Abstract: Introduced here are computer programs and computer-implemented techniques for discovering malicious emails and then remediating the threat posed by those malicious emails in an automated manner. A threat detection platform may monitor a mailbox to which employees of an enterprise are able to forward emails deemed to be suspicious for analysis. This mailbox may be referred to as an “abuse mailbox” or “phishing mailbox.” The threat detection platform can examine emails contained in the abuse mailbox and then determine whether any of those emails represent threats to the security of the enterprise. For example, the threat detection platform may classify each email contained in the abuse mailbox as being malicious or non-malicious. Thereafter, the threat detection platform may determine what remediation actions, if any, are appropriate for addressing the threat posed by those emails determined to be malicious.

Abstract: Selectively rewriting URLs is disclosed. An indication is received that a message has arrived at a user message box. A determination is made that the message includes a first link to a first resource. The first link is analyzed to determine whether the first link is classified as a non-rewrite link. In response to determining that the first link is not classified as a non-rewrite link, a first replacement link is generated for the first link.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Selectively rewriting URLs is disclosed. An indication is received that a message has arrived at a user message box. A determination is made that the message includes a first link to a first resource. The first link is analyzed to determine whether the first link is classified as a non-rewrite link. In response to determining that the first link is not classified as a non-rewrite link, a first replacement link is generated for the first link.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Techniques for producing records of digital activities that are performed with accounts associated with employees of enterprises are disclosed. Such techniques can be used to ensure that records are created for digital activities that are deemed unsafe and for digital activities that are deemed safe by a threat detection platform. At a high level, more comprehensively recording digital activities not only provides insight into the behavior of individual accounts, but also provides insight into the holistic behavior of employees across multiple accounts. These records may be stored in a searchable datastore to enable expedient and efficient review.

Abstract: Introduced here are computer programs and computer-implemented techniques for producing records of digital activities that are performed with accounts associated with employees of enterprises. Such an approach ensures that records are created for digital activities that are deemed unsafe and for digital activities that are deemed safe by a threat detection platform. At a high level, more comprehensively recording digital activities not only provides insight into the behavior of individual accounts, but also provides insight into the holistic behavior of employees across multiple accounts. These records may be stored in a searchable datastore to enable expedient and efficient review.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Introduced here are computer programs and computer-implemented techniques for discovering malicious emails and then remediating the threat posed by those malicious emails in an automated manner. A threat detection platform may monitor a mailbox to which employees of an enterprise are able to forward emails deemed to be suspicious for analysis. This mailbox may be referred to as an “abuse mailbox” or “phishing mailbox.” The threat detection platform can examine emails contained in the abuse mailbox and then determine whether any of those emails represent threats to the security of the enterprise. For example, the threat detection platform may classify each email contained in the abuse mailbox as being malicious or non-malicious. Thereafter, the threat detection platform may determine what remediation actions, if any, are appropriate for addressing the threat posed by those emails determined to be malicious.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Introduced here are computer programs and computer-implemented techniques for producing records of digital activities that are performed with accounts associated with employees of enterprises. Such an approach ensures that records are created for digital activities that are deemed unsafe and for digital activities that are deemed safe by a threat detection platform. At a high level, more comprehensively recording digital activities not only provides insight into the behavior of individual accounts, but also provides insight into the holistic behavior of employees across multiple accounts. These records may be stored in a searchable datastore to enable expedient and efficient review.

Abstract: Introduced here are computer programs and computer-implemented techniques for discovering malicious emails and then remediating the threat posed by those malicious emails in an automated manner. A threat detection platform may monitor a mailbox to which employees of an enterprise are able to forward emails deemed to be suspicious for analysis. This mailbox may be referred to as an “abuse mailbox” or “phishing mailbox.” The threat detection platform can examine emails contained in the abuse mailbox and then determine whether any of those emails represent threats to the security of the enterprise. For example, the threat detection platform may classify each email contained in the abuse mailbox as being malicious or non-malicious. Thereafter, the threat detection platform may determine what remediation actions, if any, are appropriate for addressing the threat posed by those emails determined to be malicious.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Abstract: Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.