![]() The data originates externally and travels inwards, preventing unauthorized access to the entire system. This design enhances security, as even if a hacker were to compromise or gain control of the management server, their influence would be limited. Notably, data traffic consistently flows from external sources toward the central management hub. Subsequently, this data is relayed upward, filtered, and then once again transmitted to the main management server. In the current setup, Splunk employs multiple collectors to gather data. However, this architecture lacks inherent security measures. This involves establishing connections from the main management server to every individual leaf and subsequently, to each lower-level server. Currently, our management server operates using a top-down approach. Consequently, efficient management of these servers becomes imperative. Splunk provides automation for large-scale environments where numerous servers are present. It would highlight their genuine intention to provide support, streamline operations, and maximize the potential of this technology for individuals and their respective companies. This strategic move would demonstrate their commitment to customers beyond just financial gain. Therefore, if Splunk integrated a service dedicated to system optimization and pricing, focusing on essential monitoring data while eliminating less crucial elements, it could potentially lead to cost savings for the customers. Oftentimes, clients are uncertain about their actual data needs. The challenge lies in the fact that we don't always require all the amassed data. This aspect seems lacking as our expenses increase with higher data connectivity, seemingly without much consideration, as this translates to increased revenue for them. Splunk could enhance its services by providing more comprehensive professional assistance aimed at optimizing our investment. This can be done using AI tools like ChatGPT, which will understand the context of what the user is trying to approve and give suggestions based on it. It will be able to give more context to the user regarding how they should approach the query. Even if users have zero knowledge, they can get comfortable with Splunk much more easily if an AI tool helps them write a query or search for any indexes or data models. It can help in a way to understand the context of what the user is trying to write, which will be very helpful for ongoing operations. If a user is struggling, they can just ask an AI tool what they are trying to do with a query, and then it can suggest how a query can be written for a particular user. It would be great if I could have a certain dialogue box in Splunk that uses innovative AI tools like ChatGPT, which are available now in the tech department. The same thing happens with Sentinel, where you select certain things, and it will create a query for you. I've recently started working for the past three months on Sentinel. ![]() I have seen users struggle with Splunk just because of the language they've used to create it. Splunk Enterprise Security has a learning curve that needs to be improved.
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