As artificial intelligence (AI) grows, cyberattacks are becoming more advanced and harder to stop. Traditional security systems that protect company networks are no longer enough, especially when dealing with insider threats, stolen passwords, and attackers who move through systems unnoticed.
Recent studies warn that cybercriminals are using AI to make their attacks faster, smarter, and more damaging. These advanced attackers can now automate phishing emails and create malware that changes its form to avoid being caught. Some reports also show that AI is helping hackers quickly gather information and launch more targeted, widespread attacks.
To fight back, many security teams are now using a more intelligent system called User and Entity Behavior Analytics (UEBA). Instead of focusing only on known attack patterns, UEBA carefully tracks how users normally behave and quickly spots unusual activity that could signal a security problem.
How UEBA Works
Older security tools were based on fixed rules and could only catch threats that had already been seen before. They often missed new or hidden attacks, especially when hackers used AI to disguise their moves.
UEBA changed the game by focusing on user behavior. It looks for sudden changes in the way people or systems normally act, which may point to a stolen account or an insider threat.
Today, UEBA uses machine learning to process huge amounts of data and recognize even small changes in behavior that may be too complex for traditional tools to catch.
Key Parts of UEBA
A typical UEBA system has four main steps:
1. Gathering Data: UEBA collects information from many places, including login records, security tools, VPNs, cloud services, and activity logs from different devices and applications.
2. Setting Normal Behavior: The system learns what is "normal" for each user or system—such as usual login times, commonly used apps, or regular network activities.
3. Spotting Unusual Activity: UEBA compares new actions to normal patterns. It uses smart techniques to see if anything looks strange or risky and gives each unusual event a risk score based on its severity.
4. Responding to Risks: When something suspicious is found, the system can trigger alerts or take quick action like locking an account, isolating a device, or asking for extra security checks.
This approach helps security teams respond faster and more accurately to threats.
Why UEBA Matters
UEBA is especially useful in protecting sensitive information and managing user identities. It can quickly detect unusual activities like unexpected data transfers or access from strange locations.
When used with identity management tools, UEBA can make access control smarter, allowing easy entry for low-risk users, asking for extra verification for medium risks, or blocking dangerous activities in real time.
Challenges in Using UEBA
While UEBA is a powerful tool, it comes with some difficulties. Companies need to collect data from many sources, which can be tricky if their systems are outdated or spread out. Also, building reliable "normal" behavior patterns can be hard in busy workplaces where people’s routines often change. This can lead to false alarms, especially in the early stages of using UEBA.
Despite these challenges, UEBA is becoming an important part of modern cybersecurity strategies.
