Where to start?
When implementing compliance with international standards, you must know what type of data your company is managing and storing. Therefore, it is important to classify your data to understand which of the data you are storing is confidential or sensitive. By classifying your data, you categorize it based on its sensitivity, relevance, or regulatory requirements.
The next step: data tagging
This involves assigning metadata labels to data assets, providing additional context, and facilitating organisation. In Azure environments, data may be stored across multiple regions, databases, and containers. Ensuring consistent and accurate classification and tagging across these diverse resources is essential for effective data governance and compliance.
Why is data classification and tagging so important for GDPR compliance?
There are three main reasons why data classification and tagging are highly important to managing your GDPR compliance.
1. Quickly respond to data subject requests
In the case of a GDPR data subject request, you must be able to locate and delete personal information on the spot. If your data is properly classified and tagged, you can quickly identify your relevant data. Therefore, minimising response time and ensuring compliance.
2. Minimise data breaches
Effective data classification and tagging enable you to assess and mitigate risks associated with data handling and storage. By identifying sensitive data and applying appropriate security controls, you minimise the likelihood of data breaches and regulatory non-compliance.
3. Manage and organise your data
By classifying and tagging your data, you facilitate better organisation and management of your data assets, enhance data governance, and reduce operational complexities.
Commonly used classification tags for sensitive data
To help you quickly identify and prioritise data, that requires heightened protection and compliance measure, we have selected some commonly used classification tags for you:
● Personal Identifiable Information (PII)
● Financial Data (e.g., credit card numbers, banking information)
● Health Information (e.g., medical records)
● Intellectual Property (e.g., patents, trade secrets)
● Confidential or Restricted Data (e.g., employee records, legal documents)
Rather leave the classification and tagging to AI?
Sure! By leveraging Azure's flexible infrastructure and development tools, you can customise AI models and algorithms to meet specific business requirements and compliance needs. Whether it's fine-tuning classification rules, integrating with proprietary data sources, or implementing industry-specific compliance standards, Azure offers the flexibility and scalability to accommodate diverse use cases.
Wondering where to start? These are three interesting options:
1. Identify patterns with AI
Azure offers advanced AI capabilities that help you develop custom solutions that automatically recognise sensitive information within data sets. By using natural language processing (NLP) and machine learning algorithms you can identify patterns, keywords, and context clues that signify sensitive data such as personal information, financial details, or proprietary data.
2. Universal data handling
In Azure, you can build AI-driven systems that are capable of handling any type of data, stored across various Azure resources such as SQL databases, Cosmos DB, and storage accounts. These systems are designed to adapt to different data formats, structures, and locations, ensuring comprehensive coverage and consistency in data classification and tagging efforts.
3. Integration with Azure Purfew and Information Protection
While Azure Purview and Azure Information Protection offer valuable features for data governance, they may have limitations in handling diverse data types and complex classification requirements. However, by integrating these services with AI-driven solutions, you can enhance their capabilities and overcome these limitations. For example, Azure Purview can be used for automated data discovery, while AI algorithms complement its classification capabilities by accurately identifying sensitive data entities within discovered data sets.
To conclude:
In short, we can safely conclude that data classification and tagging are indispensable components of GDPR compliance on Azure! By using Azure's strong tools and services, you can sort and label your data, lower risks related to following rules, and show that you are dedicated to safeguarding your organisation, coworkers, and customers' privacy and rights. A win-win right?
