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What is Tarsal?

Tarsal offers boundless scalability, ensuring that as your business grows, it can effortlessly accommodate your evolving requirements. With just a single click, Tarsal allows you to change where your data is directed; for instance, data that functions as SIEM information today can be repurposed as data lake content tomorrow. This means you can sustain your SIEM while progressively transitioning your analytics to a data lake without the hassle of a complete system revamp. Although some analytics might not integrate smoothly with your existing SIEM, Tarsal equips you to have data prepared for queries in a data lake setting. Recognizing that your SIEM incurs considerable costs, leveraging Tarsal to shift some of that data to your data lake can serve as a financially wise decision. Tarsal distinguishes itself as the pioneering highly scalable ETL data pipeline tailored specifically for security teams, enabling swift exfiltration of extensive data volumes with minimal effort. Thanks to its immediate normalization capabilities, Tarsal facilitates the efficient routing of data to any chosen destination, revolutionizing data management to be more straightforward and effective. This adaptability not only allows organizations to optimize their resources but also significantly enhances their data handling efficiency, ultimately leading to improved operational performance.

What is Auguria?

Auguria stands at the forefront of cloud security data platforms, combining human insight and machine learning to analyze vast amounts of log data in real time, isolating the vital 1 percent of security events through processes of cleansing, denoising, and ranking. The platform's core feature, the Auguria Security Knowledge Layer, functions as both a vector database and embedding engine, crafted from an ontology built on substantial real-world SecOps experience, which enables it to categorize trillions of events semantically into actionable insights for thorough investigations. Users benefit from the ability to effortlessly connect any data source into a streamlined automated pipeline that intelligently prioritizes, filters, and channels events to various outlets such as SIEM, XDR, data lakes, or object storage, all without the necessity for specialized data engineering expertise. Auguria continuously refines its sophisticated AI models with up-to-date security signals and context tailored to different scenarios, providing not only anomaly scoring and explanations for every event but also real-time dashboards and analytics that enhance the speed of incident triage, promote proactive threat hunting, and ensure compliance with regulatory standards. This holistic strategy not only simplifies the security workflow but also equips organizations with the tools they need to respond more adeptly to emerging threats and vulnerabilities, fostering a resilient security posture. Ultimately, Auguria empowers teams to make informed decisions rapidly, bolstering their overall cybersecurity efforts.

Media

Media

Integrations Supported

Amazon S3
Elastic Cloud
Okta
Snowflake
Box
Cato SASE
Facebook
Intercom
Microsoft 365
Opal Security
PayPal
Persona
Superhuman Docs
Tines
Zendesk
Zoom

Integrations Supported

Amazon S3
Elastic Cloud
Okta
Snowflake
Apache Kafka
Azure Storage
MySQL
SentinelOne Singularity

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

Tarsal

Company Location

United States

Company Website

tarsal.co

Company Facts

Organization Name

Auguria

Date Founded

2022

Company Location

United States

Company Website

auguria.io

Categories and Features

Data Pipeline

Not specified

Data Security

Not specified

Categories and Features

IT Security

Not specified

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