Defend Zero Day Attacks

Garner holistic visibility across development and deployment life cycle. Mitigate risks proactively to foil attacks with our most advanced and sophisticated CNAPP product.

Open Source

AccuKnox is the first 5G Security-ORAN to be published on Nephio

From fortifying the control plane to addressing vulnerabilities in the data plane, read the white paper and discover the crucial steps we need to take in order to enhance the security of 5G networks.

Cloud Native Security Redefined

Accelerate your cloud journey with our battle-tested expertise, delivering a comprehensive zero trust framework that safeguards cloud infrastructure and applications from targeted attacks.

Open Source

KubeArmor is now certified Redhat Openshift Operator

Embracing the Power of Open Source: We are proud to contribute to the open-source community, allowing businesses to leverage the strength of KubeArmor to safeguard their containerized environments.


Scaling SPADE to “Big Provenance”

Leveraging Data Provenance Middleware for Large-Scale Applications

This 8-page document explores SPADE, an open-source data provenance middleware, and its adaptation for handling large datasets. It discusses challenges, techniques, and successful implementations in various domains, showcasing its effectiveness in handling large-scale provenance data.

What is Included In this Technical Paper:

Core concepts and capabilities of provenance middleware.

Ways in which SPADE enables individuals and applications to capture, store, and query records representing computational processes and data artifacts.

Two case studies that highlight the actual applicability of SPADE in dealing with enormous provenance datasets.

Collection/queue and integration challenges in managing provenance at scale, which are critical for building successful solutions.

SPADE’s solution to these difficulties includes transformers, content-based integration, and storage screening, all of which improve provenance management and system performance.

This technical paper investigates the advantages of employing SPADE to handle large provenance datasets, offering insights and techniques for scholars, data scientists, and industry experts. The aim is to extend the data provenance middleware’s full capability for projects and applications.

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Download the Technical Paper

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