Combating Cybercrimein the Healthcare Industry

whitePaper | August 30, 2022

Access to the user’s data requires using the User Master Password which is only known by the user. It is used to generate the symmetric Advanced Encryption Standard(AES) 256-bit key for encryption and decryption of the user’s personal data on the user’s device. We useWebcrypto API for most browser based cryptography and the native libraries for IOS and Android.

whitePaper | October 20, 2022

Organizations continue to face a daunting challenge. Email is simultaneously the most important business communication tool and the leading attack vector for security breaches. The ubiquitous, and casual, use of email makes it the perfect avenue to deliver threat-centric content, insert malware into corporate systems, steal data, and extort money.

whitePaper | October 26, 2022

At Adobe®, we know the security of your digital experiences is important. Security practices are deeply ingrained into our internal software development and operations processes and tools and are rigorously followed by our cross-functional teams to prevent, detect, and respond to incidents in an expedient manner.

whitePaper | September 22, 2022

Ransomware is one of the fastest-growing and most costly cyber threats. According to a recent Perimeter 81 survey of over 500 IT professionals, 76% of respondents reported that their company experienced an attack within their organization.

whitePaper | August 24, 2022

In the last couple of years, “zero trust” has gained significant traction in the cybersecurity realm as a means to protect networks and increase security across organizations. In part, the growing popularity of this security model can be attributed to the shift to hybrid working practices that call for a more secure work environment whether on- or off-premises.

whitePaper | December 4, 2019

This paper describes a distributed anomaly detection approach designed to accurately track actions performed by adversaries across a network. The illustrated approach involves running multiple machine learning models on both endpoints and the network as well as on a centralized backend, and includes communication and replication of learned states between these models.