Today, Gathr is excited to announce the launch of our new Confluent Cloud Connector, offering customers the ability to stream their data to and from Confluent Cloud in real time using Gathr. With today’s announcement, customers have a new way to manage their streaming data and derive value from it, 50X faster.
Author: Amit Assudani
Detect and prevent insider threats with real-time data processing and machine learning
Insider threats are one of the most significant cybersecurity risks to banks today. These threats are becoming more frequent, more difficult to detect, and more complicated to prevent. PwC’s 2018 Global Economic Crime and Fraud Survey reveals that people inside the organization commit 52% of all frauds. Information security breaches originating within a bank can include employees mishandling user credentials and account data, lack of system controls, responding to phishing emails, or regulatory violations.
Ignoring any internal security breach poses as much risk as an external threat such as hacking, especially in a highly regulated industry like banking. Some of the dangers of insider threats in the banking and financial industry include:
- Exposing the PII information of the customers
- Jeopardized customer relationship
- Fraud
- Loss of intellectual property
- Disruption to critical infrastructure
- Monetary loss
- Regulatory failure
- De-stabilized cyber assets of financial institutions
Identifying and fighting insider threats requires the capability to detect anomalous user behavior immediately and accurately. This detection presents its own set of challenges such as appropriately defining what is normal or malicious behavior and setting automated preventive controls to curb predicted threats.
Why modernizing ETL is imperative for massive scale, real-time data processing
During the past few years, a sea change occurred in the way enterprises acquire, process, and consume data. The exponential surge in the number of data sources and customer interactions fueled a major paradigm shift, with real-time stream processing and cloud technologies emerging as the backbone of intelligent decision making. This is driving businesses to re-look at traditional extract, transform, and load (ETL) platforms used to integrate data from multiple sources into a single repository. This article explores the need for ETL modernization and provides insights for evaluating ETL platforms and ensuring a seamless modernization journey.