Abstract: Systems and methods of the present invention may relate to prediction of a mental health score, representing a mental condition of a speaker. Embodiments of the invention may analyze speech by receiving an audio data element representing a discussion; extracting a first set of audio segments pertaining to speech of a first speaker in the discussion; analyzing the first set of audio segments to produce a set of audio features; and applying a machine-learning (ML) model on the set of audio features, to predict a mental health score, representing a mental condition of the first speaker. Embodiments of the invention may provide technical means for supporting psychiatric assessment of mental disorders such as depression and anxiety in an automatic, nondisruptive manner, e.g., without direct patient's collaboration, thereby increasing validity of the assessment.

Abstract: A system and method of predicting the probability of exploitation of vulnerabilities of a computing environment. The method includes acquiring one or more environment variables associated with a computing environment. The method includes identifying a vulnerability in the computing environment based on a vulnerability database (VDB) and the one or more environment variables associated with the computing environment. The method includes generating an input dataset based on behavioral-based endpoint detection and response (EDR) data associated with the vulnerability. The method includes providing the input dataset to one or more predictive models respectively trained to predict probabilities of exploitation of vulnerabilities of computing environments based on the input dataset. The method includes generating, by a processing device, a vulnerability risk score for the vulnerability of the computing environment based on the input dataset and the one or more predictive models.

Abstract: The disclosure generally relates to a vulnerability management system configured to implement an asset-based identification algorithm to identify, update, and otherwise reconcile assets in a network according to various identification attributes that are ordered on a spectrum from authoritative to speculative based on an ability that each identification attribute has to accurately link a host to a given asset. The identification algorithm may further enable an elastic asset-based licensing approach, wherein each asset that is scanned in a current licensing period consumes a single license and licenses are reclaimed from any old assets that are not scanned in a current licensing period (i.e., the old assets do not count towards a total licensed asset count. Furthermore, asset counts may be allowed to temporarily exceed the total licensed asset count without requiring license upsells, with true-up payments only required if and/or when asset counts reflect general expansion of a customer network.

Abstract: The disclosure generally relates to a vulnerability management system configured to implement an asset-based identification algorithm to identify, update, and otherwise reconcile assets in a network according to various identification attributes that are ordered on a spectrum from authoritative to speculative based on an ability that each identification attribute has to accurately link a host to a given asset. The identification algorithm may further enable an elastic asset-based licensing approach, wherein each asset that is scanned in a current licensing period consumes a single license and licenses are reclaimed from any old assets that are not scanned in a current licensing period (i.e., the old assets do not count towards a total licensed asset count. Furthermore, asset counts may be allowed to temporarily exceed the total licensed asset count without requiring license upsells, with true-up payments only required if and/or when asset counts reflect general expansion of a customer network.

Abstract: The disclosure generally relates to a vulnerability management system configured to implement an asset-based identification algorithm to identify, update, and otherwise reconcile assets in a network according to various identification attributes that are ordered on a spectrum from authoritative to speculative based on an ability that each identification attribute has to accurately link a host to a given asset. The identification algorithm may further enable an elastic asset-based licensing approach, wherein each asset that is scanned in a current licensing period consumes a single license and licenses are reclaimed from any old assets that are not scanned in a current licensing period (i.e., the old assets do not count towards a total licensed asset count. Furthermore, asset counts may be allowed to temporarily exceed the total licensed asset count without requiring license upsells, with true-up payments only required if and/or when asset counts reflect general expansion of a customer network.

Abstract: The disclosure generally relates to a vulnerability management system configured to implement an asset-based identification algorithm to identify, update, and otherwise reconcile assets in a network according to various identification attributes that are ordered on a spectrum from authoritative to speculative based on an ability that each identification attribute has to accurately link a host to a given asset. The identification algorithm may further enable an elastic asset-based licensing approach, wherein each asset that is scanned in a current licensing period consumes a single license and licenses are reclaimed from any old assets that are not scanned in a current licensing period (i.e., the old assets do not count towards a total licensed asset count. Furthermore, asset counts may be allowed to temporarily exceed the total licensed asset count without requiring license upsells, with true-up payments only required if and/or when asset counts reflect general expansion of a customer network.