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PADAS

Real-Time Security Event Streaming and Detection

Purpose-Built for Streaming Security Data

PADAS is a high-performance, Kafka-native engine designed to transform, filter, and detect threats in real-time. With built-in support for OCSF schemas and a powerful domain-specific language (PDL), PADAS helps security teams offload noisy data from SIEMs, normalize events, and run detections directly on the stream—before it hits storage.

Real-Time Security Event Processing

Transform, filter, and analyze terabytes of streaming security data in real-time using a high-performance Kafka-native engine purpose-built for SIEM augmentation.

KafkaConfluentStreams
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Smarter Detection, Lower SIEM Cost

Boost detection accuracy and speed with inline stream processing, while minimizing SIEM data ingestion and storage costs through pre-SIEM filtering and normalization.

MITRE ATT&CKSIEMAI/ML
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Enterprise-Ready & Confluent Certified

PADAS runs natively on Apache Kafka and is certified for Confluent Platform, delivering the reliability and scalability required for critical security operations.

EnterpriseCertifiedVerified
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Explore Capabilities

  1. Core Capabilities

    1. Key Features & Benefits
      1. Real-Time Data Processing

        Transform, filter, and analyze massive volumes of data in real-time, ensuring timely and actionable insights.

      2. Enhanced Threat Detection

        Boost the accuracy and speed of threat detection with efficient data processing, minimizing false positives and improving response times.

      3. Scalable Architecture

        Seamlessly handle terabytes of streaming data without compromising on speed or performance, no matter your data load.

      4. Reduced SIEM Load

        Offload data processing tasks from your SIEM, freeing up resources and reducing operational costs while maintaining high performance.

      5. Cost Optimization

        Streamline your security operations and reduce costs by processing only relevant data, avoiding vendor lock-in, and maximizing SIEM efficiency.

    2. Integration & Flexibility
      1. Flexible Integrations

        Easily integrate with existing platforms and leverage pre-built rules aligned with the MITRE ATT&CK Framework.

      2. Seamless Data Integration

        Automate data transformation from various sources, simplifying complex data pipelines and ensuring smooth data flow between systems.

      3. Vendor Independence

        Retain flexibility and control over your security operations with a platform that integrates with multiple SIEMs, avoiding long-term vendor lock-in.

    3. Future-Proofing & Adaptability
      1. Customizable Rules

        Create and deploy custom detection rules using PADAS Domain Language (PDL), with no dependency on your current SIEM infrastructure.

      2. Scalable and Modular Design

        Expand and adapt your security infrastructure as your organization grows, with a platform designed to evolve with your needs.

      3. Long-Term Flexibility

        Maintain the ability to integrate new technologies and data sources as they emerge, ensuring your security operations remain cutting-edge.

    4. Professional Support & Services
      1. Technical Support

        Assistance with errors or issues in your Padas environment for long-term health.

      2. Professional Services

        Consulting for architecture, deployment, configuration, and operations to ensure a production-ready cluster.

System Architecture

Explore our robust and scalable system architecture designed for high performance and reliability

PADAS Architecture
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HowPADASWorks

Discover the seamless data processing architecture

that powers next-generation streaming

See PADAS in Action

Get a quick overview of how PADAS transforms security event data and detects threats in real-time.

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Try PADAS now

Frequently Asked Questions

What is PADAS?
PADAS is a high-performance, Kafka-native streaming engine designed to transform, filter, enrich, and detect security events in real time. Using the PADAS Domain Language (PDL), it allows scalable event processing, inline detection, and schema normalization—before data reaches your SIEM or data lake.
How does PADAS work?
PADAS processes streaming event data from Kafka topics through real-time pipelines. These pipelines can transform, filter, enrich, aggregate, and detect threats using PDL. Alerts or normalized outputs are written to new Kafka topics. A web UI (PADAS UI) is included to help configure, monitor, and test pipelines.
Who are your competitors?
PADAS complements SIEM and observability platforms. While tools (data/telemetry pipelines) or custom stream pipelines process telemetry, PADAS focuses on real-time threat detection and transformation using a domain-specific language—filling a gap between raw data collection and final analytics.
How does PADAS handle real-time data processing?
PADAS uses a Kafka-based engine to process terabytes of security events in real time. It filters, enriches, correlates, and applies detection rules to data on the fly—minimizing storage needs and accelerating response.
Can I customize detection rules in PADAS?
Yes. PADAS supports custom detection logic through its purpose-built PADAS Domain Language (PDL), which allows various evaluation logicfiltering, aggregation, and temporal correlations across events.
What types of data sources can PADAS integrate with?
PADAS consumes any data ingested into Kafka—including syslog, Windows Event Logs, firewall logs, cloud telemetry, and more. The engine supports regex-based parsing and OCSF schema normalization.
How does PADAS contribute to cost reduction?
PADAS pre-processes data in the stream—reducing noise and normalizing events before forwarding them to SIEM or S3. This lowers ingest volume, reduces storage, and minimizes licensing overhead.
Is PADAS dependent on a specific SIEM vendor?
No. PADAS is completely vendor-agnostic. It integrates with Splunk, Elastic, S3, and any platform capable of consuming Kafka or normalized logs via output connectors.
How does PADAS ensure future-proof security operations?
PADAS supports open standards like OCSF and will evolve toward modular, AI-augmented detection in upcoming versions (v1.1+), including anomaly detection, entity intelligence, and Flink runtime options.
What kind of support is available for PADAS?
You can access official documentation, CLI tools, and GitHub resources. Direct support and advisory services are available for enterprise deployments. Please contact us for details.
Can PADAS improve the accuracy of threat detection?
Yes. PADAS applies real-time filtering and correlation logic directly on the stream, improving fidelity and reducing false positives before logs hit your SIEM or analyst dashboards.
How can I get started with PADAS?
You can download the latest release of PADAS Engine and PADAS UI from our website. Follow the documentation to deploy it locally, in your lab, or on production Kafka clusters.
How is PADAS different from logging or analytics solutions?
PADAS is not a log storage or search solution. It operates as a pre-processing and detection layer in front of your SIEM—optimizing ingestion, reducing costs, and enabling real-time detection on streaming data.

Built for Security Practitioners, by Security Engineers

Our Vision

To redefine how security teams work with data—making real-time, intelligent analytics accessible across any streaming platform. We envision a future where AI-enhanced detection transforms security from reactive defense to proactive insight, with simplicity and performance at its core.

Real-Time Processing
Advanced Analytics

Our Mission

PADAS delivers a Kafka-native, real-time analytics platform that simplifies detection, transformation, and enrichment of streaming security data. Built to reduce SIEM load and enhance operational efficiency, PADAS is evolving to support additional streaming platforms and AI-powered analytics—enabling defenders to do more with less.

Automation
Scalability

Why Padas?

PADAS was born out of real-world frustrations with traditional SIEM deployments—especially the limits of real-time detection in tools.

After years of hands-on experience implementing Splunk, Elastic, and Kafka-based pipelines, we saw a consistent problem: defenders couldn't get the visibility they needed fast enough. Customers struggled to ingest critical telemetry like endpoint or Sysmon data, making detection rules—and even our own MITRE ATT&CK-based content—ineffective.

So, we built PADAS to solve this at the source: a streaming analytics layer that transforms, filters, and enriches security data before it hits the SIEM with detection logic, which allows you to get more value from your existing tools.