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Enterprise AI Security Practice: From Data Leakage Prevention to Governance

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.

1 learners are taking this course

Level Beginner

Course period Unlimited

security
security
agents
agents
AI
AI
security training
security training
Government-Funded Bootcamp
Government-Funded Bootcamp
security
security
agents
agents
AI
AI
security training
security training
Government-Funded Bootcamp
Government-Funded Bootcamp

What you will gain after the course

  • Customized security design for 4 types of AI adoption and establishment of 30 core security measures

  • Identification and response to 15 major AI security threats, including prompt injection and data leakage

  • Establishing Security Governance and Safe Utilization Frameworks for Agentic AI and Physical AI Environments


Generative AI Security, Information Leakage Can It Be Prevented?

In the early stages of AI adoption, you will systematically learn 30 core security measures based on the National Intelligence Service guidelines to resolve security anxieties and prevent information leakage incidents.


You are someone who actively uses generative AI like ChatGPT and Claude for work, yet remains cautious for fear that internal information might be exposed to AI services.

Are you feeling frustrated because you cannot clearly distinguish between the 15 types of AI security threats encountered in practice, such as prompt injection and data leakage, and the 30 core security measures presented in the National Intelligence Service guidelines?

You will learn practical methods for internalizing security throughout the entire AI lifecycle, from customized security designs for the four types of AI adoption to the latest security issues in Agentic AI and Physical AI.

AI security is no longer a vague anxiety; you can perfectly prepare for it through systematic learning and practical application.


Master the security design for
4 types of corporate AI adoption and
the implementation methods for 30
core practical security measures.


Transitioning from the risks of AI adoption
to a secure organization,
without worrying about data leaks,
you will grow into an AI governance expert.




By the end of this course, you will


You will be able to effectively respond to new security threats arising from the adoption of generative AI.

  • You will be able to accurately identify 15 major security threats that frequently occur when using generative AI, such as corporate confidential information leakage and prompt injection, and establish customized response strategies for each threat type. You no longer have to worry about information leakage incidents caused by AI utilization.

You can directly build the optimal security design tailored to the four types of AI adoption.

  • By selecting and applying 30 key security measures tailored to your company's AI adoption goals and methods, you will complete a framework that internalizes security throughout the entire AI lifecycle. You will be able to present a security roadmap optimized for each corporate environment.

You can deeply understand the security issues of the latest AI trends, such as Agentic AI and Physical AI, and utilize them safely.

  • You can accurately identify the unique security risks of autonomous Agentic AI and real-world-connected Physical AI, and present governance and utilization strategies for their safe operation. Preemptively prepare for the potential risks of future AI technologies.

You can lead the establishment of AI governance within the enterprise and the cultivation of a safe AI culture.

  • You will learn how to establish practical AI governance policies based on National Intelligence Service (NIS) guidelines and foster a culture of safe AI usage by raising the security awareness of organizational members. You can grow into a security expert leading the AI era.






✔️

Strengthening core practical capabilities to take charge of corporate security in the era of generative AI

AI Era, Corporate Security Practice:
From Data Leakage Prevention to Governance

This course provides in-depth learning on practical strategies to counter emerging security threats such as information leakage and prompt injection caused by the introduction of generative AI, based on 30 core security measures from NIS guidelines and 15 practical threat cases. It covers security embedding throughout the AI lifecycle and security issues in the latest trends, such as Agentic AI and Physical AI, while presenting methods for establishing governance frameworks that can be immediately applied in the field.

Practical AI Security Threat Identification and Response

We analyze 15 major AI security threat cases, such as prompt injection and data leakage, alongside customized security designs for four types of corporate AI adoption, and learn practical response strategies for each threat. Furthermore, through hands-on exercises in building security governance and safe utilization systems for Agentic AI and Physical AI environments, participants will develop capabilities that can be immediately applied in the field.

