The modern cyber-threat landscape is evolving rapidly, with newer and more sophisticated attacks emerging daily. Enterprises have dealt with these risks by implementing complex cybersecurity frameworks consisting of firewalls, anti-malware, intrusion detection systems, etc. However, these are no longer sufficient in the age of hybrid clouds and remote working. Users can access environments from a variety of devices and locations. Hence, we need a new approach focusing on the user’s behaviour instead of the device or the location. This is where User and Entity Behavior Analytics (UEBA) comes in as an approach that shifts the focus to what the user is doing rather than where they are coming from or what their device is doing.
UEBA provides an intelligent context-based approach to securing the modern-day environment by leveraging the power of Artificial Intelligence and Machine Learning to cybersecurity. By analyzing threat signals from a user’s behaviour and comparing them to their regular activity, UEBA can create a risk profile based on factors such as login patterns, application access, file access, device usage, etc. This risk profile is not static and dynamically changes as users change their behaviour over time. By essentially learning how the user behaves using AI, UEBA provides a smarter, risk-based approach to cybersecurity, instead of the static allow/disallow rules that have been used traditionally. When users start deviating from their established behaviour, the UEBA can proactively highlight this as a potential threat.
The power of UEBA is apparent when trying to mitigate attacks involving abuse of privileged access or compromised accounts. These attacks are not trivial to detect as the compromised accounts are typically authorised to access the assets. Sophisticated attacks using zero-day exploits, which can evade even the most advanced cybersecurity solutions, can be detected using UEBA as UEBA analyzes subtle context-based signals.
UEBA leverages the power of machine learning and applies it to the analysis of user behavior within a network. By building a baseline of what is “normal” behavior and what isn’t, UEBA can detect subtle shifts in user behaviour that may indicate malicious activity. These contextual data signals can easily get missed by conventional tools like SIEM solutions or firewalls. A UEBA solution works by analyzing the vast amounts of data within the organization during its learning phase, where it studies data signals such as logins, file access, application and database usage patterns. This telemetry data is fed into its analytics platform, where powerful machine learning algorithms are used to understand and establish a baseline for the users and assets.
Unlike static rules, UEBA algorithms improve their detection as they learn how each environment operate. Simply put, the more data the UEBA gathers, the better it gets at identifying anomalies. The UEBA then continually monitors user behaviour against this normal baseline in real-time and flags any deviations. This can be invaluable in identifying complex multi-stage attacks like compromised accounts, privilege abuse, insider attacks, etc.
Common use cases of UEBA
UEBA can help with various security use cases to detect threats that other solutions might miss. Let us take a look at a few of the most important ones:
- Compromised Accounts: A UEBA solution can be highly effective at detecting if an account has been compromised, due to its ability to flag activities the account does not normally perform. Even if an attacker has managed to authenticate and authorize themselves successfully, UEBA uses various indicators to determine if the account has been compromised. For instance, if the account is accessed from an unusual location or during odd hours, the UEBA solution promptly detects this unusual activity pattern and triggers an alert.
- Malicious Insiders and Privilege Abuse: A user misusing their authorized access can be one of the most challenging actions to detect. This is where UEBA solutions can show their value. Similar to compromised accounts, a UEBA solution does not rely on the authenticated status of the user, but instead analyses the user activity, eg, checking if the users are accessing servers they do not usually access or accessing files in a pattern which deviates from their norm.
- Privilege Escalation: An attacker or insider attempting to escalate their privileges can be detected by a UEBA solution. This can be very useful in detecting a compromised account which the attacker is using to stay persistent within the network.
- Unauthorized Data Access and Exfiltration: By analyzing file and data transfer attempts and comparing it against a historical baseline, UEBAs can proactively detect data exfiltration attempts and unauthorized data access. A user attempting to exfiltrate suspicious amounts of data or accessing data outside of their role will get proactively flagged.
How InsiderSecurity can help
While a UEBA solution is not a silver bullet and should form part of a holistic cybersecurity framework, its ability to detect insider attacks, compromised accounts, data exfiltration, etc., is an essential defense against modern attacks. UEBA allows companies to respond to sophisticated attacks that will fly under the radar of most cybersecurity solutions.
Insider Security’s Automated UEBA solution provides all the abilities mentioned, along with the ability to protect data on-prem and in the cloud. Our solution delivers holistic visibility into data as it is accessed, used, or moved and can detect suspicious user or network-level activity.
Automated UEBA offers the following:
- Proactive detection of malicious user activity within the network
- Advanced machine learning and advanced user behaviour analytics to detect user compromises and privileged abuse
By leveraging the power of machine learning and providing actionable insights, Automated UEBA gives your cybersecurity team the ability to respond quickly and mitigate modern cyber threats without compromising user productivity.