A large proportion of data breaches come from authorized network users. Since they have network privileges, however, this type of cybersecurity threat is extremely difficult to address. Insider threat detection comprises the methods and technologies that organizations use to identify and mitigate these insider threats.
There are various types of insider threats, not all intentional or malicious. Pawns, for example, are simply victims of phishing or other social engineering traps while Goofs lost confidential data due to ignorant or arrogant flaunting of security policies. The malicious types of insider threats come from Collaborators and Lone Wolves, who are rarely encountered.
Insider threat detection strengthens security in a number of ways. Generally, it establishes cybersecurity policies for employees to follow while centralizing employee monitoring. Machine learning programs are especially helpful as they identify and respond automatically to anomalous or unsafe behavior, like a user logging into their account from a new location.
In order to identify insider threats, these systems must verify users and devices, preferably at each and every action or log-in. Users should also be arranged in a hierarchy of privileged data access, where they can only access the minimum amount of information they need to perform their duties.
Further, the insider threat detection system should run behavior monitoring and analytics, to identify when employees do anything unusual. If this occurs, the threat detection system can respond according to rules set down by the company’s cybersecurity posture.
Since insider threat detection is, as its name implies, concerned with internal threats security, there is not much external data to consider. However, IT departments should always stay alert for new cybersecurity threats.
Organizations may also consider updating their employee training to ensure they fully understand security protocols and what to do when they encounter issues. Organizational psychology may help with this task.
The main challenge for this use case is the main reason for its existence. Mitigating insider threats is very difficult for organizations when they grant network access to those insiders. Furthermore, the main cause of problems comes from unintentional user data breaches, which are next to impossible to prevent.
To reduce this threat, a comprehensive insider threat detection system must be created. Further, data scientists should periodically review the monitoring, reporting, and responding system to ensure it is operating at full efficiency.
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