Abstract Security: Security Analytics Platform

Additional Info

CompanyAbstract Security
Company size (employees)10 to 49
Headquarters RegionNorth America
Type of solutionSoftware


Abstract Security is a revolutionary platform designed to centralize the management of security analytics, allowing analysts to better align security efforts to business value. The complete AI-powered data streaming platform allows security teams to transcend next-gen SIEM solutions. Built by industry veterans who have consistently redefined the cybersecurity landscape, the Abstract platform correlates data in real-time between data streams and separates compliance and security data to both increase detection effectiveness and lower costs.

Today, security analytics is a complex patchwork of products that don’t deliver business value. The majority of data being collected by security analysts is not useful or relevant for detection scenarios. Meanwhile, adversaries are taking less time to compromise organizations (62 minutes, on average), and the average cost of data breaches has risen to 4.45 million, a 15% increase over three years. The SIEM market emerged over 20 years ago with promises to address real-world security challenges, but as the industry evolved, its weaknesses became apparent. Despite advancements like next-gen SIEM and XDR, the category continues to experience growing pains — costs have surged, and security analytics have been reduced to saved searches and dashboards without the power of correlation. Working to surpass the mistakes of next-gen SIEM solutions, Abstract’s mission is to deliver on the promise that the current offerings fall short of while effectively detecting threats and keeping costs low.

Key Capabilities / Features

A key feature of the Abstract Security platform is the Abstract Security Engineer (ASE), which enables security teams to be more effective through automation and AI by helping them navigate the complexities of security data. The ASE is built on leveraging AI, expert systems, machine learning, and subject matter expertise. The two main roles of ASE are to understand and analyze enterprise data needed for detection and consistently improve detection effectiveness. Protecting the business requires an understanding of all the data sources, the intricacies of each one, and the complex cloud environments in which the business operates — the ASE helps organizations better understand this. Effective detection requires both fine-tuned rules and the correct data sources to apply them. The ASE offers a data source expertise that enables true detection effectiveness.
Other key capabilities of Abstract Security include the following:
- Observability pipelines: Abstract separates users’ data for compliance and security use cases to save on costs, increase efficiencies, and supercharge detection capabilities.
- Streaming analytics: Abstract allows customers to move past saved searches and dashboards by offering real-time streaming correlation and machine learning with pre-built and user-defined detection rules.
- Data Lake: Abstract provides a one-click data lake on a privacy-first architecture, so customers don’t worry about scaling storage or losing data to vendor lock-in.

How we are different

The Abstract platform is architected differently to abstract away the operational weight of security platform engineering by:
- Bifurcating compliance and security use cases: The Abstract platform allows for security data to be separated from the compliance and observability data to save money, resources, and time, allowing analysts to focus on the most important, critical threats.
- Providing security analysts with powerful tools: Abstract leverages real-time streaming, correlation, and machine learning with pre-built and user-defined detection rules to show correlation between data streams.
- Reducing immense compute and storage costs: Abstract provides a one-click data lake on a privacy-first architecture that reduces the overall cost of ownership, preventing organizations from losing data to vendor lock-in and paying unreasonable costs due to a high volume of logs.