Open access peer-reviewed chapter - ONLINE FIRST
Abstract
Insider threats pose a major threat to most organizations. It usually avoids all types of traditional cybersecurity controls and defenses. In this chapter, “Hunting the Invisible: Harnessing UEBA to Unmask Insider Threats,” focus will be given on where AI, machine learning, and User and Entity Behavior Analytics (UEBA) are completely changing the mechanism of detection of insider threats. UEBA identifies real-time anomalous user behavior that signature-based systems miss. The chapter would discuss the kind of behavioral data that is analyzed by UEBA as well as the development of AI and machine learning in helping continuously improve detection. Using case studies in finance and healthcare, it explains the preventive activities regarding insider attacks and gives an overview of future roles of UEBA as an analytics tool and emerging strategy in security.
Keywords
- user and entity behavior analytics (UEBA)
- insider threats
- anomaly detection
- machine learning in cybersecurity
- behavioral analysis
1. Introduction
There are numerous definitions for the terms insider and insider threats. An insider is someone who has been legitimately empowered with the right to access, represent, or decide about one or more of the organization’s structures or assets [1].
The insider threat is one who possesses privileges and misuses them or accesses security to the disadvantage of someone [1].
These impacts can be broken down into financial losses, disruption to the organization, loss of reputation, and long-term impacts on organizational culture. Such impacts can become very nuanced, with little measurement or accounting [1].
It is not clear how effective different prevention, detection, or response measures are to reduce insiders and, therefore, the threat. Conversely, they might be terribly important. The security domains might be misused by insiders in unexpected ways, triggering false alarms, with insiders either maliciously acting or working with legitimate credentials through illegal means and threats from non-malicious insiders who could be performing so either through negligence or ignorance [1].
Malicious insider threats comprise deliberate actions by insiders to harm the organization. For example, this might include a former employee leaking sensitive data to their competitors for compensation or out of personal reprisal. Edward Snowden notoriously leaked NSA classified information in the summer of 2013; he believed the U.S. government violated privacy rights before it happened [1].
Mischievous threats occur when an insider intentionally misuses those privileges, although they do not intend to cause actual harm to the organization. An employee may download non-approved software to make work easier or to avoid management restrictions, not intending to disable or harm an organization but to introduce security gaps. These acts, although not malicious, may expose the organization to vulnerabilities [1].
Negligent insider threats arise when insiders inadvertently undermine security because of some careless misjudgment. For example, an employee may forget to update a password or corrupt some software and thereby expose the organization to potential risk from external attackers. These cannot be categorized as crimes against the organization but rather as careless mistakes that can have disastrous consequences [2].
Accidental insider threats involve those situations where insiders act outside the bounds of the law by mistake rather than malice. For example, they might inadvertently send a sensitive email to an unintended recipient or wrongly delete files. Such incidents happen mostly out of error or failure to adhere to established security guidelines and can have considerable repercussions despite no malicious intent [2].
1.1 Real-life examples of insider attacks
Examples of high-profile insider attacks discarded severe consequences arising from such threats. The most serious of these incidents involved financial losses, tarnished reputations, and regulatory inquiry. Notable examples include:
Edward Snowden (NSA whistleblower): Edward Snowden was a former contractor in the National Security Agency, and in 2013, he leaked highly classified information about the agency’s surveillance programs. As a malicious insider, he would have gained access to confidential information and released it. His actions instigated an international debate on privacy concerning government surveillance [3].
Target data breach: In 2013, hackers were able to enter Target’s internal network through credentials stolen from a third-party vendor. The attackers utilized these credentials legitimately in establishing malware on Target’s point-of-sale systems, which led to the stealing of credit or debit card details involving 40 million accounts. Target was vulnerable due to poor internal security practices, while compromised third-party credentials were the specific cause of that breach [4].
The Capital One Data Breach (2019): A former employee of Amazon Web Services (AWS) hacked into and stole Capital One’s consumer data on AWS servers. This revealed more than 100 million customer accounts, including credit scores, credit card applications, and social security numbers; the alleged hacker, a former employee of AWS, had knowledge of the system and exploited a vulnerability, thus accessing the information [5].
The Google Project Zero Insider Attack: In 2019, a Google employee was found to have misused his access to the internal systems and exfiltrated intellectual property. Though the cow was tended to early, concerns regarding the supply chain have been related to rising indirect threats even in high-security environments [6].
1.2 Why traditional defenses fail softer against insider threats
Traditional cyber defense strategies have long focused on keeping outside threats from attacking the organization’s periphery. However, insider threats, be they malicious, negligent, or even compromised, dictate a different approach that those old model systems just cannot offer. Key reasons traditional defenses have failed against insider threats are as follows.
