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 threat detection platforms designed to discover possible instances of email account compromise in order to identify threats to an enterprise. In particular, a threat detection platform can examine the digital activities performed with the email accounts associated with employees of the enterprise to determine whether any email accounts are exhibiting abnormal behavior. Examples of digital activities include the reception of an incoming email, transmission of an outgoing email, creation of a mail filter, and occurrence of a sign-in event (also referred to as a “login event”). Thus, the threat detection platform can monitor the digital activities performed with a given email account to determine the likelihood that the given email account has been compromised.

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: Techniques for identifying and processing graymail are disclosed. An electronic message store is accessed. A determination is made that a first message included in the electronic message store represents graymail, including by accessing a profile associated with an addressee of the first message. A remedial action is taken in response to determining that the first message represents graymail.

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: Deriving and surfacing insights regarding security threats is disclosed. A plurality of features associated with a message is determined. A plurality of facet models is used to analyze the determined features. Based at least in part on the analysis, it is determined that the message poses a security threat. A prioritized set of information is determined to be provided as output that is representative of why the message was determined to pose a security threat. At least a portion of the prioritized set of information is provided as output.

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: Techniques for detecting instances of external fraud by monitoring digital activities that are performed with accounts associated with an enterprise are disclosed. In one example, a threat detection platform determines the likelihood that an incoming email is indicative of external fraud based on the context and content of the incoming email. To understand the risk posed by an incoming email, the threat detection platform may seek to determine not only whether the sender normally communicates with the recipient, but also whether the topic is one normally discussed by the sender and recipient. In this way, the threat detection platform can establish whether the incoming email deviates from past emails exchanged between the sender and recipient.

Abstract: Introduced here are computer programs and computer-implemented techniques for detecting instances of external fraud by monitoring digital activities that are performed with accounts associated with an enterprise. A threat detection platform may determine the likelihood that an incoming email is indicative of external fraud based on the context and content of the incoming email. For example, to understand the risk posed by an incoming email, the threat detection platform may seek to determine not only whether the sender normally communicates with the recipient, but also whether the topic is one normally discussed by the sender and recipient. In this way, the threat detection platform can establish whether the incoming email deviates from past emails exchanged between the sender and recipient.

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 threat detection platforms designed to discover possible instances of email account compromise in order to identify threats to an enterprise. In particular, a threat detection platform can examine the digital activities performed with the email accounts associated with employees of the enterprise to determine whether any email accounts are exhibiting abnormal behavior. Examples of digital activities include the reception of an incoming email, transmission of an outgoing email, creation of a mail filter, and occurrence of a sign-in event (also referred to as a “login event”). Thus, the threat detection platform can monitor the digital activities performed with a given email account to determine the likelihood that the given email account has been compromised.

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 threat detection platforms designed to discover possible instances of email account compromise in order to identify threats to an enterprise. In particular, a threat detection platform can examine the digital activities performed with the email accounts associated with employees of the enterprise to determine whether any email accounts are exhibiting abnormal behavior. Examples of digital activities include the reception of an incoming email, transmission of an outgoing email, creation of a mail filter, and occurrence of a sign-in event (also referred to as a “login event”). Thus, the threat detection platform can monitor the digital activities performed with a given email account to determine the likelihood that the given email account has been compromised.

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.