0
Score
AI Technology Score
4%
Funding Score
74%
Founders Score
100%

Business Overview

Acquired
Status
2014
Total Founded
67
# Employees
$28M
Funding
3
# Rounds
Preempt delivers a one-two punch for securing identities and preventing threats like credential compromise and targeted attacks. We help enterprises optimize their identity health posture to reduce their attack surface and preempt threats in real time. Our patented technology continuously analyzes, adapts and responds to threats based on identity, behavior and risk to auto-resolve incidents.Preempt was founded in 2014 by global security and networking experts with a passion for making IT security teams more effective in protecting their organizations from breaches and internal threats.Preempt delivers a modern approach to authentication and securing identity with the markets first solution to deliver Conditional Access for continuously detecting and preempting threats based on identity, behavior and risk. Preempts patented technology empowers Enterprises to optimize Identity hygiene and stop attackers and insider threats in real-time before they can impact business.
EnterpriseCyber SecurityAuthenticationThreat Detection
Software

Employee Count

Lists

News

Stock Chart

Founders

NamePositionMilitaryClassificationContact
hidden22…
CEO
hidden22…
CTO

AI Team

Board and Advisors

Revenue

Funding Rounds

RoundDateCapital RaisedValuationInvestors
June 2018
hidden$1…
4
May 2016
hidden$8M
4
July 2014
hidden$2M
1

Exits and M&A

TransactionDateTarget/AcquirerConsideration
hidden146183
September 2020
hiddenCrowdStrike/Preempt Security
hidden$96M

Investors

InvestorInvestor TypeLocationPortfolio
hidden93157
Corporate Venture CapitalUnited States49
hidden93189
Venture CapitalUnited Kingdom19
hidden93645
AngelIsrael13
hidden15…
AngelUnited States10
hidden93263
CompanyIsrael6
hidden93289
Private EquityUnited States5

Research

Certifications & Approvals

AI Technology Stack

0
# AI Employees
Preempt machine-learning models. The models get ongoing feedback that allows them to adapt to the unique character of the network. In addition to having supervised ML models, which are trained by data scientists, and unsupervised ML models, which detect deviations from a local baseline, we also use a hybrid technique called semi-supervised machine learning. In this case, the model is trained by real-time feedback from the end-user. This means that individual detections are far more reliable, but also the models themselves become far more attuned to the environment over time. LDAP anomaly detection, through pre-trained machine learning models, automate the detection of sophisticated attack patterns.
Machine Learning

Jobs

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