The threat landscape is continually evolving, with increasingly complex, zero-day malware that traditional security measures are unable to keep up with. As a result, security specialists anticipate that the cost of cybercrime will boost security expenditures by up to 16 times by the end of 2019, reaching $2.1 trillion. To keep ahead of today’s fast IT trends, it is vital to include artificial intelligence (AI) in an organization’s network security strategy.
The goal of AI is to replicate the analytical processes of human intellect while allowing decision making at the machine’s pace. The most successful artificial intelligence uses a deep learning model based on an artificial neural network (ANN).
This network is made up of hardware and software that has been constructed in accordance with neuronal models found in the human brain. This architecture not only expedites analysis and decision making, but also allows the network to adapt and change in response to new inputs.
To do this, an ANN undergoes a machine learning (ML) training process in which huge and more sophisticated volumes of information are carefully fed into implanted learning models. After identifying patterns and problem-solving techniques, the system is given fresh information that allows it to alter its algorithms in order to adapt and discover new tactics and capabilities used by malware or an attack vector.
Fortinet began creating a self-evolving threat detection system six years ago as an early adopter of AI. This system employs a custom-designed ANN made up of billions of nodes that are rigorously trained with fresh threat data every day, giving it a huge competitive threat information edge over other security market suppliers.
The FortiGuard Labs team is now using this powerful AI technology to evaluate files and URLs and categorize them as safe or dangerous-at machine speeds and with great accuracy. The threat intelligence generated by FortiGuard AI has grown so rapidly and dependable that it is now integrated as a major component of every solution, including the FortiWeb web application firewall.
Many cybersecurity firms claim to have integrated AI capabilities into their products. However, most can not attain genuine AI because their infrastructure is insufficient or their learning patterns are insufficient. Others refuse to reveal their methodology, raising questions about the dependability of their AI. Instead, Fortinet chooses to be more upfront about its technique, so that consumers understand the parameters of the study involved.
To begin with, the best learning requires data. Therefore, in order to handle a problem as complex as the present threat landscape, enormous volumes of data must be collected on a continual basis to provide ANN with the information it needs to adapt and enhance rules in real time. Fortinet excels in this area as well.
Fortinet collects data from over 4 million worldwide security sensors. This data is then analyzed by an artificial neural network (ANN), which scans files on more than 5 billion nodes to detect distinct clean or dangerous properties. This enables us to develop detecting skills, which are subsequently integrated into our portfolio of goods.