[Webinar] How to Protect Sensitive Data with CSFLE | Register Today
Real-Time Security Event Processing
Enhance SIEM with scalable, real-time event data transformation, filtering, enrichment, detection, and aggregation on Confluent. Seamless integration, customizable rules, and cost-effective processing together drive greater efficiency and scalability of security operations.
SIEM Augmentation with Scalable, Real-Time Data Processing
Overview
In today’s digital landscape, organizations face the challenge of monitoring and securing vast and ever-growing amounts of data generated from various sources. Traditional SIEM systems, while effective, often struggle to keep up with the sheer volume and velocity of data, leading to potential delays in threat detection and response. The need for scalable, real-time data processing solutions that can seamlessly integrate with existing security infrastructure is more critical than ever.
Challenges
- Data Volume: Large organizations generate terabytes of data daily, making it difficult for traditional SIEM systems to process and analyze all incoming data in real-time.
- Cost and Resource Efficiency: Processing such large volumes of data can significantly increase operational costs, as organizations may need to invest in additional hardware, storage, and licenses for their SIEM systems.
- Real-Time Threat Detection: Delays in processing can lead to missed or delayed detection of security threats, putting the organization at risk.
Solution
PADAS offers a robust solution by offloading data transformation, filtering, enrichment, and detection tasks from existing SIEM systems. Built on the Confluent platform, PADAS enables organizations to process streaming event data in real-time, significantly improving the efficiency and scalability of their security operations.
Streamline Security Operations with Real-Time, Scalable Data Processing
PADAS revolutionizes security operations by providing a scalable, real-time data processing solution. It efficiently transforms, filters, enriches, detects, and aggregates streaming event data before it reaches your SIEM, ensuring only relevant and actionable data is processed. This approach not only enhances the accuracy and speed of threat detection but also reduces the operational load on your SIEM, optimizing overall security infrastructure.
This approach enables organizations to maintain a strong security posture, even as data volumes continue to grow, by ensuring that their SIEM systems are not overwhelmed and can focus on identifying and responding to the most critical threats. The benefits include:
Handle trillions of messages per day with low latency, ensuring your security operations scale with your data growth without compromising performance.
Reduce operational costs by offloading data processing tasks from expensive SIEM systems, allowing them to focus on more critical analysis.
Improve response times and reduce risks by detecting security threats in real-time, using customizable detection rules tailored to your organization’s needs.
Build with Confluent
This use case leverages the following building blocks in Confluent Cloud:
Reference Architecture
Transform: Process raw streaming data by applying transformations, such as field extraction and data restructuring, to prepare it for further analysis.
Filter: Apply filtering rules to remove irrelevant or redundant data, ensuring only the most pertinent information is passed through the system.
Enrich: Enhance data quality by integrating additional context from lookup files, such as enriching log entries with geographical or organizational information.
Detect: Implement real-time detection of anomalies and threats using Sigma rules or custom detection rules written in PADAS Domain Language (PDL).
Aggregate: Combine and summarize data streams, including Sigma rule-based aggregation with PDL, to provide a consolidated view for deeper analysis or reporting.
Stream Processing: For a complete list of functions, visit https://docs.padas.io/latest/introduction/#task
- APPLY_RULES: Apply predefined rules (per event and/or correlated/aggregated) to streaming events.
- EXTRACT: Extract any event input with provided Regular Expression defition (named groups).
- FILTER: Filter an event (keep or drop) based on PDL or regex definition.
- OUTPUT_FIELD: Outputs the value of a given field.
- PARSE_CEF: Parse input CEF event into JSON.
- PARSE_CSV: Parse input CSV event into JSON.
- PARSE_KV: Parse input key-value pairs event into JSON.
- PDL_EXPRESSION: Allows event data transformation and enrichment via PDL expressions.
- TIMESTAMP: Define a field from within the event data (JSON formatted) to use as the timestamp.
- Data enrichment (e.g. lookup, eval), event matching, and correlation via Padas Domain Language.
Resources
Contact Us
Contact Seynur to learn more about this use case and get started.