Matthew Finn

Navindra Yadav

Supreeth Hosur Nagesh Rao

Bharathwaj Sankara Viswanathan

Alok Lalit Wadhwa

Aria Rahadian

Vasil Dochkov Yordanov

Ravi Sankuratri

Zhiwen Zhang

Yiwei Wang

Ravi Shanker Prasad

Paul John Lesiak

Muhammada Furqan

Soumyadeep Choudhury

Udayan Joshi

Brady Schulman

Manish Arigala

In one aspect, a method for implementing a cloud-platform push for one or more known data breaches includes the step of, for each data breach of the one or more known data breaches, providing a functionality that maps one or more kill chains or Tactics, Techniques, and Procedures (TTPs) for a specified set of security dimensions. A step includes generating a security rule for each mapped kill chain or TTP based on the functionality that maps the one or more kill chains or Tactics, Techniques, and Procedures (TTPs) for a specified set of security dimensions. A step includes pushing the security rule to an enterprise so that the enterprise is aware of a vulnerability in the data breach. A step includes generating a customized posture for a Virtual private cloud (VPC) in the enterprise's cloud-based network. A step includes determining that the data breach can occur within the enterprise's cloud-based network.

Cloud-platform push for known data breaches

In one aspect, a computerized method for automatically identifying and solving for vendor data abuse in an enterprise network, includes the step of implementing a vendor detection at one or more gateways of the enterprise network. The method includes the step of mapping a set of data along with any associated data attributes of the set of data that are being shared with a vendor via the one or more gateways. The method includes the step of detecting and identifying an access anomaly with respect to the set of data associated with a vendor access. The method includes the step of implementing a specified data minimization process to the access anomaly.

Methods and systems for prevention of vendor data abuse

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Lebanon, GA, United States of America

Matthew Finn