Deep Instinct







0.6 years

Deep Instinct is the first company to apply deep learning to cybersecurity. Deep learning is inspired by the brain’s ability to learn. Once a brain learns to identify an object, its identification becomes second nature. Similarly, as Deep Instinct’s artificial brain learns to detect any type of cyber threat, its prediction capabilities become instinctive. As a result, zero-day and APT attacks are detected and prevented in real-time with unmatched accuracy. Deep Instinct brings a completely new approach to cybersecurity that is proactive and predictive. Deep Instinct provides comprehensive defense that is designed to protect against the most evasive unknown malware in real-time, across an organization’s endpoints, servers, and mobile devices. Deep learning’s capabilities of identifying malware from any data source results in comprehensive protection on any device, any platform, and operating system.





Employee counts updated on a monthly basis.

The profile picture of Eli DavidEli DavidCTO
Guy CaspiCEO1
Nadav Maman


RoundDateCapital RaisedInvestors
Deep InstinctFunding Round20231
Deep InstinctPre-IPO2022$62M2
Deep InstinctSeries D2021$67M1
Deep InstinctSeries D2021$100M7
Deep InstinctSeries C2020$43M5
Deep InstinctSeries B2017$32M4
Deep InstinctSeries A2014$5M

ㅤInvestor NameInvestor TypeAUM ($)
Anne WojcickiAngel
ATW PartnersPrivate Equity
BlackRockInvestment Firm
Coatue ManagementVenture Capital$21B
Columbus Nova Technology PartnersInvestment Firm
J-VenturesVenture Capital$30.79M
LG Technology VenturesCorporate Venture Capital$400M
Millennium New HorizonsVenture Capital
NvidiaCorporate Venture Capital
PayPal VenturesCorporate Venture Capital$850M
Shravin MittalAngel
The Tudor GroupInvestment Firm
UnboundVenture Capital
Untitled InvestmentsVenture Capital
Zeev VenturesVenture Capital$1.65B


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.

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