User and Entity Behavior Analytics
User and Entity Behavior Analytics (UEBA) uses AI-based machine learning to spot changes in user behavior that often indicate inside attacks that have evaded perimeter defenses. Security teams are armed with insights into malicious, compromised or negligent users, systems and devices – cutting off the threat before it does damage.
Using advanced analytics that provides context to behavioral analysis makes it easier to identify internal security threats and find individual offenders
User behavior analytic tools are different in that they shift the focus from sending alerts of potential threats from outside the network to identifying more concentrated and individualized insider threats based on user behavior.
In the older model of user analytics, the collection of data has resulted in an overload of alerts that are nearly impossible to analyze.
UEBA is a very important component of IT security, allowing you to:
- Detect insider threats
UEBA can help you detect data breaches, sabotage, privilege abuse, and policy violations made by your own staff.
- Detect compromised accounts
Sometimes, user accounts are compromised. It could be that the user unwittingly installed malware on his or her machine, or sometimes a legitimate account is spoofed. UEBA can help you weed out spoofed and compromised users before they can do real harm.
- Detect brute-force attacks
Hackers sometimes target your cloud-based entities as well as third-party authentication systems. With UEBA, you are able to detect brute-force attempts, allowing you to block access to these entities.
- Detect changes in permissions and creation of super users
Some attacks involve the use of super users. UEBA allows you to detect when super users are created, or if there are accounts that were granted unnecessary permissions.
- Detect breach of protected data
If you have protected data, it is not enough to just keep it secure. You should know when a user accesses this data when he or she does not have any legitimate business reason to access it.
Best Practices for UEBA
Speaking of best practices, one should remember that UEBA is a response to the weaknesses of earlier monitoring systems such as SIEM and DLP. UEBA arose out of the malicious behavior by users and other entities that went undetected by these earlier approaches.
Therefore, it is good to remember that UEBA tools and processes are not meant to replace earlier monitoring systems, but instead should be used to complement them and enhance your company’s overall security posture.
Another great practice is to harness the storage and computational powers of big data, using machine learning and statistical analysis to prevent getting an avalanche of useless alerts and become overwhelmed with the large volume of data generated.
UEBA uses machine learning and algorithms to strengthen security by monitoring users and other entities, detecting anomalies in behavior patterns that could be indicative of a threat. By taking a more proactive approach to security and gaining more visibility into user and entity behavior, today’s enterprises are able to build a stronger security posture and more effectively mitigate threats and prevent security breaches