Copyright 2017, Zighra Inc.

Transcription

1 Zighra core AI based Adaptive Authentication Engine Patent (US Patent# ) 1. What does this cover? Zighra s patent answers the security question who is the real user behind a transaction or device? by combining the strength of Artificial Intelligence, Behavioural biometrics, Sensor analytics and Network/Device/Social Intelligence. By actively evaluating the users unique sensorial signature including the way the user interacts with their mobile device, Zighra provides frictionless authentication and continuous fraud detection.

2 2. Use cases: Use cases include authenticating payment transactions, online commerce, and others on sensor-based smart devices including: PoS mobile and other PoSs using user mobile as a security key ATMs Either authenticating directly on ATM (via user i/p and sensors) or using user mobile as a security key Online apps online banking, Enterprise access, Web access Either authenticating directly leveraging laptop sensors or using user mobile as a security key Mobile apps mobile banking, mobile payments/wallets, enterprise apps Wearables smartwatches, glasses and other wearables - Either authenticating directly leveraging sensors embedded in wearables or using user mobile as a security key IoT connected cars, connected home, Amazon Alexa, and other sensor based devices - Either authenticating directly leveraging on-device sensors or using user mobile as a security key

3 3. User Interface Input Touchscreen inputs (tap, swipe, ) Keyboard inputs (e.g., in Kiosks, Laptops with inbuilt sensors,..) Audio inputs (e.g., Amazon Alexa, voice inputs in cars/in mobile,...) Camera inputs 4. Sensors Login input speed unit Accelerometers Gyroscopes GPS Environmental

4 5. Context Information Device intelligence Network intelligence Location analytics Biometrics analytics Social intelligence User behavior analytics

5 6. Adaptive Authentication Engine (powered by Artificial Intelligence and Machine Learning) Zighra s patent includes an AI powered transaction authentication engine that adaptively learns over time and dynamically calculates a weighting for each input and determines a level of correlation with an adaptive learning model. If correlation is less than a threshold, the authentication is unsuccessful and the transaction authentication information unit initiates an explicit authentication task/step-up o Examples include: Device 1 = connected car Device 2 = user s mobile phone User input = user making payment (or interacting) in-car via the car s voice interface Zighra collects contextual information from Device 1 and Device 2 Zighra s AI engine adaptively learns and creates a unique model and checks correlation Simple correlation check could be What is the geo-location of the user s phone and the geo-location of the car? Another correlation check would be is the car accelerating at the same acceleration of the user s phone? Advanced correlation check could be Does the user s voice based sensor-print match the user s previously created voice sensor-print model Step up authentication

6 7. Step up (Explicit authentication) 2-way rich push notifications 2-way sms Automated voice call or chat between agent and user Automated and/or manual multi-channel communications

7 Point of Contact Zighra point of contact: Name: Deepak Dutt Title: CEO Phone: