Deep Instinct

Cybersecurity for Zero-Day Threats and APT Attacks

Deep Instinct is the first company to apply deep learning to cybersecurity. Harnessing the power of deep learnings predictive capabilities in order to create the ultimate cyber security solution: On- device, proactive solution that protects against zero-day threats and APT attacks with unmatched accuracy. Deep Instinct provides comprehensive defense that is designed to protect against the most evasive unknown malware in real- time, across an organizations endpoints, servers, and mobile devices. Leveraging deep learnings capabilities of identifying malware from any data source results in total protection: Any threat. Anywhere. Any time
Active
November 2014
347
Software
B2B
Launched

people
Alex Haiut
VP Engineering
people
Alon Girmonsky
CEO
AI Expert Serial Entrepreneur
people
Andrey Pokhilko
Chief Scientist
eli-david-pic
Eli David
CTO
AI Expert Serial Entrepreneur

$309,000,000

Series D+

March 7, 2023
Funding Round
PayPal Ventures

Date Announced

Round Stage

Round Size

Lead Investors

September 19, 2022
Pre-IPO
$62,000,000
Chrysalis Investments

Date Announced

Round Stage

Round Size

Lead Investors

July 7, 2021
Series D
$67,000,000

Date Announced

Round Stage

Round Size

Lead Investors

investor
Anne Wojcick
Angel

Investor

Investor Type

Founded

Funds Raised

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ATW Partners
Private Equity
2016

Investor

Investor Type

Founded

Funds Raised

blackrock-logo
BlackRock
Investment Firm
1988

Investor

Investor Type

Founded

Funds Raised

Using deep learning, we are able to identify even the slightest mutations and evasion techniques, thus detect and prevent Zero-Days and APT in real-time. Deep Instinct’s solution is based on a two- phase approach, similar to way that the brain learns and then acts in an instinctive mode: • Training phase: The training process is performed with hundreds of millions of malicious and legitimate files that takes place at Deep Instinct’s headquarters. The output of this process is the prediction model. • Prediction phase: Once a device has the deep learning prediction model (D-Brain), it becomes an autonomous analysis entity, allowing it to predict in real-time malicious intents and prevent them at a pre-execution level. There is no need for any supplementary analysis in a remote server or sandboxing appliance. The entire analysis and the determination of whether it is malicious or benign is done on the device within milliseconds.
Deep Learning
5
Neural Networks
GPU

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