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How can you classify data in a containerized environment?

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Data is the lifeblood of any organization, but it can also pose significant risks if not managed properly. Data governance is the practice of ensuring that data is accurate, consistent, secure, and compliant with regulations and policies. One of the challenges of data governance is how to classify data in a containerized environment, where applications run in isolated and portable units called containers. In this article, we will explore what data classification is, why it is important, and how you can apply it in a containerized environment.

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Manik Garg

CISSP | CISA | CRISC | CCSP | ISO 27001:2013 LA | ISO 27701:2019 CLIP/IA | AZ-900 | ISO 22301:2019 LA | PCI-DSS ISA |…

1 What is data classification?

Data classification is the process of categorizing data according to its sensitivity, value, and usage. Data classification helps you to identify what data you have, where it is stored, who can access it, and how it should be protected. Data classification can be based on different criteria, such as the type of data, the source of data, the owner of data, the purpose of data, or the level of confidentiality, integrity, and availability of data. Data classification can also follow different standards or frameworks, such as the ISO 27001, the NIST 800-53, or the GDPR.

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Albert R.

Client Technical Specialist and Chief Database Architect at Mphasis, a Blackstone company || Health AI @ DocNote.ai || GenAI Search @ MetaRAG.ai || GRC @ NIST.ai || KYC @ OFAC.ai
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same way you classify data when its not in a container environment... query the dictionary tables of your target. (125 characters min.)

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Luis José R.

Data Specialist with experience in Data Engineering, Architecture, Advanced Analytics, BI, and Big Data. Focused on driving results with Generative AI and agile methodologies in industrial and service environments
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En un entorno de contenedores, la clasificación de datos se puede lograr mediante etiquetado y metadatos. Las herramientas de orquestación como Kubernetes admiten la aplicación de etiquetas a los contenedores, permitiendo identificar la sensibilidad y el tipo de datos. Además, el uso de sistemas de almacenamiento compatible con metadatos facilita la categorización eficiente. Implementar políticas de clasificación basadas en contenido, donde se evalúa automáticamente el tipo de datos y se asignan etiquetas, es otra estrategia. Integrar soluciones de seguridad que reconozcan y actúen según estas etiquetas refuerza la clasificación.

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2 Why is data classification important?

Data classification is important for several reasons. First, it helps you to comply with legal and regulatory requirements, such as data protection laws, privacy laws, or industry-specific regulations. Data classification can help you to avoid fines, lawsuits, or reputational damage if you breach any of these rules. Second, it helps you to optimize your data security and risk management. Data classification can help you to apply the appropriate controls and measures to protect your data from unauthorized access, modification, or loss. Data classification can also help you to prioritize your data backup and recovery plans, as well as your data retention and disposal policies. Third, it helps you to improve your data quality and usability. Data classification can help you to eliminate redundant, outdated, or irrelevant data, and to ensure that your data is accurate, consistent, and reliable. Data classification can also help you to enhance your data analysis and decision making, by providing you with relevant and meaningful insights from your data.

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3 How to classify data in a containerized environment?

Classifying data in a containerized environment can be difficult due to the dynamic, ephemeral, and distributed nature of containers. To address this challenge, adopting best practices and tools is essential. Metadata can be used to label and annotate data and containers, allowing you to identify and classify them, as well as enforcing policies and rules. Data catalogs can also be created to store information about data and containers, helping with discovery, classification, and tracking changes. Additionally, data classification software can scan, analyze, and classify data automatically with predefined or custom rules. These practices are important for data governance in containerized environments where data is constantly changing. By following the best practices and tools mentioned above, organizations can effectively classify their data and containers while reaping the benefits of data governance.

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Manik Garg

CISSP | CISA | CRISC | CCSP | ISO 27001:2013 LA | ISO 27701:2019 CLIP/IA | AZ-900 | ISO 22301:2019 LA | PCI-DSS ISA | AWS Solutions Architect | PCIP
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Data Classification usually depends a lot on the policies and parameters that the organization has decided for the classification and severity level. In containerized environment it's important to decide and analyze if the classification needs to happen at a storage resource level based on the tools similar to a non-containerized environment or if we want to classify the data before even entering the environment. That can help us with appropriate data labelling and analysis and in future stages can help in implementing appropriate security measures.

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4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you classify data in a containerized environment?

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1 What is data classification?

2 Why is data classification important?

3 How to classify data in a containerized environment?

4 Here’s what else to consider

Data Governance

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How can you classify data in a containerized environment?

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1 What is data classification?

2 Why is data classification important?

3 How to classify data in a containerized environment?

4 Here’s what else to consider

Data Governance

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