Preempt Security

Authentication, Cyber Security, Enterprise
Machine Learning

Business Overview

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 market’s first solution to deliver Conditional Access for continuously detecting and preempting threats based on identity, behavior and risk. Preempt’s patented technology empowers Enterprises to optimize Identity hygiene and stop attackers and insider threats in real-time before they can impact business.

Operating Status
July 2014
Offering type
Funding stage
Series B
Total funding
$27.5 Million
Authentication, Cyber Security, Enterprise, Threat Detection


Preempt Security
Roman Blachman
Preempt Security
Ajit Sancheti

Funding Rounds

Calculated as the average ratio between the current funding round amount raised and the previous funding round amount raised. Note that 'plus' rounds are summed together. i.e. Series A = Series A and Series A+.
Funding Growth Multiple

New wpDataTable

Date Announced

Funding Round

Amount Raised


July 2014
$2 M

Exit Events and Acquisitions

Date Announced





September 2020
$96 M

AI Technology Stack

AI Description

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.

AI types
Machine Learning
ML types
Semi-Supervised Learning, Supervised Learning

In The News

CrowdStrike acquires zero-trust cybersecurity startup Preempt Security for $96M | SiliconANGLE

September 24, 2020

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