AI or Artificial Intelligence-based DB security is now growing up to an advanced phase to provide more precise and reliable services to the users. The customers of such unique services include the top corporate financial firms, manufacturers, healthcare providers, and even government organizations.
Advanced AI database security administration practices can non-intrusively assess all tables, databases, and other applications with access to the database and find faults. High-end machine learning and patented behavioral analysis techniques are used to identify the compromised credentials and potential database attacks. Here are some insights shared by Benjamin Farber, Ph.D., Technical Expert at DB Networks.
How does the technology work?
AI-based DB security uses deep-reaching inspection protocols to extract database items like user information and table information from a series of a session and non-intrusively monitor the entire database network. All this information is closely analyzed with the help of adaptive models by creating a unique profile.
Once it is streamlined with RemoteDBA.com, next with the help of reinforcement learning, the accuracy can be improved dramatically over time. As discussed above, this approach first learns what exactly is defined as a standard application and user behavior. AI-based database security is a field-proven approach to be very accurate as precision is critical to avoid any Security Operations Center fatigue.
Artificial intelligence driven initiatives can identify and relay the unexpected and untoward behaviors to a multi-dimensional cluster routine. From this event cluster, one can derive optimal quality incidents and reliable supportive information. This new approach considers the events as either asset-centric or user-centric, which allows the administrators to detect the distributed attacks also in advance.
The clustering technology also makes sure that the security personnel is never overwhelmed with the conventional type of endless streams of low-value alerts. On the other hand, by aggregating all related events through batching, AI DB security offers a highly actionable DB UBA.
The concept of Database UBA
Database UBA becomes essential in light of the next generation proactive artificial intelligence DB security technologies. As discussed earlier, event clustering is the basis of database UBA. It displays the detected anomaly from the database activities at the granular level to incidents which share a unique set of attributes. UBA is generically referred to as a digital security approach, which aims at detecting insider threats, persistent threats, compromised credentials, and fraudulence.
All in all, database UBA helps to enable anomaly detection from the granular database table level compared to the previous macro-level interactions between the users and file servers, database servers, etc. As of late, WannaCry created real chaos, and another stolen NSA hacking is PassFreely. Database UBA now use its advanced machine learning techniques to instantly identify such anomalies and risks to provide detailed alerts to the admin personnel.
All these may surely sound so reassuring, but one needs to take time and effort to rightly identify the high-risk segments in their environment and the upfront cost to establish a proactive security system. Invest very carefully in automation to avoid being inundated with a myriad of low-quality security alerts. An ideal security tool should work to put you at ease, and not generate more work for you.