Microsoft Healthcare Japan

Mitsubishi Tanabe Pharma

Security and Compliance

Enhancing cloud security is a key challenge for data utilization to achieve “VISION 30”

System overview diagram Azure OpenAI improves operations, saves 550 hours of work a year, and improves security literacy What is noteworthy about Azure OpenAI’s approach is that it does more than just translation and summarization: it automatically shows why measures are necessary and what risks would be involved if left unchecked.
With about 10 cases handled per day, the annual reduction amounts to 550 hours.
“Azure OpenAI [enabled us] to quickly realize our ideas while leveraging our existing PaaS environment.” Hiromichi Ozaki, Pharma Solution Delivery Department’s Value Chain Solution Group, Mitsubishi Tanabe Pharma Corporation Discover more details CUSTOMER PRODUCTS Azure Azure Logic Apps Azure AI Studio Microsoft Sentinel Microsoft Defender Microsoft Teams SERVICES AND SUPPORT Azure support plans ORGANIZATION SIZE 1,000-9,999 employees COUNTRY Japan BUSINESS NEED Security INDUSTRY Health Pharma Explore related stories Stories in this industry Stories about this customer Showing 32 of 98 categories Showing 1-2 of 2 slides Previous slide Next slide We are Microsoft Empowering others Our mission is to empower every person and every organization on the planet to achieve more.

Reported impact

Business outcome

Generative AI, which can generate a variety of contents using learned data such as images, texts, and program codes, can be expected to improve productivity if utilized well. In this context, Mitsubishi Tanabe Pharma has incorporated Azure OpenAI Service (hereinafter, Azure OpenAI) into its security operations to reduce the time required to check incidents and improve the employee security literacy.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

Mitsubishi Tanabe Pharma used Azure OpenAI Service, Azure AI Studio, Microsoft Dynamics 365 to improve secure, governed access to business data and operations. Enhancing cloud security is a key challenge for data utilization to achieve “VISION 30”. Key implementation elements included Azure OpenAI Service generates natural-language responses and task automation for the solution; Azure AI Studio provides the workspace for building, testing, and deploying the AI application; Microsoft Dynamics 365 provides the common business process layer that standardizes operations; Microsoft Defender provides security controls across the consolidated environment. Source metrics include System overview diagram Azure OpenAI improves operations, saves 550 hours of work a year, and improves security literacy What is noteworthy about Azure OpenAI’s approach is that it does more than just translation and summarization: it automatically shows why measures are necessary and what risks would be involved...

Solution architecture

6 components · 3 layers
  1. Compute
    • Azure OpenAI Service Generates natural-language responses and task automation for the solution.
    • Azure AI Studio Provides the workspace for building, testing, and deploying the AI application.
    • Microsoft Azure Hosts the application, data, and integrations on scalable cloud infrastructure.
  2. Orchestration
    • Microsoft Dynamics 365 Provides the common business process layer that standardizes operations.
  3. Governance
    • Microsoft Defender Provides security controls across the consolidated environment.
    • Microsoft Entra ID Manages identity and secure user access.

Architecture clues

  • Emphasizes secure enterprise access to data and AI capabilities.

Evidence from the source

Enhancing cloud security is a key challenge for data utilization to achieve “VISION 30”
Generative AI, which can generate a variety of contents using learned data such as images, texts, and program codes, can be expected to improve productivity if utilized well. In this context, Mitsubishi Tanabe Pharma has incorporated Azure OpenAI Service (hereinafter, Azure OpenAI) into its security operations to reduce the time required to check incidents and improve the employee security literacy.
In this context, Mitsubishi Tanabe Pharma has incorporated Azure OpenAI Service (hereinafter, Azure OpenAI) into its security operations to reduce the time required to check incidents and improve the employee security literacy.
System overview diagram Azure OpenAI improves operations, saves 550 hours of work a year, and improves security literacy What is noteworthy about Azure OpenAI’s approach is that it does more than just translation and summarization: it automatically shows why measures are necessary and what risks would be involved if left unchecked.
With about 10 cases handled per day, the annual reduction amounts to 550 hours.
Transparency Evidence & methodology 77/100 reference value · 90% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value77/100
  • Problem clarity12/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Microsoft

    Mitsubishi Tanabe Pharma customer story

    First-party
    https://www.microsoft.com/en/customers/story/1762135872701615281-mt-pharma-azure-pharmaceuticals-en-japan
    Source type
    Vendor-published customer story
    Published
    May 1, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. Tanabe Pharma Corporation

    Corporate Profile | About Us | Tanabe Pharma Corporation

    First-party
    https://www.tanabe-pharma.com/en/company/outline.html

    The corporate profile states "Number of Employees: 4,340 (Consolidated) 2,384 (Non-consolidated)(As of March 31, 2026)".

    Source type
    Official company website
    Supports
    • Company size

Date evidence

  • Published on source page May 1, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.