Data Flow Intelligence & Governance

In modern organizations, data propagates and moves fast, creating a sprawling environment of data warehouses, applications, systems, and LLMs. The complicated nature of this data flow makes it challenging to track where data actually originated from, how it transformed over time, and where it eventually ended up.

Additionally, data that flows through modern streaming environments like Kafka or Confluent increases the complexity twofold. It makes it more difficult for teams to identify exposed sensitive data or data access anomalies.

In dynamic data environments, it is not unusual to track the location of data since it may originate from various sources, undergo multiple transformations, and end up in countless destinations. A lack of visibility into data can jeopardize its security and increase compliance risks. Here, data lineage plays a crucial role in tracking the data lifecycle—from its creation, through its transformation, and then to its destination.

However, today’s dynamic or more opaque data environments make it challenging for traditional lineage tools to deliver complete data lifecycle insights. Therefore, modern teams leverage an alternative and a more robust alternative, which is inferred lineage. This type of lineage tracking uses contextual information along with AI-powered methodologies to offer a comprehensive view of data flows in highly complex environments.

Modern DSPM solutions leverage this capability and more to provide deep and continuous information about data flows, including transformations and associated risks, across multiple cloud environments.

The solution uses customizable and automated data flow maps that illustrate how data moves across systems and applications, how it is transformed, and how it interacts with different environments. DSPM’s data lineage tracking capability leverages techniques like SQL parsing, dbt integration, and direct system interrogation to collect all those details. With inference-based lineage tracking, DSPM helps teams quickly assess data characteristics like timestamps, formats, and even data formats to show data relationships and their movement patterns.

DSPM further leverages clustering techniques to detect and group similar objects or files. By assessing metadata attributes like the data’s creation date and the relational context, DSPM understands how the data moved between sources or destinations.

As discussed, streaming platforms have further complicated tracking the movement of data in environments with high-velocity data movement. For such environments, DSPM checks real-time insights from Topics and event flows, offering comprehensive insights into streaming data pipelines. For instance, in Kafka – an open source streaming platform – DSPM first automatically discovers data in the Kafka Topics, assessing large volumes of data without latency and in real-time. Secondly, the solution can further mask or encrypt the sensitive data sets automatically in the pipeline before the data is transmitted to consumers. More importantly, the built-in regulatory and reporting context helps compliance teams to comply with data regulations.

In the same context, DSPM helps compliance teams proactively track regulatory violations and mitigate them as soon as possible. For instance, as data moves between systems and borders, the solution flags the data transfer as a potential violation of data residency provisions. These capabilities enable compliance teams to effectively stay ahead of critical regulations and frameworks like the EU GDPR , EU AI Act, NIST AI RMF, etc.

A DSPM solution may also integrate tribal knowledge, providing teams with details associated with process-level and business data attributes. Data flow maps enrich them with contextual information that includes aspects like business purpose, data usage frequency, or data owners. All these insights help teams get a better view of their data landscape and govern it effectively.

DSPM provides a visual representation of data flow maps with an integrated Knowledge Graph. It is an intelligent layer that offers team details into cross-border data transfer activities, third-party data sharing, and data processing at different stages of the data lifecycle. These data flow maps provide information related to security context, compliance violations, misconfigurations, and any policy gaps. With all these informative insights, teams can effectively detect and remediate risks associated with data flows, protecting the data across its lifecycle.