Predicting Insider Threats (PIT)™ www.incyber1.com

How Did We Not See Him Coming? Edward Snowden CIA/NSA

The Team 20+ years of experience in startups and fortune 500 companies including NASDAQ IPO of Giltronix inc Former Chairman of the Technology Management Dept. at Jerusalem College of Technology More than 20 years of experience in the fields of Fraud prevention and investigation Risk Project Manager at Price Waterhouse Coopers - Internal fraud prevention Founder and CEO of Ivy League Gradates, a company that disrupted the US MBA applications market International Mergers & Acquisitions and Taxation Specialist for Ernst & Young

69% of breaches are caused by insiders (1) The Problem 69% of breaches are caused by insiders (1) Less than half of companies have a dedicated budget for insider threats (3) >50% 74% of security professionals say they’re organizations are vulnerable to insider thefts (2) 74% 84% of the S&P 500’s value now consists of IP (3) 69% EMA report LinkedIn’s Information Security Community and Crowd Research Partners, 2017 Forcepoint Global Threat Report 2016

$4,295,568 Containment Costs (1) $206,933 $347,130 $493,093 The average total annual cost of containment/remediation for all types of insider threats Average cost per attack: $206,933 Negligent insider $347,130 malicious insider $493,093 Credential Theft Ponemon Institute Cyber report 2016

Current Solutions Over 20 Years of User Behavior Analysis (UBA) solutions  Continued increase in thefts UBA is not a prediction tool  By the time the system reacts under the rule based system the money/IP is long gone UBA does not take into consideration life events of employees UBA does not include industry factor

Initial Prediction Score Critical Prediction Scores InCyber’s 3 Step Solution True Prediction of Insider Threats ™ (TPIT)™ Machine Learning Advanced and Custom Machine Learning algorithms to detect suspicious behavior indicative of planning an inside attacks Initial Prediction Score ™ (IPS)™ Integrative Data inputs of both internal (HR meta data) and external (corporate and public sources) sources Critical Prediction Score (CPS) Using smart mathematics and the Crystal Ball Ranking Method™ we calculate the CPS and report to CISO or CIO Machine Learning Initial Prediction Score Critical Prediction Scores

Who We Are Looking For Irregular Activities User ID #z123w9 Activities cluster of insurance company exec that we caught Irregular Activities User ID #z123w9

Our Strengths Very profitable Only product to predict insider threats Only product to use internal and external information Red flagging is done automatically 25 times less false positives Marking only the truly dangerous employees Implementation can be done in any company No need to study companies’ systems before implementing No Big Data algorithms Product is at least 4 times cheaper than competition Tested on over 12,000 employees and counting Very profitable

GDPR InCyber’s product is inline with the GDPR Processing Information necessary to protect company’s vital interests Employees should receive information about InCyber’s product GDPR asserts that employees have a right not to be subject to a decision made solely by automated processing, and that employers should incorporate human intervention into automated processes that significantly affect employees This is exactly what InCyber does

Market Size -$20B More than 8,000 banks in the US alone(2) Over 105,000 companies worldwide with >500 employees (1) More than 27,000 financial institutions worldwide(1) 14,000 + financial institutions in US (2) DMDatabase 2015 survey Quara.com – 2016 survey

* All monetary figures are in 1,000$ Financials 3 Years financial projection Summary * * All monetary figures are in 1,000$ Years post Inv’ 1 2 3 Direct Clients 16 197 Direct Sales 590 16,787 52,756 Integrator Sales 348 8,550 31,785 Total Revenue 974 25,344 84,541 Expenses 1,438 9,290 18,785 Profit/Loss 494 15,220 63,246 12

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