|eval distance = 6371 * c, time_difference_hours = round((_time - last_time) / 3600,2), speed=round(distance/ ( time_difference_hours),2) |where isnotnull(category) AND last_src != src AND _time - last_time < 8*60*60 | iplocation last_src |streamstats window=1 current=f values(_time) AS last_time values(src) AS last_src by user Sourcetype=sourcetype=aws:cloudtrail user=* Run the following search. You can optimize it by specifying an index and adjusting the time range.Use your data mapping results to build a lookup that associates systems to their system category.Ensure that those assets are configured properly to report logging activity to an appropriate central repository.This includes not only data stores and repositories that house sensitive personal data (PD) and personally identifiable information (PII), but also any technologies that are involved in the processing, storage, transmission, receipt, rendering, encrypt/decrypt, relaying, and handling of such data in any capacity. These are all IT assets that are relevant to the full audit trail of data processing activities. Identify all relevant IT assets from a data mapping exercise conducted by the Data Privacy Officer’s team.I think the default maxspan is 1 day, which can cause a large number of evicted records if you have a large log volume. Using a reasonable maxspan value and startswith will significantly reduce the number of transactions in memory. Or you can calculate those with timechart/stats/chart and get a table of values or a visual representation, and use predict to forecast the values. If you click on the duration field on the left of the events list, it will show the average, minimum, maximum, and standard deviation. `comment("| timechart avg(duration) AS avg_duration, p95(duration) AS p95_duration, max(duration) AS max_duration, min(duration) AS min_duration by cs_username | predict avg_duration p95_duration max_duration")` | eventstats p95(duration) AS p95_duration | eval action = if(match(a_action, "event_status"), "login_complete", action) Depending on your log volume and what you want to see, the following will show the 95th percentile of the time between two events. The transaction command is the simplest way to aggregate related logs. If anyone knows how to tackle this issue at the same time that would be hugely convenient, but one issue at a time will suffice for now ) We can see this from the cs_username field. (However, we do get onto the issue of making sure that we are looking at the same person instance of the product. The difference between these values is all we need, what is the easiest way to calculate this? Is it possible to find the time difference between these two events? I know they both have timestamps, which can be converted in epoch. However, we have come to realize that what actually happens when someone logs in, is that the action=login starts the process, and then another log/event finishes this process, called a_action=event_status In order to work out how long it takes someone to log in, we have simply been using the time_taken field for this action. So far, when someone logs in we have been using the (custom field) value of action=login to view this event. These are events performed by someone who is using a product that we make at the company I work at.
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