How Modern Data Security Discovers Sensitive Data at Cloud Scale
Modern cloud environments contain vast amounts of data stored in object storage services such as Amazon S3, Google Cloud Storage, and Azure Blob Storage. In large organizations, a single data store can contain billions (or even tens of billions) of objects. In this reality, traditional approaches that rely on scanning every file to detect sensitive data quickly become impractical.
Full object-level inspection is expensive, slow, and difficult to sustain over time. It increases cloud costs, extends onboarding timelines, and often fails to keep pace with continuously changing data. As a result, modern data security platforms must adopt more intelligent techniques to build accurate data inventories and sensitivity models without scanning every object.
Why Object-Level Scanning Fails at Scale
Object storage systems expose data as individual objects, but treating each object as an independent unit of analysis does not reflect how data is actually created, stored, or used.
In large environments, scanning every object introduces several challenges:
- Cost amplification from repeated content inspection at massive scale
- Long time to actionable insights during the first scan
- Operational bottlenecks that prevent continuous scanning
- Diminishing returns, as many objects contain redundant or structurally identical data
The goal of data discovery is not exhaustive inspection, but rather accurate understanding of where sensitive data exists and how it is organized.
The Dataset as the Correct Unit of Analysis
Although cloud storage presents data as individual objects, most data is logically organized into datasets. These datasets often follow consistent structural patterns such as:
- Time-based partitions
- Application or service-specific logs
- Data lake tables and exports
- Periodic reports or snapshots
For example, the following objects are separate files but collectively represent a single dataset:
logs/2026/01/01/app_events_001.json
logs/2026/01/02/app_events_002.json
logs/2026/01/03/app_events_003.json
While these objects differ by date, their structure, schema, and sensitivity characteristics are typically consistent. Treating them as a single dataset enables more accurate and scalable analysis.
Analyzing Storage Structure Without Reading Every File
Modern data discovery platforms begin by analyzing storage metadata and object structure, rather than file contents.
This includes examining:
- Object paths and prefixes
- Naming conventions and partition keys
- Repeating directory patterns
- Object counts and distribution
By identifying recurring patterns and natural boundaries in storage layouts, platforms can infer how objects relate to one another and where dataset boundaries exist. This analysis does not require reading object contents and can be performed efficiently at cloud scale.
Configurable by Design
Sampling can be disabled for specific data sources, and the dataset grouping algorithm can be adjusted by the user. This allows teams to tailor the discovery process to their environment and needs.
Automatic Grouping into Dataset-Level Assets
Using structural analysis, objects are automatically grouped into dataset-level assets. Clustering algorithms identify related objects based on path similarity, partitioning schemes, and organizational patterns. This process requires no manual configuration and adapts as new objects are added. Once grouped, these datasets become the primary unit for further analysis, replacing object-by-object inspection with a more meaningful abstraction.
Representative Sampling for Sensitivity Inference
After grouping, sensitivity analysis is performed using representative sampling. Instead of inspecting every object, the platform selects a small, statistically meaningful subset of files from each dataset.
Sampling strategies account for factors such as:
- Partition structure
- File size and format
- Schema variation within the dataset
By analyzing these samples, the platform can accurately infer the presence of sensitive data across the entire dataset. This approach preserves accuracy while dramatically reducing the amount of data that must be scanned.
Handling Non-Standard Storage Layouts
In some environments, storage layouts may follow unconventional or highly customized naming schemes that automated grouping cannot fully interpret. In these cases, manual grouping provides additional precision. Security analysts can define logical dataset boundaries, often supported by LLM-assisted analysis to better understand complex or ambiguous structures. Once defined, the same sampling and inference mechanisms are applied, ensuring consistent sensitivity assessment even in edge cases.
Scalability, Cost, and Operational Impact
By combining structural analysis, grouping, and representative sampling, this approach enables:
- Scalable data discovery across millions or billions of objects
- Predictable and significantly reduced cloud scanning costs
- Faster onboarding and continuous visibility as data changes
- High confidence sensitivity models without exhaustive inspection
This model aligns with the realities of modern cloud environments, where data volume and velocity continue to increase.
From Discovery to Classification and Continuous Risk Management
Dataset-level asset discovery forms the foundation for scalable classification, access governance, and risk detection. Once assets are defined at the dataset level, classification becomes more accurate and easier to maintain over time. This enables downstream use cases such as identifying over-permissioned access, detecting risky data exposure, and managing AI-driven data access patterns.
Applying These Principles in Practice
Platforms like Sentra apply these principles to help organizations discover, classify, and govern sensitive data at cloud scale - without relying on full object-level scans. By focusing on dataset-level discovery and intelligent sampling, Sentra enables continuous visibility into sensitive data while keeping costs and operational overhead under control.
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Ariel is a Software Engineer on Sentra’s Data Engineering team, where he works on building scalable systems for securing and governing sensitive data. He brings deep experience from previous roles at Unit 8200, Aidoc, and eToro, with a strong background in data-intensive and production-grade systems.
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