Vectra AI Inc. Case Studies American University jettisons signatures and open-source tools for network detection and response

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	American University jettisons signatures and open-source tools for network detection and response Vectra AI Inc.

American University jettisons signatures and open-source tools for network detection and response

Vectra AI Inc.

Technology Category	Analytics & Modeling - Machine Learning Cybersecurity & Privacy - Network Security Networks & Connectivity - Network Management & Analysis Software
Applicable Industries	Education
Applicable Functions	Business Operation
Use Cases	Cybersecurity
Services	System Integration Data Science Services
Challenge	American University, a private institution in Washington D.C., was preparing to expand its cloud presence and needed to enhance its cybersecurity measures to protect its public cloud, data center, and campus networks. The university was facing two significant cybersecurity challenges that were consuming a significant amount of time and resources. The first was the use of open-source tools to monitor network traffic, and the second was the use of signatures to detect intrusions. The university's network supports about 60,000 users with more than 20,000 devices at any given time, along with 700 servers and hundreds of applications. The information security team was looking for non-open-source solutions that utilized artificial intelligence and aligned with their goals. Read More
About Customer	American University is a private institution located in Washington D.C. The university supports a network of about 60,000 users with more than 20,000 devices at any given time. It also supports 700 servers and hundreds of applications. The university was preparing to expand its cloud presence and needed to enhance its cybersecurity measures to protect its public cloud, data center, and campus networks. The university was using open-source tools to monitor network traffic and signatures to detect intrusions, both of which were consuming a significant amount of time and resources. Read More
Solution	After evaluating various vendors, American University chose the Cognito® Network Detection and Response (NDR) platform from Vectra®. The Cognito NDR platform uses AI-derived machine learning algorithms to automatically detect, prioritize, and respond to in-progress attack behaviors that pose the highest business risk across cloud, data center, IT, and IoT networks. The university deployed Vectra virtual sensors on every VMware server to monitor all traffic between servers. The Cognito NDR platform automated manual Tier-1 and Tier-2 security tasks, reducing the security analyst’s workload and allowing them more time to focus on incident investigations and threat hunting. The platform also integrates and shares context and insights with third-party security solutions for end-to-end threat management and visibility. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Cybersecurity & Privacy - Network Security

Networks & Connectivity - Network Management & Analysis Software

Applicable Industries

Education

Applicable Functions

Business Operation

Use Cases

Cybersecurity

Services

System Integration

Data Science Services

Challenge

American University, a private institution in Washington D.C., was preparing to expand its cloud presence and needed to enhance its cybersecurity measures to protect its public cloud, data center, and campus networks. The university was facing two significant cybersecurity challenges that were consuming a significant amount of time and resources. The first was the use of open-source tools to monitor network traffic, and the second was the use of signatures to detect intrusions. The university's network supports about 60,000 users with more than 20,000 devices at any given time, along with 700 servers and hundreds of applications. The information security team was looking for non-open-source solutions that utilized artificial intelligence and aligned with their goals.

About Customer

American University is a private institution located in Washington D.C. The university supports a network of about 60,000 users with more than 20,000 devices at any given time. It also supports 700 servers and hundreds of applications. The university was preparing to expand its cloud presence and needed to enhance its cybersecurity measures to protect its public cloud, data center, and campus networks. The university was using open-source tools to monitor network traffic and signatures to detect intrusions, both of which were consuming a significant amount of time and resources.

Solution

After evaluating various vendors, American University chose the Cognito® Network Detection and Response (NDR) platform from Vectra®. The Cognito NDR platform uses AI-derived machine learning algorithms to automatically detect, prioritize, and respond to in-progress attack behaviors that pose the highest business risk across cloud, data center, IT, and IoT networks. The university deployed Vectra virtual sensors on every VMware server to monitor all traffic between servers. The Cognito NDR platform automated manual Tier-1 and Tier-2 security tasks, reducing the security analyst’s workload and allowing them more time to focus on incident investigations and threat hunting. The platform also integrates and shares context and insights with third-party security solutions for end-to-end threat management and visibility.

Impact #1	The Cognito NDR platform provided visibility into behaviors across the full lifecycle of an attack.
Impact #2	The platform increased overall efficiency in processing data.
Impact #3	The platform offered greater protection for critical university assets.

Impact #1

The Cognito NDR platform provided visibility into behaviors across the full lifecycle of an attack.

Impact #2

The platform increased overall efficiency in processing data.

Impact #3

The platform offered greater protection for critical university assets.

Benefit #1	Shifted approximately 25% of Tier-2 analyst work to Tier-1 analysts.
Benefit #2	Reduced the time to respond to threats by about 20%.

Benefit #1

Shifted approximately 25% of Tier-2 analyst work to Tier-1 analysts.

Benefit #2

Reduced the time to respond to threats by about 20%.

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Overview

American University jettisons signatures and open-source tools for network detection and response

Operational Impact

Quantitative Benefit