The Platform

Enable your security and operations teams to proactively identify, prioritize, and remediate exposures to stay ahead of attackers.

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GigaOm Radar for Attack Surface Management 2024

The expansion of an organization's attack surface continues to present a critical business challenge. Download the GigaOm Radar for Attack Surface Management to get an overview of the available ASM solutions, identify leading offerings, and evaluate the best solution for you.  More...

Use Cases

The CyCognito platform helps you identify all of the attacker-exposed assets in your IT ecosystem for a complete view of your attack surface.

Real World Solutions Assess Your Security Effectiveness Evaluate Merger & Acquisition Risk Manage Your Attack Surface
  Monitor Subsidiary Risk Prioritize & Eliminate Attack Vectors Scale Your Pen Testing Simplify Compliance Initiatives
State of External Exposure Management

Download CyCognito’s State of External Exposure Management Report to learn key recommendations that your Security teams can implement to improve their exposure management strategy and minimize risk. More...

Our Customers

External attack surface management is advancing cybersecurity into a new era. Learn how security experts across all industries benefit from using CyCognito’s platform.

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The Total Economic Impact™ of The CyCognito Platform

Read The Total Economic Impact™ of The CyCognito Platform — a Forrester study. Cost Savings And Business Benefits Enabled By The CyCognito Platform. More...

About CyCognito

We believe all organizations should be able to protect themselves from even the most sophisticated attackers.

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Resources

The knowledge you need to manage and protect your attack surface.

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Learning Center Application Security Attack Surface Exposure Management Vulnerability Assessment Vulnerability Management
EASM 101 External Attack Surface Management Attack Surface Management Attack Surface Discovery
  Attack Surface Protection Attack Surface Reduction Security & Compliance Glossary

Machine Learning

Machine learning is a branch of artificial intelligence describing the study of computer programs that leverage algorithms and statistical models to improve automation without explicit programming. This is used to improve the capabilities of a machine, software, or program by allowing it to essentially program itself using data.

Machine learning can be broken down into three major components: a decision process, an error function and model optimization. The decision process uses an algorithm to make predictions or classifications. The error function evaluates the efficacy of the prediction. Finally, the model optimization process iterates the data and outcome, adjusting different weights until it fits into a certain degree of accuracy.

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