Hamamoto, TIMEWELL.
Generative AI has fundamentally changed how we work. Meeting minutes, draft proposals, code generation — the range of applications grows every day. But hidden behind that overwhelming convenience is a serious security risk that could shake the very foundation of your business.
Many organizations are unknowingly handing over their most valuable asset — information — to external LLM (Large Language Model) vendors for free. This is no different from giving a stranger the key to your vault. This article explains the danger in detail and tells you how to protect your organization.
Chapter 1: The Trap of Feeding Data Directly to LLM Vendors
The Real Cost of Free and Consumer AI Services
"ChatGPT has improved our productivity." This is something I hear often. But in many cases, the version being used is the free tier or a consumer subscription like ChatGPT Plus. Entering confidential company information into these services is genuinely dangerous.
Here's why: under default settings, data entered by you or your employees may be used to train the LLM. This is stated in the terms of service — but most users overlook it.
Once information is incorporated as training data, it may appear in responses to other users, or be stored on vendor servers indefinitely. Your hard-won business ideas, your proprietary source code, your customers' personal data — all of it could find its way into a competitor's hands without your knowledge.
What Companies Must Never Enter Into ChatGPT
The following types of information are extremely dangerous to enter into free or consumer AI services:
| Type of Information | Examples | Risk |
|---|---|---|
| Customer personal data | Names, addresses, phone numbers, email addresses | Personal information law violations; loss of customer trust |
| Confidential information | Source code, design documents, meeting contents, strategy | Leakage to competitors; loss of competitive advantage |
| Financial data | Revenue, margins, pricing, investment plans | Stock price impacts; accountability to stakeholders |
| Credentials | Passwords, API keys, tokens | Account takeover; system compromise |
| Medical information | Patient data, diagnosis information | HIPAA violations; patient privacy breach |
| Legal documents | Contracts, NDAs, litigation-related documents | Legal risk; contractual obligation violations |
Once this information leaks, corporate credibility collapses — and recovery takes enormous time and resources.
Chapter 2: Real Incidents That Reveal the Stakes
Case 1: Samsung Electronics Confidential Data Leak (April 2023)
In 2023, employees at Samsung Electronics entered confidential source code into ChatGPT, resulting in a leak. The company took the incident seriously and immediately banned all internal use of generative AI tools.
This case shows clearly how a single employee's careless action can expose an entire organization. If a global giant like Samsung can be hit by this kind of incident, no company should consider itself immune.
Case 2: ChatGPT Bug Exposes Personal Information (March 2023)
In March 2023, a bug in ChatGPT made certain paid subscribers' personal information visible to other users — a serious incident.
Information exposed:
- Names
- Email addresses
- Billing addresses
- Credit card information (type, last four digits, expiration date)
OpenAI suspended the service, fixed the bug, notified affected users, and implemented preventive measures. But what this case illustrates is the harsh reality that no matter how large and reputable a service is, security incidents are unavoidable.
Case 3: Malware-Driven Mass Theft of Account Credentials (2023)
In 2023, it was revealed that over 100,000 ChatGPT account credentials had been stolen by malware and were being traded on the dark web. By February 2025, posts appeared indicating that 20 million credentials were being sold.
How the attack works:
- An employee's device gets infected with information-stealing malware (Redline, Lumma, etc.)
- The malware steals ChatGPT account credentials
- Stolen credentials are sold on the dark web
- Attackers use the credentials to access the company's confidential information
The critical point: a leak can occur through an infected employee device even without a direct attack on company systems. Your company's accounts may already be trading on underground markets.
Case 4: Database Misconfiguration Exposes User Data (November 2023)
A vulnerability was discovered in a conversational AI service operated by a Japanese provider. A database configuration error made it possible for third parties, through specific operations, to view and edit user nicknames, input prompts and generated results, and registered email addresses and LINE IDs.
These vulnerabilities can exist at any AI service provider — not just major players.
Struggling with AI adoption?
We have prepared materials covering ZEROCK case studies and implementation methods.
Chapter 3: What Feeding Data Directly to LLM Vendors Actually Means
How Corporate Assets Leak Out
Feeding data directly to LLM vendors sets in motion a process by which corporate assets are lost:
Step 1: Data entry and transmission When an employee enters corporate data into a service like ChatGPT, that data is transmitted to the vendor's servers. At this point, the data leaves corporate control.
Step 2: Training data use Under default settings, the data entered is used to train the LLM — effectively using your corporate data to improve the AI model.
