First, total factor productivity improvement: AI big model can improve the detection and response speed of security incidents and reduce the dependence on human resources by automatically and intelligently processing a large amount of data. This efficiency improvement is not only reflected in the processing speed, but also in the prediction and prevention of security threats through machine learning and deep learning technology, thus reducing potential security accidents and economic losses.
Second, innovation: the application of AI big model in network security is the embodiment of technological innovation. They can learn and adapt to new attack patterns, and constantly optimize their detection algorithms to cope with increasingly complex network threats. In addition, AI big model can also introduce innovative methods in security policy formulation, risk assessment, security training and so on.
Third, quality advantage: AI big model improves the accuracy of safety detection and reduces false positives and false negatives through accurate data analysis and pattern recognition. This high-quality output enables the security team to focus resources and attention on real threats more effectively, and improves the overall quality of security protection.
Fourth, advanced: AI big model represents the advanced productivity in the field of network security. They use the latest artificial intelligence technologies, such as natural language processing, image recognition and complex event processing, which provide unprecedented analysis and response capabilities for network security. This advanced nature enables enterprises to better cope with emerging security challenges.
Fifth, the technical level is higher: the introduction of AI big model has improved the technical level of network security. They can not only process and analyze large-scale data sets, but also improve their abilities through continuous learning to adapt to the ever-changing network environment.
Sixth, higher efficiency: AI big model improves the efficiency of safe operation by automating processes and optimizing decision support. They can quickly identify and respond to threats and reduce the need for manual intervention, thus reducing the cost and time of safe operation.
7. More sustainable: AI models are sustainable, because they can learn and adapt over time and maintain their relevance and effectiveness in the field of network security. In addition, the scalability of AI big models means that they can be adjusted and expanded with the growth of enterprise scale and the change of threat environment.
Take the convinced security GPT as an example, it has brought "new qualitative change" to network security:
1. has changed from the traditional rule/pattern matching detection mode to a new detection mode based on offensive and defensive intention/knowledge reasoning.
2. From the detection angle of completely different dimensions, we can find advanced threats that are difficult to be covered by traditional engines.
3. From the traditional manual safety operation mode to the automatic safety operation paradigm of man-machine cooperation.
4. Customize the safety operation journey through the dialogue window, bridge the level difference of safety personnel, and carry 80% of safety operation operations, such as platform tool challenges, key information aggregation, and disposal plan thinking.
5.MTTD/MTTR reduced the fixed noise reduction order by 85% to the full-scale item-by-item analysis mode.
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