HOW BEHAVIORAL ANALYTICS HELPS CLOSING THE CREDENTIALS SECURITY GAP

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Protecting user credentials from compromising is more like a daunting task. A number of credentials are leaked every day and they circulate online. This happens due to intrusions or unprotected servers that allows phishing to dupe users.

Data breaching leads to so many vectors that can happen in an organization over and over and in order to avoid it, many companies have taken multitudes of measures to eliminate it.

  • Access controls, including check-in security methods (biometric authentication) and identity management to reduce the risk of physical theft.
  • Email protection and antivirus are used to mitigate phishing attacks
  • Two-factor authentication is common these days to make the stolen password useless as it requires two-way of communication to process anything further.
  • Security controls always follow some standards. In order to secure credentials, you will need to follow a few steps that cascade down to form a strong security process. However, when credential measures are tallied either by external threat or internal user, the only thing that can help you from sabotaging is User and Entity Behavioral Analytics (UEBA).

    Now you might have been thinking about what this UBEA means and how it helps to prevent credentials from compromising. UEBA is a type of technology that let you sense unusual credential usage that can lead to data breaching and enables you to prevent data loss or minimize adverse effects on your organization.

    In order to prevent your credentials from compromising, here’s what your team needs to know about UEBA.

    MARSHALING MACHINE LEARNING

    User and entity behavioral analytics (UBEA) mainly emphasizes on the different patterns of human behavior to detect variances that can lead you to the potential threat. However, UEBA implementations are super expensive and can turn into data experiments with less return on investment. Whereas, if these experiments turn out perfectly they can speed up the process of identifying and analyzing important trends to protect you against some genuine threats.

    To execute a plan with an ML-based UEBA product you need high-quality data in a good amount. The more data you’ll serve, the better results you’ll get as it will educate itself to assist you with targeting malfunctions. Data sources that are used to feed UBEA (User and Entity Behavioral Analytics) includes:

    1. SECURITY INFORMATION AND EVENT MANAGEMENT (SIEM) DATA

    SIEM is a useful repository of data which itself cannot generate data from security-related sources such as security tools, server logs, and user directories.

    2. ACTIVE DIRECTORY AND LDAP DIRECTORY INFORMATION

    Well, they are the most common sources of data security for analytics programs. They enable the system to understand the role, organization, authentication, and access rights. Whilst working with all this the system can also establish baselines from which abnormalities in data pattern are detected.

    3. VPN, PROXY, AND NETFLOW ANALYTICS PRODUCTS

    Network data is also a source of collecting a volume of information such as data transfer, communications to unusual internal and external sources and unusual connections between machines.

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