Step 3: Exposure in other users' responses Trained data may appear in responses generated for other users. Your company's confidential information could end up in an answer to a competitor's question.
Step 4: Irrevocable storage Once data becomes training data, users cannot delete it. It is stored on vendor servers indefinitely.
Regulatory Violation Risk
What makes this even more serious is that this kind of data provision may constitute a legal violation.
GDPR The EU's GDPR sets strict requirements for transferring personal data to third countries. Unrestricted entry of data into ChatGPT may constitute a GDPR violation.
Japan's Personal Information Protection Act Japan's law requires the consent of the individual when providing personal information to third parties. Entering customer data into ChatGPT may violate this requirement.
HIPAA Healthcare organizations entering patient data into ChatGPT could face HIPAA violations, substantial fines, and legal liability.
Chapter 4: Enterprise Solutions as a Shield
Does this mean organizations have to give up on the benefits of AI? No. The answer lies in leveraging AI platforms specifically designed for enterprise use.
The Three Major Enterprise AI Players
Three services are currently dominant as enterprise AI platforms:
1. Azure OpenAI Service (Microsoft)
Azure OpenAI Service is Microsoft's enterprise AI platform. A January 2026 survey found it to be the most-used AI platform among IT system administrators.
Key features:
- Microsoft environment integration: Seamless integration with Microsoft 365 and Azure environments
- Enterprise-grade security: Multi-layered defense based on the Zero Trust model
- Data retention policy: 30-day data retention (opt-out available)
- On Your Data feature: Securely integrate corporate data into a private AI environment
- Japan legal compliance: Microsoft contract compliant with Japanese law; jurisdiction in Tokyo District Court
2. Amazon Bedrock (AWS)
Amazon Bedrock is a fully managed service from AWS. It provides access to models from multiple LLM providers with flexible configuration.
Key features:
- VPC endpoints: Private access that avoids internet routing
- Data protection and encryption: TLS 1.2 in transit; AWS KMS-managed encryption at rest
- Access control: Fine-grained permissions via AWS IAM
- Model provider protection: Mechanism preventing model providers from accessing customer data
- Audit logging: All operations recorded via CloudTrail and CloudWatch Logs
3. Google Vertex AI (Google Cloud)
Google Vertex AI is Google Cloud's AI platform. Its strength is Gemini's ultra-long context window processing.
Key features:
- VPC Service Controls: Network access restrictions
- Enterprise security features: VPC peering support
- Data exfiltration risk mitigation: Processing within VPC
- Logging controls: Option to avoid logging customer data
How Enterprise Solutions Protect Your Data
These platforms protect your data through mechanisms including:
1. Private Network Connection (VPC)
Your cloud environment connects to AI services via a private network rather than the public internet. This eliminates the risk of third-party interception at the root.
2. Data Encryption
All data in transit and at rest is strongly encrypted. Even if data somehow leaves the environment, decryption is extremely difficult.
3. Strict Access Control
Detailed management of who can access what data, from where — with unauthorized access completely blocked.
4. Data Not Used for Training
Most importantly: with these services, your corporate data is never used to train LLMs. Data remains completely in your control, protected in an isolated environment.
Selection Criteria: Compatibility with Existing Environments Is Critical
A January 2026 survey identified the most important factors organizations consider when selecting AI platforms:
| Platform | Adoption Rate | Primary Selection Reasons |
|---|---|---|
| Azure OpenAI Service | 7.5% | Microsoft environment integration; enterprise-grade security |
| Amazon Bedrock | 6.7% | Serverless architecture; ability to use multiple LLMs |
| Google Vertex AI | 4.6% | Google Workspace integration; Gemini's long context processing |
The findings show that compatibility with existing cloud environments and security are decisive factors in enterprise selection. The logic of "Azure environment → Azure OpenAI, AWS environment → Bedrock, GCP environment → Vertex AI" is sound — leveraging existing cloud investments and security policies is a key success factor.
Chapter 5: The Concrete Benefits of Enterprise Solutions
1. Complete Retention of Data Ownership
With enterprise solutions, corporate data always remains under corporate control. Vendors can only access what the organization explicitly permits.
2. Full Regulatory Compliance
Full compliance with GDPR, HIPAA, Japan's Personal Information Protection Act, and other regulations — while still receiving the benefits of AI.
3. Guaranteed Auditability
All operation logs are recorded and auditable. Even if a security incident occurs, root cause analysis and response can happen quickly.
4. Customizable Security Settings
Addresses organization-specific security requirements. Examples: store data only in specific regions; limit access to specific departments only.
