AI+Double Engine: Fortinet sets a new benchmark for network security operations

The vigorous development of artificial intelligence (AI) technology has injected new vitality into the field of network security and significantly improved the intelligence, automation and efficiency of enterprise network security operations. This change has effectively alleviated the problems faced by enterprises in terms of network security management, such as lack of talent, frequent false positives and omissions, and delayed response. However, the practical application of AI in network security operations still faces many challenges, which require network security companies to continuously explore and overcome. In this regard, Fortinet has a trump card to “challenge”.

AIEmpowering network security operations to implement challenges

AI does face challenges when implementing network security operation solutions, which may affect whether the AI ​​system can be smoothly integrated into the existing network security system and directly affect the effectiveness and efficiency of the entire network security operation. At present, the challenges are mainly concentrated in the three aspects of “integration and compatibility”, “real-time and performance”, and “network security operation workflow integration”.

First, integration and compatibility are a big pain point. Enterprise cybersecurity systems are often composed of multiple components and subsystems, which may use different technical standards, data formats and architectures. Such as SIEM (security information and event management) systems, firewalls, intrusion detection systems, etc. How to ensure that the AI ​​system can smoothly connect with these existing systems and match data such as log information, alarm information, response operations, communication methods, etc. is the primary consideration. This is not only related to whether the AI ​​system can operate normally, but also directly affects the stability and effectiveness of the entire network security system.

Second, real-time and performance are critical to cybersecurity operations. When a cyberattack occurs, timely response and handling are the key to reducing losses. For example, in an intrusion detection system, the AI ​​model needs to receive network traffic data in real time and analyze it in real time to detect whether there are abnormal behaviors or attack patterns. What’s special is that intelligent network security scenarios not only require AI systems to have efficient algorithms and computing frameworks, but also the network and security performance of underlying firewalls and other equipment cannot be ignored.

Finally, the convergence of cybersecurity operations workflows is critical. Cybersecurity operations often involve multiple tools and platforms, such as SIEM, SOAR, firewalls, etc. Integrating AI technology into these tool chains requires ensuring that the AI ​​system can seamlessly connect with other tools, share data and collaborate on processing. This requires the existing tool chain to be transformed and optimized to adapt to the needs of AI technology.

Fortinet Intelligent Dual Engine Leads the Threat Detection Revolution

Fortinet has always been at the forefront of AI cybersecurity. As early as more than ten years ago, Fortinet FortiGuard Labs used AI technology to automatically handle billions of threat events every day, greatly reducing manual burden. Entering the new era of generative artificial intelligence (GenAI), Fortinet is the first to apply advanced technologies such as deep neural networks and AIGC large models to the field of network security, receiving and processing data from tens of millions of Fortinet security devices and systems around the world every day. Security data and security requests. After careful sorting and analysis by the FortiGuard AI laboratory, these data are eventually transformed into threat intelligence, empowering various networks and security devices around the world, forming a complete closed loop of detection, analysis and response.

Currently, AI technology has been deeply integrated into dozens of Fortinet products and solutions. In the latest version of the unified network security operating system FortiOS 7.6, the AI-based anti-virus engine developed by FortiGuard AI Lab has been built-in. The engine uses neural network technology to learn and differentiate between massive virus files and normal files. Through classification and scoring, it can accurately judge unknown files and viruses without relying on traditional virus signature libraries. Therefore, Fortinet's FortiGate and other products have dual-engine support in terms of anti-virus. They have both the traditional anti-virus engine based on the virus signature library and the next-generation anti-virus engine (Anti-virus NGAV) based on AI. Double protection and security No worries.

In addition, AI technology is integrated into multiple products and functions of Fortinet, forming an efficient workflow. Take threat detection and response as an example. When FortiGate's dual engines cannot determine the legitimacy of a file, it can submit the file to FortiNDR for in-depth analysis and use cloud deep neural network AI to conduct 0-Day attacks, ransomware, etc. Deep analysis. If you are still unsure, you can further submit it to FortiSandbox for simulation operations in a virtual machine environment for more in-depth detection. With the support of this series of processes and technologies, Fortinet's threat detection and response capabilities have reached the top level in the industry and are moving towards a perfect score of 100.

FortiAI fully intelligentizes network security operations throughout the workflow

Building on the foundation of excellent dual-engine threat detection, FortiAI (formerly known as Fortinet Advisor) based on GenAI technology has now become a powerful assistant for enterprise network security operations, demonstrating unparalleled reliability. With its advanced generative AI capabilities, FortiAI not only supports OpenAI and Google Bard (FortiSOAR only) cloud engines, but can also seamlessly connect with other large language models. It cleverly integrates Fortinet FortiGuard's threat intelligence, product knowledge and use case data, greatly improving the intelligence level of the public AI engine, providing users with concise and highly context-aware prompts and directly actionable guidance.

At present, the GenAI assistant based on FortiAI has been perfectly integrated into solutions such as FortiAnalyzer, FortiSIEM and FortiSOAR, bringing unprecedented enhancements to security operations teams. These capabilities not only help teams make more informed decisions, they also enable teams to respond to cyber threats more quickly by simplifying complex tasks. Specifically, FortiAnalyzer has been significantly enhanced in threat detection, incident investigation, incident response, and natural language commands; FortiSOAR has also been significantly improved in alarm analysis, information prompts, privacy protection, and record forensics; and FortiSIEM Enhancements have been implemented in threat query, asset and behavior information query, and event summary.

Fortinet is committed to continuously developing and expanding the application scope of FortiAI and other AI-enhanced solutions across its product portfolio. This includes providing similar GenAI capabilities for WAN and LAN infrastructure, designed to assist network operations teams so that even novice users can easily achieve their desired goals with the power of GenAI. By integrating FortiAI-powered FortiManager into key operational workflows, operations teams will have full control over the entire cybersecurity operations lifecycle. These features not only provide conversational access to documents, help users easily get onboarded, accelerate configuration and network design, and optimize troubleshooting steps, but also improve monitoring and network insights, enable data-driven baseline and alarm threshold setting, and network management. Team expansion and enhancement.

Article source: Feita