related articles pour out and it appears as a required competency in job postings, the barrier feels far too high for non-majors. "Did you give up because of abbreviations that sound like alien languages, such as PLC, MES, or ERP?" "Are you afraid to even start because you lack knowledge in coding or engineering?" Don't worry. This lecture deals with 'structure,' not 'engineering.' Instead of complex text, we will help you complete a full map of the smart factory in your head using intuitive drawings and diagrams, just like assembling Lego blocks.

This course covers practical strategies for leveraging AI in the machinery and metal processing industry to enhance productivity and quality. It analyzes key process data from CNC, welding, cutting, and other operations, explaining how AI technology is applied to reduce defect rates, enable predictive maintenance, and manage worker safety through real-world case studies. Learners will understand AI-based processing optimization methods that can be immediately implemented in the field, from small and medium enterprises to large corporations, and gain insights to realize manufacturing innovation in global competition.

This course covers how to leverage AI in the food and pharmaceutical manufacturing industry to enhance productivity and quality. Through real-world case studies, you'll learn how AI is applied in key stages such as raw material management, production processes, quality inspection, and regulatory compliance, while understanding defect rate reduction and efficient operational strategies. Through this course, learners will master ▲characteristics of the food and pharmaceutical industry and the necessity of AI adoption ▲data-driven quality management ▲predictive maintenance and equipment optimization ▲regulatory compliance monitoring and risk management ▲analysis of domestic and international implementation cases, gaining strategies and insights that can be immediately applied in the field.

The chemical and fine chemical manufacturing industry is a sector that requires complex processes and high safety standards. This course covers strategies for leveraging AI to increase productivity, reduce costs, and enhance safety. You can systematically learn practical skills centered on AI application cases that can be immediately applied in actual field settings, including process data analysis, quality prediction, equipment predictive maintenance, and environmental regulation compliance.

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

Digital transformation (DX) is not a choice but a necessity. This course serves as the first step in digital transformation for all employees, providing easy and clear explanations from core DX concepts to business innovation cases. Don't fall behind in this changing era—develop the power to grow yourself and lead organizational innovation! Please don't overthink this lecture and take it with a light heart.

As AI rapidly permeates daily life and industries, attack techniques targeting these systems are evolving alongside them. This course is a practical, hands-on program designed to help you understand the latest security threats surrounding Generative AI and LLMs, automate security tasks using AI, and experience LLM jailbreak attacks and defenses firsthand.

A lecture on practical blog operation using AI tools, condensed from 20 months of experience! It offers step-by-step utilization methods from topic selection to content creation with Perplexity, ChatGPT, and Midjourney.

This course examines major security threats and incident cases that can occur in the process control environment of the chemical industry, providing an easy-to-understand approach to AI-based anomaly detection and response. Participants will learn practical OT security, focusing on risks unique to the chemical industry such as production shutdowns, manipulation of process values, and hazardous substance leaks.

We will examine the major OT security threats that can occur at production sites in the food and pharmaceutical industries, focusing on real-world cases. You will understand what security issues can arise in smart factories, production facilities, quality control systems, and traceability management processes, and learn basic response methods to prevent production shutdowns and data falsification in a way that is easy for beginners to follow.

In the era of smart factories and autonomous driving, is our production line truly safe? The remarkable advancement of Artificial Intelligence (AI) has brought innovative productivity and efficiency to the automotive manufacturing industry, but it is simultaneously creating unprecedented security threats to the Operational Technology (OT) environment within factories. This lecture breaks down "OT Security"—which can often feel vague and difficult—in a clear and easy-to-understand manner through vivid, real-world examples from the automotive industry.

Learn context engineering techniques for creating high-quality AI agents through hands-on practice.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is currently the technology that has come closest to AGI. In this lecture, we will explore LLM and Agent technologies from a layperson's perspective.

As the adoption of generative AI accelerates, new security threats such as corporate confidential information leaks and prompt injections are surging. This course covers strategies for embedding security throughout the entire AI lifecycle, based on the National Intelligence Service (NIS) guidelines' 30 core security measures and 15 practical threat cases. It also addresses security issues in the latest AI trends, such as Agentic AI and Physical AI, and presents methods for establishing governance frameworks that can be immediately applied in practice.

This course focuses on M365 Copilot, a smart work partner in the Microsoft 365 ecosystem, and covers how to achieve work innovation by utilizing AI in an environment where security and copyright are protected. It introduces the main features of Copilot and various ways to leverage it in conjunction with Microsoft 365 services, while also presenting prompting techniques to elicit accurate and effective responses.

Sensational video content created with just a single line of text! A quick and easy introduction to SNS content creation starting with Kling AI

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

Understand Transformer-based LLM architectures and GPU utilization strategies, and gain hands-on experience with the actual serving process using vLLM. This course covers the entire practical workflow, from building AI system pipelines to monitoring and multi-GPU utilization, and is designed for intuitive understanding through diagrams and practice without complex formulas.

