Use of Artificial Intelligence for User and Entity Behavior Analytics (UEBA)

Artificial Intelligence and Cybersecurity are two terms gaining importance in today’s digital age. While Artificial Intelligence is a simulation of human Intelligence in machines capable of learning, making decisions, and solving problems, cybersecurity protects systems and digital information against unauthorized access, theft, and damage. Artificial Intelligence in cybersecurity can revolutionize how companies protect themselves from cyberattacks.

In 2023, it shows that Artificial Intelligence is a technology that will continue to be unstoppable in its development, application, adoption, and acceptance by all professional and social sectors. For this reason, and so that you are aware before anyone else of everything that will await us about Artificial Intelligence, applied explicitly in cybersecurity, we anticipate its use of behavior analysis of users and entities that will mark the main lines of this technology in 2023 and the coming years. Let’s start! Are you going to miss them?

Applications of Artificial Intelligence in Cybersecurity

The inefficient operation of machinery continues to cause significant productivity losses for companies. As we move into 2023 and beyond, cybersecurity remains an organization’s top priority. Key predictions to keep in mind include the following:

• 60% of organizations will adopt the zero-trust principle as a starting point for ensuring secure environments.
•  For 60% of organizations, cybersecurity risk will be a determining factor in conducting transactions with third parties.
•  30% of countries will pass legislation regulating payments, fines, and negotiations for ransomware attacks.

In this context, there is an opportunity to leverage artificial Intelligence (AI) to combat cybercriminals. Implementing AI in cybersecurity can revolutionize how companies protect themselves against cyber threats. AI can be used to detect and prevent cyberattacks in real-time, as well as analyze vast amounts of data to identify patterns leading to potential threats. Some ways AI can be applied in the realm of cybersecurity include the following:

• Threat detection: Artificial Intelligence can detect cyber threats by analyzing large amounts of data and identifying potentially dangerous patterns. This allows companies to respond faster and more effectively to threats.
•  Malware Detection: Artificial Intelligence can be used to detect Malware by analyzing the code and identifying patterns indicative of malicious behaviour. This allows companies to detect and prevent Malware before it can cause damage.
•  Phishing Detection: Artificial Intelligence can detect Phishing attacks by analyzing the content of emails and identifying patterns indicative of Phishing. This allows companies to detect and prevent phishing attacks before they can cause damage.
•  Network security: Artificial Intelligence can monitor network traffic and identify patterns that indicate a potential threat. This allows companies to detect and prevent cyberattacks before they can cause damage.

Advantages of using Artificial Intelligence in cybersecurity for companies

The use of Artificial Intelligence in the field of cybersecurity offers many advantages for the companies that are mentioned below:

• Improved security: Artificial Intelligence can detect and prevent cyber threats in real time, improving companies’ overall security.
•  Increased efficiency: Artificial Intelligence can analyze large amounts of data much faster than a human, increasing the efficiency of security operations.
•  Cost savings: Artificial Intelligence automates the detection and prevention of cyber threats, allowing companies to require less labor and save on costs.
•  Better decision-making: Artificial Intelligence can be used to analyze data and provide conclusions that can help companies make better security decisions.
•  Better response time: Artificial Intelligence can detect and act on cyber threats in real time, which helps companies respond.

In summary, Artificial Intelligence and machine learning are two concepts significantly impacting the field of cybersecurity. By automating many tasks traditionally performed manually, AI saves time and reduces the risk of human error. Additionally, AI can process vast amounts of data much faster than humans, thereby facilitating the identification and prevention of large-scale cyber threats. Companies investing in cybersecurity and AI will be better equipped to protect their digital assets and maintain a competitive edge in the ever-evolving technology landscape.

Artificial Intelligence is always trying to incorporate innovative methods, applying machine learning algorithms and templates to our cybersecurity solutions and products to offer the most advanced and flexible protection.

Understanding User and Entity Behavior Analytics (UEBA)

User and Entity Behavior Analytics (UEBA) solutions enable modeling user behavior and their devices while they browse or use an application. UEBA involves monitoring, collecting, and evaluating data and activities of users interacting with a system, which could be informational, transactional, or process-based.

UEBA technologies leverage Artificial Intelligence and machine learning to analyze historical data records, including text, numbers, voice, audio, and video, to identify patterns and feed systems that facilitate decision-making in individual classification, social reintegration, physical security, logical security, and cybersecurity. Based on their analysis, these systems can take measures or actions and automatically adapt to make “intelligent automated decisions.”

Advanced Capabilities and Applications of UEBA Tools

User behavior analysis tools possess more advanced exception and profile monitoring capabilities than traditional computer systems. They are used to establish a baseline of normal activities specific to the organization and its users and identify deviations from that norm. UEBA employs big data algorithms and machine learning to assess these deviations in near real-time, enabling organizations to make classifications, and decisions, detect hidden patterns, and uncover risk situations or other potential security threats.

UEBA collects various data types such as user roles and titles, access, accounts, permissions, user activity, geographic location, and security alerts. The data can be gathered from past and current activities, with the analysis considering factors like resources used, session length, connectivity, and peer group activity to compare anomalous behaviors. It is also automatically updated when data changes, such as when permissions are added.

UEBA systems do not report all anomalies as risky but assess the potential impact of the behavior. Low impact scores are assigned to less sensitive resources, while higher impact scores are given to more sensitive data, such as personally identifiable information. This approach allows security teams to prioritize which traces to follow. Simultaneously, the UEBA system automatically restricts or increases authentication difficulty for users exhibiting abnormal behavior.

Machine learning algorithms enable UEBA systems to reduce false positives, providing clearer and more accurate actionable risk intelligence for cybersecurity teams.

Conclusion

In recent years, the use of techniques called User and Entity Behavior Analytics (UEBA) for analyzing the behavior of users and entities has spread. These techniques have many applications that always have something in common: recording user behavior in the past, modeling this behavior in the present, and predicting what it will be like.

A UEBA system collects data about user and entity activities from system logs. It applies advanced analytical methods to analyze the data and establishes a baseline of user behavior patterns. UEBA continuously monitors entity behavior and compares it to baseline behavior for the same entity or similar entities to detect abnormal behavior.

Baselining is key to a UEBA system, as it makes it possible to detect potential threats. The UEBA system compares the established baseline with current user behavior, calculates a risk score, and determines if deviations are acceptable. The system alerts security analysts if the risk score exceeds a certain threshold.