Core Measures and Guidelines for AI Security Internalization

You will systematically acquire 30 core security measures based on the National Intelligence Service (NIS) guidelines and learn how to apply the 'Security by Design' principle, a security embedding strategy for each stage of the AI lifecycle. Through this, we provide practical guidance for establishing AI security governance policies optimized for corporate environments and fostering a security culture within the organization.


📚

AI Security Practical Strategy:
From Information Leakage to Governance

Section 1

Analysis of AI Security Threat Cases and Countermeasures Based on NIS Guidelines

We conduct an in-depth analysis of real-world AI security incidents and study 30 core security measures based on the National Intelligence Service (NIS) guidelines. Furthermore, we provide customized security designs for different implementation types and practical methods for the safe use of commercial AI.


Section 2

Latest AI Trends and Establishing Corporate Security Governance

We will cover security issues related to the latest AI technology trends, such as Agentic AI and Physical AI, and learn how to establish security governance and safe utilization systems in these environments. Finally, we aim to spread a practical security culture through the establishment of in-house AI governance and implementation tasks.


We can solve the concerns
of people like this!

📌

Corporate Security Managers
Those who have directly experienced or received reports of accidental internal information leaks following the introduction of generative AI tools.


📌

Compliance Officers
Those tasked with managing the risk of corporate confidential information leakage caused by AI adoption, and establishing and implementing an AI governance framework within the organization, including 30 core security measures based on National Intelligence Service guidelines.


📌

Information Security Managers
Those who are in a situation where they must prepare practical response measures against 15 major security threats, such as prompt injection and data leakage, that may occur while utilizing generative AI like ChatGPT for work.




security, agents, devsecops, Midjourney, prompt engineering, AI security


Notes before taking the course


Practice Environment

  • Operating System: Universal operating systems such as Windows, macOS, Linux, etc.

  • Required Tools: Web browser (Chrome recommended), PDF viewer

  • PC Specifications: No special high-end requirements

Prerequisite Knowledge and Important Notes

  • Security personnel currently reviewing or operating AI implementation

  • Those with experience using generative AI, such as ChatGPT, for work tasks

  • It is even better if you have experience related to establishing AI governance.

Learning Materials

  • Lecture slide PDF (provided separately)

  • Security measures documentation based on practical threat cases and National Intelligence Service (NIS) guidelines

  • Security Internalization Checklist for Each Stage of the AI Lifecycle


Recommended for
these people

Who is this course right for?

  • Security officers and information security managers who are considering or currently operating AI implementation within their organizations.

  • Practitioners who need to manage security risks while utilizing generative AI, such as ChatGPT, for work.

  • Executives and compliance officers responsible for establishing AI governance policies and embedding a security culture within the organization

Need to know before starting?

  • Understanding of basic information security concepts (Confidentiality, Integrity, Availability, etc.)

  • Experience using or basic understanding of generative AI services such as ChatGPT and Claude

  • Practical experience in corporate IT systems and data management processes

Hello
This is Slearnic AI LAB

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As the CEO of 'Slurnic,' a startup maximizing the value of digital labor through AI, he develops and provides solutions that analyze early-stage startup business development and organizational tasks at the workflow level and transform them into AI-based processes. Additionally, as the co-founder of 'Slurners,' a routine management solution company for individuals with borderline intellectual functioning, he leads product and technology strategy while building practical support systems to help design daily life.


He conducts AI lectures across various educational platforms and industrial sites, including Multi Campus, Inflearn, Crowd Academy, Fast Campus, and Class101. In particular, his practical AI education capabilities have been proven at Inflearn, where he received a 2025 Award and surpassed 9,000 cumulative students. Based on his actual entrepreneurial experience, he also serves as a business development mentor at Ablen, supporting early-stage teams with market entry strategies, productization, and sales/partnership design. Ultimately, he aims to enhance both corporate productivity and individual capabilities through the practical application of AI, contributing to the productivity of society as a whole and, on a larger scale, the Gross Domestic Product (GDP).

Contact: mingyu.kim@slearnic.com

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