Machine generated log data is growing exponentially making it extremely hard for cybersecurity software to provide real-time defense for the infrastructure. The software needs to analyze Terabytes of data every day generated by servers, applications, and firewalls. With Grovf massively parallel FPGA cores companies analyze and prevent the cyber threat 15x faster, saving critical time for organizations to detect a penetration and counteract almost in real-time.
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Security Log Analytics
Implementing effective cybersecurity measures is particularly challenging today because there are more devices than people, and attackers are becoming more innovative.
Cybersecurity brings dual challenge of low-latency detection and remediation of advanced threats, and batch analysis of log data from servers, firewalls, applications and security systems. Considering how fast new threats and attacks emerge, Big Data performance and the use of new types of software and hardware accelerators is becoming more critical.
Technology is essential to giving organizations and individuals the computer security tools needed to protect themselves from cyber attacks. Three main entities must be protected: endpoint devices like computers, smart devices, and routers; networks; and the cloud.
Grovf's text processing FPGA cores and Open source software SDK provide a effortless way to use powerful FPGA devices for vast amount of security log analysis.
FPGA's powered with Grovf's Regex, Exact Search and Similiarity Search functions provide to organizations to analize hundreds of megabytes of data in real-time and detect security alerts.
Financial Fraud/Risk Analytics
A fraud risk assessment is an essential element of an organization’s fight against fraud.
The topic of risk continues to be of critical importance across financial services segments. While there are many forms of risk, the most common form of risk across all financial segments is surrounding cybercrime and fraud. There is also a post-financial crisis regulatory aspect of risk management that forces lenders to know precisely how much capital they need in reserve. Keep too much and you tie up capital unnecessarily, lowering profit. Keep too little and you run afoul of Basel III regulations.
There is great promise in new big data and machine learning technologies to enable lenders to tap into an ever-deepening pool of new data to analyze all aspects of risk and fraud. Identifying risk and fraud can require huge data volumes and large compute clusters which are typical of modern big data systems.