{"id":24282,"date":"2023-06-22T12:19:30","date_gmt":"2023-06-22T10:19:30","guid":{"rendered":"https:\/\/smartfense.com\/sin-categorizar-en\/success-story-hiding-software-generated-statistics-false-positives\/"},"modified":"2024-06-19T21:01:37","modified_gmt":"2024-06-19T19:01:37","slug":"success-story-hiding-software-generated-statistics-false-positives","status":"publish","type":"post","link":"https:\/\/smartfense.com\/en\/blog\/success-story-hiding-software-generated-statistics-false-positives\/","title":{"rendered":"Success story: Hiding software-generated statistics (false positives)"},"content":{"rendered":"

As we explained in our post: Do you really know what a Phishing simulation is?<\/a> Are you sure?<\/a>, the Whitelist process is used, among other things, to prevent security tools from interacting with Phishing simulation emails generating statistics on behalf of users (false positives). <\/strong>You can refer to that post to learn more about software-generated statistics.<\/p>\n

While the Whitelist process can resolve this situation, its implementation requires:<\/p>\n

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  1. Know with certainty what security technologies are used in the organization.<\/li>\n
  2. That each of the technologies used provides the possibility of configuring a Whitelist.<\/li>\n<\/ol>\n

    It seems to be increasingly difficult for both points to be fulfilled.<\/p>\n

    To keep in mind: many organizations think they have point 1 solved, but then end up being surprised when they discover a large number of unexpected IP addresses automatically interacting with their phishing simulation emails.<\/em><\/p>\n

    In addition, there are organizations that do not wish to implement a Whitelist directly, for various internal reasons.<\/p>\n

    So, what do we do with software-generated statistics (false positives)?<\/strong> How can we obtain reliable results in a simulation if there are interactions that have nothing to do with the behavior of our users?<\/p>\n

    At SMARTFENSE we offer a complementary (and for some organizations, alternative) solution to the Whitelist process which is the Output of software-generated statistics<\/strong>.<\/p>\n

    This solution allows:<\/p>\n

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    • Detect interactions generated by a software tool and define a time interval after which interactions become invalid.<\/li>\n
    • Set filters at the user agent and IP address level.<\/li>\n<\/ul>\n

      While stat hiding is not set up overnight<\/strong>, but requires a process of refining over time, currently allows many organizations with Whitelist can obtain reliable results in their simulation campaigns<\/strong>, to the point of using these results to make decisions about users<\/strong> who fell into cheating (such as applying certain penalties).<\/p>\n

      In the following video we talk about this relevant topic with Ricardo Rojas, former CISO of a large corporation in Chile, who successfully implemented statistics hiding in his organization. As of today, this organization continues to take action against users<\/strong> who fall for simulated traps because they fully trust the results obtained<\/strong>.<\/p>\n