Abstract: Systems and methods are disclosed to implement a delta data collection technique for collecting machine characteristics data from client machines. In embodiments, the collected data is used by a machine assessment service to maintain a virtual representation of the client machine for assessments. To initialize the collection process, the client uploads an initial copy of the data in full. Subsequently, the client determines periodic deltas between a current baseline of the data and a last reported baseline, and the deltas are uploaded as patches. The machine assessment service then applies these patches to update the virtual representation of the client machine. In embodiments, to facilitate the generation or uploading of the patches, the client may generate the baselines in a different encoding format as used by the data. For example, baselines in the new encoding format may be more easily compared and manipulated during the patch generation process.

Abstract: Systems and methods are disclosed to implement a self-learning machine assessment system that automatically tunes what data is collected from remote machines. In embodiments, agents are deployed on remote machines to collect machine characteristics data according to collection rule sets, and to report the collected data to the machine assessment system. The machine assessment system assesses the remote machines using the collected data, and automatically determines, based on what data was or was not needed during the assessment, whether an agent's collection rule set should be changed. Any determined changes are sent back to the agent, causing the agent to update its scope of collection. The auto-tuning process may continue over multiple iterations until the agent's collection scope is stabilized. In embodiments, the assessment process may be used to analyze the remote machine to determine security vulnerabilities, and recommend possible actions to take to mitigate the vulnerabilities.

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Abstract: Systems and methods are disclosed to implement a self-learning machine assessment system that automatically tunes what data is collected from remote machines. In embodiments, agents are deployed on remote machines to collect machine characteristics data according to collection rule sets, and to report the collected data to the machine assessment system. The machine assessment system assesses the remote machines using the collected data, and automatically determines, based on what data was or was not needed during the assessment, whether an agent's collection rule set should be changed. Any determined changes are sent back to the agent, causing the agent to update its scope of collection. The auto-tuning process may continue over multiple iterations until the agent's collection scope is stabilized. In embodiments, the assessment process may be used to analyze the remote machine to determine security vulnerabilities, and recommend possible actions to take to mitigate the vulnerabilities.

Abstract: Systems and methods are disclosed to implement a self-learning machine assessment system that automatically tunes what data is collected from remote machines. In embodiments, agents are deployed on remote machines to collect machine characteristics data according to collection rule sets, and to report the collected data to the machine assessment system. The machine assessment system assesses the remote machines using the collected data, and automatically determines, based on what data was or was not needed during the assessment, whether an agent's collection rule set should be changed. Any determined changes are sent back to the agent, causing the agent to update its scope of collection. The auto-tuning process may continue over multiple iterations until the agent's collection scope is stabilized. In embodiments, the assessment process may be used to analyze the remote machine to determine security vulnerabilities, and recommend possible actions to take to mitigate the vulnerabilities.