Every organization has an enormous amount of data, in a dizzying array of unpredictable formats, volumes, speed, and in silos across the organization. In most organizations, regardless of the state of digital maturity, that data remains underused and undervalued. However, it contains powerful and valuable insights that allow organizations to detect advanced security threats. Logs generated by your security appliances, network devices, servers, and endpoints can provide a treasure trove of valuable insight and present a deafening amount of unstructured data. This data lake is growing exponentially and has the potential to introduce analysis paralysis. Even with a technology platform that is scalable and flexible enough, you still need to make sense out of it all. Translating these vast and diverse datasets into usable knowledge can be a real work of art.
Bad intelligence is even worse than a lack of it. If we can’t trust the underlying data, our decisions will be flawed. False positives, false negatives, out-of-context intelligence will almost guarantee a bad decision. Syndromes such as alert fatigue or death by ticket are spread by irrelevant or poorly configured use cases and can quickly drain your resources. To avoid this at an early stage, new data sources are best onboarded by experienced engineers.
Data reveals all activity, behavior, (critical) information of your customers and users, transactions, applications, servers, networks, mobile devices, and more. You need to unlock the power of data by extracting the decisive information. With the right technology platform and enablement, it can be quite easy to get new data in. However, a couple of trade secrets are involved. Data sources must be inventoried and different data input methods require careful consideration. Data sources must point to the right indexes to reflect the data jurisdictions within your organization. They must be properly configured and take into account data segregation, replication, and retention requirements. Data modeling is key to report consistently across different technologies and power your analytics use cases.
If we can't trust the underlying data, our decisions will be flawed.
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