AI Finally Gets a Body - What is My Survival Strategy in the Age of Robots? Physical AI is not just about robots. It is an overthrow of the economic paradigm created by the grand convergence of artificial intelligence, robotics, and energy technology. This course explores various perspectives, from the background of Physical AI's emergence to the strategies of global big tech, hardware sovereignty, and future social issues such as robot taxes, basic income, and global inequality.

Feeling overwhelmed by where to start with AI auditing? AI has moved beyond a mere technology trend and is rapidly expanding into corporate decision-making, workflow automation, customer service, recruitment, security, and risk management. However, compared to the speed of AI adoption, not many people systematically understand questions like: "Is the AI being properly controlled?", "Can we trust the AI's judgment?", or "What risks could arise from the data, models, and operational processes?" In particular, those working in IT auditing, security, internal control, and project management often face these concerns: - How does AI auditing differ from traditional IT auditing? - Is it enough to just look at the code for an AI model, or must we examine the data and training process as well? - How do AI governance, risk, bias, explainability, MLOps, and incident response connect to one another? - Where should I start to understand the concepts covered in AI auditing certification courses like AAIA from a practical perspective? This course was created specifically to resolve this sense of uncertainty. For about 20 years, I have gained experience in IT service planning, development, PM, collaboration platform operation, groupware construction, chatbot service planning, and security and audit-related tasks. I have firsthand experience in how systems are planned, developed, and operated in actual organizations, and how failures, changes, security, and user requirements are interconnected. Furthermore, I have synthesized the perspectives of IT auditing and AI auditing through CISA and AAIA study programs. In this course, rather than diving deep into complex AI technology through math or development, I focus on the core concepts that auditors and IT practitioners must understand. We will examine AI systems by breaking them down into the flow of data, models, operations, security, governance, risk, and audit procedures, and summarize what controls and checkpoints are required at each stage. Through this course, students will understand that AI auditing is not simply "evaluating AI," but rather the task of inspecting governance, risk, and control systems to ensure that AI is used safely and responsibly within an organization. This course is suitable for: - Those new to AI auditing. - Those in IT auditing or security who want to expand into the AI domain. - Those who want to organize AI governance and risk from a practical perspective. - Those who want to grasp the overall concepts before starting AI audit-related studies such as AAIA. Upon completing the course, you will be able to answer the following questions yourself: - What components make up an AI system? - What should an AI auditor check during the data, model, and operation stages? - Why are AI governance and Responsible AI important? - How are AI risk, bias, explainability, privacy, and security connected? - What perspectives and evidence should be included in an AI audit report? In the rapidly changing era of AI, the ability to manage and inspect technology to make it trustworthy is just as important as the ability to use it well. This course is an introductory program for those who want to understand the big picture of AI auditing step-by-step from the beginning. Let's build the first standard for looking at IT and AI from an auditing perspective together.

The lecture explains why the harness is more important than the model through the lens of Claude Code architecture. It also covers how to design a Claude Code work system around six structures: Context Harness, Permission Harness, Verification Harness, Tool Harness, Subagent & Worktree Harness, and Debugging Harness. It then connects these concepts to CLAUDE.md, session management, controlling large codebases, parallel worktree operations, error log analysis, and tracking environment variable and configuration scope issues, helping you establish practical standards for continuing to use Claude Code in real-world backend and DevOps work.

What if you could hand over those repetitive tasks you've been stuck with all day to an AI assistant? Introducing a guide to building your own AI work assistant—not a GPT chat that requires a fresh explanation every time, but one that remembers you, works in your style, and even reports back via Slack. From installation and Slack integration to skill setup, Notion/Calendar/Email automation, and even security and cost management! Complete an AI work system you can use immediately at the office, with a step-by-step approach that anyone can follow even without coding knowledge. Point 1. Set up a "Personalized AI Assistant" that remembers you, unlike standard GPT chats - Build the basic AI assistant environment by following steps from OpenClo installation to Slack integration. - Create an assistant that works according to your professional style by setting its personality and rules. Point 2. Master practical automation patterns to reduce repetitive tasks - Connect with actual work tools like Notion, Calendar, and Email by installing skills. - Practice immediate automation for tasks such as morning briefings, writing work logs, sending emails, and managing schedules. Point 3. Complete an AI work system that can be used safely in the workplace - Review essential checkpoints for professionals, including permission management, security settings, and cost control. - Learn how to expand from a single AI assistant to a multi-agent work system.