Perimeter-centric security models: Traditional security systems focus on preventing external attacks with the help of firewalls, intrusion detection systems (IDS), and antivirus applications. These defenses operate under the premise that internal users are trusted; hence, they are permissible for getting by undetected by security systems for compromising insiders.
Research insight: About 78% of insider threats bypass traditional perimeter defenses such as firewalls and IDS, according to a 2021 cybersecurity study. This reveals how vulnerable such arrangements are, considering that malicious or compromised insiders are well able to go beyond perimeter defenses based on trust levels, all while carrying out hostile activities.
Example: A particularly notorious case occurred in 2021 when an employee’s stolen credentials were used by the attacker to launch an attack against sensitive systems belonging to Colonial Pipeline. Due to the traditional defenses failing to notice the attack until considerable damage was done, these soft-attack dangers may unfold.
No behavioral analytics and context-aware security: Traditional defensive systems tend to operate on static, signature-based detection methods. The mode of detection is based upon attitudes toward identifying known threats or patterns, not on detecting behavior that constitutes anomalies and which would not readily conform to established signature patterns. From this, it poses the threat that insiders may or may not set alerts, especially when modifying their activities slightly from routine ones and their behavior is not overtly suspicious.
Insufficient privilege and access management: Old systems do not detect and limit user privileges properly, necessarily creating insults where employees are allowed to perform more actions than prescribed. Excessive access rights provide insiders an avenue to perform major havoc, either intentionally (malicious insider) or unintentionally (negligent insider).
Research insight: As reported in a 2023 study, 56% of insider threats related to insiders misusing their legitimate access rights have been highlighted as a major flaw in traditional practices of access management [7].
Example: An employee at a financial institution plundered sensitive customer data in 2022 by leveraging their administrative access. This activity was, however, under wraps for a few months on the basis that nobody managed privilege dynamically.
False alarms and alert fatigue: Traditional security systems often generate alerts based on heuristics and signatures. They tend to undergo a problem of false positives. Also, this will strain out the security teams, thus reducing their effectiveness, creating delays, and creating a situation in which insider threats can be overlooked.
Impact: A 2021 paper published in IEEE Transactions on Security found that false positives overwhelm security teams, causing them to exhaust themselves and allowing threats to escape through fireworks [8].
Example: A user intentionally initiating a login may be wrongfully interpreted as an attack, drawing security attention away from subtler and more grave insider actions like unauthorized data download.
Delayed detection and response time-to-time: Traditional systems are not meant for real-time detection of insider threats. As they are based on periodical scans or manual reviews, these systems might take a couple of hours or even days to sift through an insider breach. This is even if the insider has already had his chance to cause damage and exfiltrate sensitive data.
Research insight: In a 2021 study by Gartner, it was observed that insider threats go unnoticed for an average of 77 days, a time period significantly longer than for an external threat, where it shortens to 56 days [9].
Going back to an example, one case in 2019 involved a malicious insider causing exfiltration for an entire 6 months before detection.
Insufficient monitoring of insider behavior: Traditional methods of defense focus on endpoint security and the detection of external threats rather than internal behavior. Such limited focus thus creates huge gaps wherein malicious or negligent insiders may bypass detection by utilizing their IoT access.
Major problem: Standard systems are incapable of detecting information being accessed by an insider unless comprehensive contextual analysis of user behavior is informed (e.g., when, where, and how sensitive data get accessed).
Research insight: A 2022 research paper on insider threat detection reported that 67% of insider attacks were undetected due to a lack of continuous monitoring of user behavior happening in real time [10].
2. What is UEBA and how does it work?
User and Entity Behavior Analytics refers to a cybersecurity solution depending on the analysis of machine learning and algorithms-based user and entity behavior in a network. It helps pinpoint any form of anomalous behavior that will deviate from the normal, such as when the user in a day is downloading 20 MB and suddenly downloads about 4 GB.
Such anomalies are flagged as potential threats for some investigational action [1]. UEBA also tracks machine behavior. An unexpected spike in requests to a server could indicate a Distributed Denial of Service (DDoS) attack. UEBA systems can spot these anomalies and inform IT administrators, who take the necessary actions [1].
2.1 Operation of UEBA
To work efficiently, UEBA should be installed on all devices connected to the corporate network, including personal devices used for work. The system collects data that, in turn, would establish norms during its first learning stage. The normal behavior is used in flagging any threat of hazard through the detection of anomalous events [1].
2.2 Essentially UEBA comprises three components
2.2.1 Analytics
The analytics component collects and organizes data to define behavioral norms for users and entities. Statistical models are utilized to detect deviations that could require alerts for potential threats [2].
