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Data Flow Intelligence & Governance

Confidently Adopt Multi-Cloud Streaming Environments

Data Flow Intelligence & Governance

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Data Flow Intelligence & Governance

Effectively Manage & Protect Sensitive Data Flowing Through Your Streaming Environment

Data is growing faster than ever. Adding multi-cloud to the mix, we see a monolith boom in the data volume, size, and complexity. Companies increasingly depend on data to drive business growth, opportunities, and revenue. However, to do so, they need real-time insights into data flowing through their environment. Consequently, this has led businesses to leverage streaming platforms like Confluent Kafka, Amazon Kinesis, or Google Pub/Sub. However, managing data's secure flow and distribution in such high-volume streaming environments is challenging.

Securiti’s Data Flow Intelligence & Governance is built to reduce sensitive data risks and unintended data sprawl in streaming environments. Securiti for data streaming leverage sensitive data intelligence and automation to enable teams to establish appropriate data security, governance, and compliance controls. Download our Data Flow Intelligence & Governance brochure to learn how Securiti helps you gain visibility and control over sensitive data flowing through streaming environments.

Here Are Our Data Flow Intelligence & Governance Key Features

Discover Sensitive Data

Leverage AI/ML to discover and classify sensitive data in your streaming platforms.

Govern Access

Prevent unauthorized access by monitoring and setting up controls around data access.

Mitigate Risks

Prevent unintended data leaks and sensitive data exposure through dynamic data masking.

Frequently Asked Questions

Streaming data means data in transit. It involves the continuous flow of data in real time. Streaming data is beneficial for businesses that demand real-time data analysis in transit. This data type is often generated by the point of sale (PoS) systems, sensors, and other devices transmitted over the internet and the intranet.

Streaming data involves the real-time flow of data as soon as the data is generated. For this type of processing, dedicated streaming environments are used, such as Kafka, Confluence, etc. Batch processing, on the other hand, is typically used to process data at rest. It involves the bulk processing of data when it is collected and stored in a database or a data warehouse.

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