Stop making mass-produced shorts that just repeat gacha-style content with a single click! This is an integrated introductory and practical course where you will understand the principles of how AI creates videos even if you don't know filming or editing. You will complete one YouTube video using Veo, Runway, Kling, HeyGen, and CapCut, and even create 'surviving AI shorts' that viewers watch until the end through LLM planning.

Generative AI is rewriting how the world works — and the people who learn it now will lead the next decade. Every industry is being reshaped in real time. Marketers are creating campaigns in minutes. Developers are shipping code faster than ever. Entire teams are automating work that used to take days. And at the center of it all sits one skill that separates those who get left behind from those who get ahead: knowing how to actually use Generative AI. The problem? Most people are still only scratching the surface. They've typed a question into ChatGPT, gotten an average answer, and assumed that's all there is. But beneath that simple chat box is one of the most powerful technologies ever built — and once you understand how it works and how to direct it, you'll never work the same way again. That's exactly what this course is built to do. Introduction to Generative AI takes you from complete beginner to confident, capable AI user — no coding, no maths, and no prior experience required. In clear, jargon-free language, you'll discover what Generative AI really is, how it works under the hood, and how to put it to work in your job, your business, and your everyday life. This isn't a dry, theoretical lecture series. It's a practical, demo-driven journey packed with real examples you can follow and copy immediately. You'll start with the foundations — what Artificial Intelligence, Machine Learning, and Deep Learning actually mean, and how Generative AI fits into the bigger picture. You'll explore ChatGPT's features and capabilities hands-on, understand why this technology has exploded so quickly, and trace its remarkable evolution. From there, you'll demystify the engine that powers it all: Large Language Models. You'll see exactly how next-word prediction, embeddings, and fine-tuning work, and you'll understand game-changing concepts like Retrieval Augmented Generation (RAG) and the rise of Agentic AI — the difference between an assistant that answers and one that acts. Then comes the real superpower: prompt engineering. This is where most learners go from "AI is interesting" to "AI is indispensable." You won't just learn what prompting is — you'll master more than a dozen proven techniques used by professionals every day, including chain-of-thought prompting, zero/one/few-shot prompting, tabular prompting, perspective prompting, comparative prompting, reverse prompting, the RGC framework, and many more. You'll get ready-to-use prompt starters, learn how to revise and sharpen your prompts for dramatically better results, and discover how to generate fresh ideas, marketing copy, client emails, and content at scale. By the end, you'll know how to make AI do precisely what you want — consistently. You'll see it all in action. This course is loaded with practical demonstrations across real business scenarios. Watch AI summarise meetings and capture insights with an AI notetaker, draft polished email responses in seconds, and spin up multiple marketing headline variations from a single product image. You'll explore how Generative AI is transforming software development, retail, and marketing, and you'll see real tools at work — generating working code with GitHub Copilot and creating images and video with cutting-edge tools like Amazon Nova. These aren't hypotheticals. They're workflows you can start using the moment you finish the lesson. But great power demands responsibility — and this course doesn't skip the hard parts. You'll dive into Responsible AI and learn how to spot and manage hallucinations, factual inaccuracies, bias, and fairness issues. You'll understand the genuine technical limitations of today's models and the practical workarounds that experienced users rely on. Through real examples — including safety refusals and bias-correction rewrites — you'll learn to use these tools wisely, ethically, and with confidence, the way professionals and organisations are expected to. And you'll finish looking forward, not just at today. You'll discover how Generative AI is disrupting search and reshaping the way the world finds information, explore the road toward Artificial General Intelligence (AGI), and get a clear-eyed view of the jobs, certifications, and fast-growing career opportunities this field is creating. Whether you want to future-proof your career, boost your productivity, or build something new, you'll leave knowing exactly where this technology is heading and how to ride the wave. Why learn from this course? You'll be joining a community of over 207,000+ students taught across leading platforms, backed by a 4.6 instructor rating and 13,000+ reviews. Every concept is explained simply, every technique is shown in practice, and every lesson is designed to take you one step closer to genuine confidence with AI. No fluff. No overwhelm. Just clear, practical, up-to-date learning that respects your time. This course is for you if you're a beginner curious about AI, a professional who wants to work faster and smarter, a marketer, creator, developer, entrepreneur, or simply someone who refuses to be left behind by the biggest technology shift of our generation. If you can use a web browser, you can do this course — and you can start today. The gap between people who use AI and people who master it is widening every single day. The tools are here. The opportunity is now. The only question is which side of that gap you want to be on. Enrol today and start your journey from AI curious to AI confident — one practical, powerful lesson at a time.

This video shows effective ways to link internal company data to sLM, and practical solutions for various issues from sLM adoption.

This is a practical course that uses Codex to connect business planning, website creation, marketing materials, task organization, and email automation. Even if you are not a developer, you will learn how to delegate tasks to AI and refine the results using Notion, Linear, Figma, and Google Workspace in an actual workflow sequence.