2.2.2 Integration
Integration with existing security systems makes UEBA much stronger by combining the data from different sources, including logs and network packets, presenting a complete picture of possible threats [2].
2.2.3 Presentation
Findings are reported to the IT teams or acted upon automatically. An alert will then prompt a manual investigation, while automation would isolate any suspicious user or device from the network to prevent further damage [2].
2.3 Differentiating UEBA from legacy systems
User and Entity Behavior Analytics (UEBA) distinguishes itself from legacy systems of security in that it incorporates advanced analytics, machine learning, and contextual data in offering much more superior threat detection. Legacy systems basically run off static rules and known threat patterns but employ a dynamic behavior-based methodology to detect anomalies and contextual understanding.
2.4 Key differences
2.4.1 Variety of data sources
2.4.2 Behavioral analysis
2.4.3 Proactive threat detection
2.4.4 Contextual understanding
2.4.5 Score on risk
2.4.6 Example of application
Thus, in conclusion, the combination of machine learning, big data analytics, and dynamic behavior profiling has facilitated the migration of organizations from a reactive to a proactive security strategy. This helps the Authorized User Behavioral Analytics address most gaps in legacy systems through contextual user activity and probabilistic models that are essential for modern cybersecurity infrastructure.
3. The role of AI and ML in UEBA
User and Entity Behavior Analytics (UEBA) refers to the use of artificial intelligence and machine learning for next-generation cyber defense, proactive detection, and mitigation of threats. Unlike traditional rule-based systems, UEBA is focused on the analysis of behavioral patterns of users and entities like devices and applications to detect anomalies that can indicate possible malicious intent, insider threats, or compromised accounts.
UEBA has become the much-needed safeguard against increasingly complex computer threats because traditional firewalls, antivirus programs, and human monitoring are grossly inadequate. This system is based on dynamic baselines created from learning by AI and ML algorithms. Overall, UEBA systems are dynamic systems and will, therefore, offer flexible protection and defense against active assailants [10].
AI and ML basically constitute UEBA’s backbone for performing concretely deep analysis on substantial heterogeneous domains comprising datasets on par with even the most minute anomalies. The basic building blocks and methods incorporate:
Example scenario: Sudden data transfer spikes or unusual login activities.
Example models: Convolutional Neural Networks (CNN) on image-based data, Recurrent Neural Networks (RNN) on time-series analysis.
Example scenario: Phishing emails and unauthorized sharing of top-secret documents.
Example scenario: Detecting privilege escalation attacks or unauthorized access chains.
3.1 List of advantages of AI-powered UEBA
Threat reduction : Detection of advanced persistent threats (APTs), insider threats, and zero-day vulnerabilities. Enter example: An employee trying to exfiltrate sensitive data using atypical access patterns [11].Reduce false positives : The AI-performed risk scoring differentiates the serious threat from benign anomalies to dramatically reduce repetitive alerts and analyst fatigue [11].Scale and efficiency : Ability to invariably process voluminous data flows from multiple sources, with a constant performance level as the organization grows. Example: Monitoring millions of endpoints across the world [11].Continuous learning and adaptation : ML models are capable of adjusting their baselines for user and entity behavior as they evolve with time [11].Better visibility and better insights : Holistic picture of network activity, which helps to spot weak points and determine security policies [11].
3.2 Challenges in deploying AI-driven UEBA systems
Data quality and availability : Need huge and great datasets for proficiently trained models. If inconsistent data or missing data is there, then will not get an accurate result [12].Privacy and compliance : User data accessed by a UEBA will always be sensitive, and hence, it must follow privacy and regulatory laws (GDPR, HIPAA) [12].Algorithmic biases : Generally, ML models directly learn from the training datasets well, so they sometimes have standards of bias, which may produce skewed results or let discrimination happen. Example Mitigation: Regular audits and diverse datasets to minimize biases [12].Skill gaps : It requires appropriate skills in cybersecurity, data science, and AI/ML, and knowing how to deploy a UEBA system or proceed with its management could require heavy investment either in training or recruiting [12].Integration with existing systems : Integration with Security Information or Event Management (SIEM) and SOAR and other security tools could be challenging and time-consuming [12].
3.3 Use cases for AI-powered UEBA financial services
Insider threats, fraud activity, and unauthorized access to sensitive financial information are detected.
Example: This could entail flagging unusual transfer patterns of broad amounts indicative of possible money laundering [10].
3.3.1 Healthcare
To monitor the access of EHRs and compliance with regulations like the Health Insurance Portability and Accountability Act, access to the electronic health record (EHR) system will need constant monitoring.
Example: A medical staff member is flagged when he/she accesses patient records outside of his/her department [10].
