Giovanni Battista Spinelli Barrile l Oct 4, 2022 l Data Protection, Big Data Analytics, Cloud Computing

3 Reasons Why Your Data Analytics Environment Needs Better Protection

Are your cloud environments secure enough to defend your data from bad actors and ensure compliance with regulatory obligations? Many databases, data warehouses, data storages, and cloud service providers offer capabilities to safeguard your valuable data. Current protection methods vary from perimeter security for defending a specific environment to data-centric security for defending the data itself. Regardless of the methodology, more and more organizations are relying on external vendors to protect their data. Snowflake alone suggests about ten possible external solutions in their technical documentation. So, what are the reasons for this trend?

1. Complex Cloud Environments

First, numerous data tools offer security features that protect valuable information within that particular environment. However, data owners and specialists must consider the bigger picture of an end-to-end architecture. Modern data ecosystems include a variety of tools that spread across hybrid and multi-cloud environments. Additionally, activating and maintaining stand-alone data protection options for each component of a complex architecture is burdensome, inefficient and prone to misconfigurations that could expose your environment to risk. Information may be exposed while in transit or when used in another tool. Constantly protecting and de-protecting data as it flows across the ecosystem produces latencies and negatively impacts performance.

The more convenient and efficient approach is to apply a single, data-centric protection technology specialized in keeping sensitive data secure as it flows across complex architectures.

2. Increasingly Stringent Regulatory Requirements

Second, sole reliance on encryption of sensitive information may not suffice for compliance with data privacy legislation, such as GDPR. The Schrems II court decision recently ruled that it is required to separate encryption keys from the data itself. Thus, encryption capabilities offered by the very same tool where both the data and the encryption keys are stored can result in non-compliance, so using an external data protection provider may be necessary to stay compliant and simplify key management.

3. Cost

Lastly, many data protection features are expensive. In some cases, security options provided by cloud service providers or data management tools result in an additional cost of 30% to the net price, and they only protect data in their particular environment. If you are using multiple cloud-based tools and secure each of them individually, the costs can add up quickly.

The Best Way to Secure Data Analytics Environments 

An external protection tool that protects data across your hybrid and multicloud environment, such as the comforte Data Security Platform, is the most robust and cost-effective way to secure your cloud applications. The comforte Data Security Platform offers different data protection methods ranging from data masking and format preserving hashing to tokenization and Format-Preserving Encryption (FPE), enabling pseudonymization or full anonymization of data. While anonymization removes sensitive elements from data and makes it unusable (e.g. for advanced analytics), pseudonymization methods—such as FPE—protect the data in a way that it is still usable in analytic or BI tools; it preserves the format of the data, referential integrity and more. The comforte platform integrates with a variety of hybrid or multi-cloud environments, allowing for greater flexibility and adaptability of its use cases.

Organizations worldwide are collecting vast amounts of data with the purpose of monetizing it for business goals. With the right privacy-preserving protection technology, they can take an important step towards fully leveraging the potential of sensitive data in a secure and compliant fashion.

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