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The present disclosure provides techniques for real-time voice analysis to prevent social engineering that can be integrated with existing communication systems . A processing device generates, via an AI model, a representation of a voice of a speaker based on an audio recording comprising the voice of the speaker. The processing device determines, based on the audio recording, an identifier of the speaker. The processing device executes, based on the representation of the voice and the identifier of the speaker, a search over a database comprising cases for known threat campaigns and profiles for verified users. The processing device outputs, based on search results for the search, an indication as to whether the speaker is part of a threat campaign and a likelihood that a purported identity of the speaker matches an actual identity of the speaker.

Prediction of CPEs using banners greatly improves computer functioning. Many web services have an unknown common platform enumeration (CPE). When the CPE is unknown, a computer system is unable to obtain cybersecurity flaws and software fixes for a software product or web service. A CPE, though, is predicted by banner-prompting a large language model using a web service banner. Once the CPE is predicted, vulnerabilities may be identified.

A security agent executing on a computing system may determine when an encryption process, configured to generate an encrypted archive file containing one or more files, is initiated on the computing system. The security agent may identify files accessed by the encryption process during generation of the encrypted archive file, and may generate corresponding archive content data indicating content of the files accessed by the encryption process and that are likely included within the encrypted archive file. The security agent may apply policies to operations associated with the encrypted archive file by using the archive content data to determine contents of the encrypted archive file, without decrypting the encrypted archive file.

The present disclosure provides an approach of collecting contextual execution data of a service executing in a runtime environment. The contextual execution data indicates a communication between the service and a runtime entity within the runtime environment. The approach determines a cybersecurity risk score of the service based on the contextual execution data and prioritizes the service based on the cybersecurity risk score. In turn, the approach performs a remediation of a cybersecurity threat to the service based on the prioritizing.

The present disclosure provides techniques for machine speed attack defense. A processing device detects evidence of a potential in-progress cybersecurity attack with respect to an endpoint. The processing device generates a data structure based on the detected evidence. The processing device performs a fuzzy comparison based on the data structure and at least one data structure associated with a known cybersecurity attack. The processing device implements, based on the fuzzy comparison, a reversible response to the potential in-progress cybersecurity attack.

The present disclosure provides an approach of obtaining information associated with a software program. The present disclosure produces, by a processing device, a classification using an AI model that identifies, in the information, a provision indicating that the software program will perform an intrusive action. In turn, the present disclosure provides the classification to a destination device that indicates the information comprises the provision.

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The present disclosure provides an approach of collecting vulnerability data corresponding to a vulnerability of a target product. The approach provides the vulnerability data to an artificial intelligence model that is trained to determine a complexity indicator from the vulnerability data. The complexity indicator corresponds to applying a vulnerability patch to remediate the vulnerability. The approach determines a patch complexity classification by providing the complexity indicator to the artificial intelligence model and, in turn, provides the patch complexity classification to a target system corresponding to the target product.

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