The Challenges of Scaling DSPM in High-Volume Data Environments

In today's digital world, companies generate and store massive amounts of data. For Fortune 1000 (F1000) companies, this can sometimes reach hundreds of petabytes of cloud data. Organizations grapple with fundamental questions: Where is my data stored? What types of data do we possess? Which data is sensitive? What is at risk? Who has access to it? Addressing these concerns is crucial for maintaining data security and compliance. This is where Data Security Posture Management (DSPM) solutions like Cyera come into play. But scaling DSPM to handle high-volume data environments presents its own set of challenges.

The Immense Scale of Data

F1000 companies deal with an enormous amount of information daily. It's sensitive customer information, financial records, and proprietary business insights and intellectual property. Ensuring that all this sensitive data is secure is crucial. 

However, when you're dealing with hundreds of petabytes, traditional data security methods just aren't scalable enough. This immense volume of data is not only vast but also distributed across a multitude of technologies, including various database systems and file storage solutions. The data exists in numerous formats and layouts, ranging from structured tables to unstructured files, and often contains complex internal relationships and dependencies.

The intricate web of data types and storage technologies complicates efforts to identify where sensitive data resides, understand what that data consists of, assess its risk level, and determine who has access to it. Managing such a heterogeneous and expansive data environment requires advanced solutions that can handle the scale and complexity—challenges that traditional data security methods are ill-equipped to address. Cyera understands this challenge and provides robust DSPM solutions designed to secure data at any scale.

Diverse Data Solutions for Scaling

To tackle the challenges of scaling DSPM, we've had to get creative in managing the enormous volumes of data at rest and the extensive processing demands that come with it. The sheer size of the data involves massive numbers of entries and generates equally massive amounts of metadata, which must be processed and stored efficiently. These challenges are compounded by the need to handle numerous complex operations—ranging from a high volume of queries to heavy read and write workloads—while ensuring rapid access and scalability

We've incorporated a variety of data solutions, including:

• OLTP Databases: These handle a large number of transactions by multiple compute units.
• OLAP Databases: Used for analyzing complex insights from a lot of metadata at a very high speed.
• Graph databases: Excel at handling data with complex relationships, which is common in large-scale environments. They allow us to model and query data as a network of interconnected entities
• Spark: An open-source, distributed processing system used for big data workloads.
• Caching Solutions: They are an integral part of capabilities for faster access.

By combining these technologies, Cyera can manage and process large datasets more efficiently. It's like using different tools in a toolbox; each has its purpose, and together they help us build something great.

Challenges with SaaS Platforms

Scaling DSPM isn't just about handling data stored in traditional databases. Many companies use Software as a Service (SaaS) platforms like Google Drive and Microsoft OneDrive. These platforms weren't originally designed to handle data fetching at such massive scales. This presents unique challenges:

• API Limitations: SaaS platforms often have limits on how much data you can fetch at once.
• Latency Issues: Retrieving data from the cloud can be slower due to network delays.
• Data Fragmentation: Data is spread across multiple services and locations, making it harder to manage.

Cyera addresses these hurdles by developing specialized strategies and tools that work within the constraints of these platforms, ensuring seamless data security management across all services.

The Scaling Secret: A Cost-Efficient Funnel

One of our key strategies for scaling DSPM effectively is creating a cost-efficient and scalable "funnel." Here's how it works:

1. Early Filtering: We use efficient and less expensive components at the beginning of the process to filter and sort data. This reduces the amount of data that needs to be processed later.
2. Layered Processing: Each stage of the funnel processes the data further, ensuring that only the most relevant information moves forward.
3. Optimized Resources: By the time data reaches the more resource-intensive stages, there's less of it, so we use resources more efficiently.

This funnel approach helps maintain high quality while scaling DSPM. At Cyera, we've perfected this method, ensuring that we're not wasting resources on unnecessary data processing.

Designing for Horizontal Scalability

To handle the vast amounts of data, everything needs to be designed for horizontal scalability. This means that we can add more machines or resources to work in parallel when needed. Here's how Cyera does it:

• Automated Scaling: Compute components and other resources are automatically created and destroyed based on demand. This automation helps us respond quickly without manual intervention.
• Efficient Scaling Up: We scale up resources not too slowly (to avoid bottlenecks) but not too rapidly (to prevent waste). It's a delicate balance that ensures efficiency.
• Pipeline Planning: Our data pipelines are designed to distribute workloads evenly, so no single component becomes a point of failure.

By planning and designing our systems this way, Cyera can handle increasing data volumes without compromising performance or incurring unnecessary costs.

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