For over two decades, our appetite for data has led us to create and invest in a lot of technologies such as NoSQL and SQL databases, streaming services, machine learning, data warehouses, etc. There is a constant demand for this information – 97.2% of businesses are investing in big data analytics and AI solutions. Nevertheless, not all the collected data carry the same weight. Only particular pieces can be used to identify an individual. Hence, it must be viewed as sensitive material. This would require special attention when isolating and safeguarding it from non-sensitive app information.
Data Privacy Vault: An Overview of its Features
Creating a successful data privacy vault requires several features, to ensure the security and isolation of sensitive data. They include:
- Security: Security measures such as encryption, tokenisation, and data masking must be natively implemented in the infrastructure.
- Database: The database should provide standard functionality so that it can easily integrate with existing infrastructures.
- Isolation: The vault has to be situated in a separate network and the access must be privileged. Audit logging must be built-in to track access.
- Achieving Enterprise-Level Reliability: The vault is an essential part of a business’s infrastructure, so it must be able to handle high levels of availability and throughout.
- ACL Management: The vault requires local data governance and zero-trust architecture. This is crucial to enable administrators to exercise precise control over user access as well as programmatic on a table, column, and row level.
- Data Utility: The vault has to ensure security in storage as well as access to sensitive data. It should enable authorized use while ensuring privacy and security.
- Use-Case Support Flexibility: The vault should be able to store structured as well as unstructured data. It should also have the capability to serve a wide range of applications.
Integrating the Data Privacy Vault Into Your Tech Setup
With the introduction of data privacy vaults, user data isn’t stored along with application and other non-sensitive data. This indicates that the application must retrieve the secured data from the data vault and not directly from the database. For applications to maintain existing schemas and pipelines, tokenised versions of PII can be employed instead of plaintext values in their storage layers. Additionally, secure functions within the vault provide third-party services with access to necessary encrypted sensitive information. There is no need for any raw plain text to enter into a backend system. This reduces both compliance burdens and risk of breach significantly.
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