AI incidents can happen

in any organization.

For example:

• sensitive data is entered
• wrong information is shared
• inappropriate content is published
• copyright issues occur

These problems cannot be fully avoided.

That is why

Incident Management (A.8.2)

is important in AIMS.

In this episode,

we explain simple first actions:

👉 Stop

👉 Check

👉 Report

These steps help reduce damage

and keep the situation under control.

👉 Good preparation helps you stay calm.

This episode gives you

a practical mindset

for handling AI incidents.

P-Mark (PMS) is not just about rules or documents.

It is a system to continuously protect personal data.

In this episode,

we explain the core concept of PMS,

why operations differ by company,

and how it compares with ISMS in a simple way.

AI is useful,

but it should not be used without checking.

That is why

Validation and Testing (A.6.2.4)

is important in AIMS.

This episode explains:

👉 what to check before using AI

👉 what to check after getting results

You will learn a simple checklist:

• before using AI
• after using AI

These checks help prevent:

• wrong information
• outdated content
• bias
• unsafe sharing

👉 AI is safe only when we check it.

This episode gives you practical steps

you can use right away.

Many companies have ISMS,

but it does not work well.

In this episode, we explain:

• four common problems
• why ISMS fails
• what companies should avoid

The key point is simple.

ISMS should be a system,

not something one person runs.

When you see an AI answer,

do you think about

the data behind it?

AI results are affected by data.

If the data has problems,

the result may also have problems.

For example:

• the data may be biased
• the source may be unclear
• the information may be old
• there may be copyright issues
• personal or sensitive data may be used

In this episode,

we talk about

Design and Development: Data Handling (A.7.2)

in AIMS.

We explain why organizations should check

data quality,

data bias,

and data sources.

This episode helps you notice

risks that are easy to miss.

Many companies start ISMS in the wrong way.

They begin by making rules and documents.

But this often causes problems.

In this episode, we explain:

• a common mistake in ISMS
• why starting with rules does not work
• what companies should do first

The key is simple.

Start by understanding your company.

I introduces new types of risks

that are different from traditional IT.

For example:

• data leakage
• incorrect outputs
• bias
• lack of transparency
• overtrust in AI

These risks can feel unclear and scary.

But when we identify and organize them,

they become easier to manage.

In this episode,

we explain AI risk assessment (A.6.3)

in a simple and practical way.

👉 If we understand AI risks, we can control them.

This episode helps you move from

“uncertain fear”

to

“clear understanding.”

Companies preparing for IPO often ask:

“Where should we start with information security?”

In this episode, we explain the first steps for building a security system in IPO preparation.

We discuss:

• Building company-wide security awareness
• Creating basic security guidelines
• Listing information assets across departments
• Establishing security roles and committees

This episode helps companies understand how to start building a practical security structure before IPO.

AI is powerful,

but it is not perfect.

That is why

Human Oversight (A.5.3)

is an important concept in AIMS.

Human Oversight means:

👉 Do not rely on AI completely.

• Check AI results
• Review before use
• Make final decisions as a human

These simple actions

can reduce many risks.

AI can make mistakes.

It can use old data.

It can misunderstand context.

That is why

humans must stay involved.

In this episode,

we explain how to apply

Human Oversight

in a simple and practical way.

Transparency is a key control

in AIMS (ISO/IEC 42001).

But what does it mean in practice?

Transparency is simple.

👉 Make AI use visible and explainable.

For example:

• Say when AI is used
• Show that humans check the results
• Be ready to explain how AI is used

These small actions

can build strong trust.

In this episode,

we explain A.5.2 Transparency

from a practical point of view.

You will learn how to

apply transparency

in your daily work.