5. Clear Accountability Through SLA
Service Level Agreements clearly define vendor responsibility. Compensation is available even if the service goes down.
Chapter 6: Common Adoption Challenges and Solutions
Challenge 1: Higher Cost
Enterprise solutions cost more than free or consumer AI services. But viewed against the potential damage from a security incident, this investment is extremely rational.
For example: if a customer data leak costs the organization credibility and causes a 10% revenue decline, the loss could run into hundreds of millions of yen. Enterprise solution costs represent a fraction of that exposure.
Challenge 2: Implementation Complexity
Deploying enterprise solutions involves multiple steps: network configuration, authentication setup, audit log configuration. But with the right partner, these processes can proceed smoothly.
Challenge 3: Employee Education and Behavior Change
Employees accustomed to free ChatGPT will need education and habit change to shift to enterprise solutions. With a clear explanation of why security matters, employee understanding and cooperation can be obtained.
Chapter 7: TIMEWELL's Secure AI Solutions
If you've read this far, you may be thinking: "I understand why enterprise solutions matter — but how do I actually deploy them?" TIMEWELL offers solutions designed to solve exactly this problem.
ZEROCK: A Secure Enterprise AI Platform
ZEROCK is an enterprise AI platform that securely connects your internal organizational knowledge with AI.
ZEROCK's key features:
- Domestic AWS server operation: All data processed within Japan on AWS servers. Eliminates the risk of overseas data transfer.
- High-precision responses with GraphRAG: Understands context across internal documents to generate more accurate answers.
- Data not used for training: Input data is never used to train LLMs.
- Private network support: Secure communication via VPC — no internet routing required.
- Prompt library: Provides prompt templates optimized for business operations.
- Knowledge control: Fine-grained access permissions by department and project.
With ZEROCK, organizations that want to use AI but worry about security can enjoy the benefits of AI with confidence.
WARP: Consulting That Guides Successful AI Adoption
WARP is a consulting service that resolves the full range of AI adoption challenges.
What WARP addresses:
- "We don't know which AI platform is right for us"
- "The enterprise solution deployment process is too complex to navigate"
- "We want a security audit of our current AI operations"
- "We want to run AI literacy training for our employees"
Consultants with backgrounds in DX and data strategy at major corporations will propose the optimal solution for your specific situation — turning vague concerns into concrete confidence.
Summary: A Compass for the AI Era
Generative AI is a powerful engine for accelerating business. But pressing the accelerator recklessly means risking going off a cliff. What matters is understanding the risks correctly and equipping the right "shield."
Key Points
Free and consumer services are not enterprise tools: ChatGPT's free tier and ChatGPT Plus are designed for personal use. Entering confidential corporate information is extremely dangerous.
Data leaks are a real threat: Even major corporations like Samsung have suffered. No company should assume it's an exception.
Enterprise solutions are essential: Azure OpenAI Service, Amazon Bedrock, Google Vertex AI — enterprise solutions reliably protect your data.
Compatibility with existing environments is critical: Choosing a solution that aligns with your existing cloud environment is a key success factor.
The True Nature of Feeding Data Directly to LLM Vendors
Feeding data directly to LLM vendors is no longer "efficiency improvement" — it is "abandoning your assets." Corporate data is the source of competitive advantage. Entrusting it unprotected to external parties is equivalent to putting your company's future at risk.
Deploying enterprise solutions and bringing your data completely under your control is the essential condition for navigating the AI era.
Contact Us
"Which service is right for us?" "What does the actual deployment process look like?" "We'd like a security audit of our current AI operations."
If any of these questions resonate, that's an important first step.
Protecting your data means protecting your future. Feel free to contact us.
References
- Lanscope (August 21, 2025). "Data Leak Risks When Using ChatGPT: Effective Countermeasures"
- HubSpot. "Can Generative AI Cause Data Leaks? Case Studies, Causes, and Countermeasures"
- Nippon Communication Network (July 4, 2025). "5 Cases of Data Leaks from Generative AI: Causes and Detailed Solutions"
- Hakky Handbook. "AWS Bedrock Security: Data Protection and Compliance"
- Open Lab (April 22, 2024). "Azure OpenAI Service In-Depth: A Secure AI Environment for Enterprises Without Worrying About Confidential Data Leaks"
- Yahoo! News (January 23, 2026). "Azure OpenAI Takes Top Utilization Spot — Why Did It Beat Amazon Bedrock and Vertex AI?"