3.3.2 Enterprise and technology
Protection of intellectual property through monitoring sensitive data access and transfer patterns. Example: Detecting unauthorized attachment downloads of proprietary source code.
3.3.3 Critical infrastructure
Securing OT networks for energy, transportation, and utility sectors.
3.3.4 Retail and e-commerce
Tracking user activity for the sake of protection against account takeover, payment fraud, and customer data breaches.
3.4 Future of AI and ML in UEBA
Multimodal analytics—combines diverse data sources (e.g., physical access logs, endpoint telemetry, cloud activity) for comprehensive anomaly detection.
Explainable AI (XAI)—enhances transparency by providing insights into why certain behaviors are flagged, thus aiding trust and validation for the analyst.
Automated response systems—it can integrate with SOAR platforms to automatically quarantine compromised accounts or block suspicious traffic in real time.
IoT and edge computing—extend UEBA capability to monitoring and securing connections for IoT devices and edge networks, which is critical as attack surfaces expand.
Collaborative threat intelligence—the sharing of threat patterns and behaviors across organizations enhances collective defense.
3.5 Case studies
3.5.1 Financial institution
Problem: Weak monitoring of a vast range of users across global operations.
Solution: Artificial Intelligence-based UEBA, using supervised and unsupervised learning methods and algorithms, with graph analytics.
Outcome: Detected insider threats. An employee attempting to sell customer data was halted from doing so, saving the organization from a huge financial and reputational loss [13].
3.5.2 Healthcare organization
Problem: Need to comply with HIPAA and prevent unauthorized access to data. Solution: NLP was used to analyze communication logs supervised for access patterns.
Outcome: Flagged and mitigated inappropriate access to patient records, thus reinforcing compliance and data security.
4. Behavioral analysis and applications in UEBA
Behavioral analysis is the process by which the patterns of action and interaction of users and entities within a system are established [14]. In the sphere of User and Entity Behavior Analytics, it comes to the great importance of enhancing cybersecurity. UEBA systems utilize big behavioral data to unveil deviations from standard patterns, allowing for automated detection of security threats and the prevention of suspected risks by either an insider attack or malware activity. Advanced analytics methodologies, such as statistical modeling and machine learning, are exploited to continuously assess and monitor the behavior of users and systems.
UEBA’s primary consensus value for behavioral analysis is its ability to set baselines for “normal” behavior, thus allowing organizations to spot subtle anomalies that could be overlooked by traditional rule-based systems. For instance, the detection system of static rules only raises concerns for the expressly unauthorized actions taken by an end-user, while behavioral analysis generates a higher level of vigilance for any user- or system-initiated activity that, despite being permissible per se, is out of the given limitations. The proactive detection capability makes UEBA a robust contributor in the fight against both outside attacks, such as compromised accounts, and insider risks, such as insider misuse.
4.1 Data collected: logs, patterns of access, mode of system usage
4.1.1 Logs
Logs form the basis of UEBA and generally describe any kind of data being captured or sought after to audit the activities on various systems. The composition of the typical log includes:
Event logs : Detailed interactions between users and systems with respect to file access, command execution, and configuration changes. Clustering techniques can be utilized to illustrate logs into meaningful sequences in order to isolate anomalies associated with users deviating from the standard action sequences.Authentication logs : Contain information regarding login attempts and origin IP addresses and devices used. Constant failures at sign-ins in strange locations or from odd devices could be taken as an indicator of maybe a brute-force attack or other user-attributed security violations.
4.1.2 Access patterns enable UEBA systems to know how users interact with resources
File access logs : Tracking which files are accessed, by whom, and how frequently. A user consistently accessing restricted data, such as financial records instead of their usual HR files, is flagged as suspicious.Network access patterns : Tracks the connections of the systems or servers. Frequent high or long-duration connections may indicate lateral movement by the attackers as they attempt privilege escalation or obtain access to sensitive information.
4.1.3 System usage
Metrics that describe how the user interacts with the system:
Session data : Tracks the length of the session, login time, and what activity has been done. Lengthy sessions that occur late at unorthodox hours reflect suspicious activities.Activity metrics : Measures application usage, document edits, and other system interactions. Deviations, such as new application access or high-volume activities, are considered threats.
All these data types allow the UEBA systems to build a strong sense of normal behavior and provide a basis for anomaly detection.
4.2 Anomaly detection in user and entity behavior
Identifying anomalies in user and entity behavior focuses on locating deviations in established baselines to discover potential threats [15]. The following discusses some of the methods and techniques used to identify anomalies.
4.2.1 Building behavioral baselines
The UEBA systems generate baselines of “normal” user and entity behavior based on historical analysis, including login time, access patterns, and system usage. These dynamically shift according to changes in user behaviors. For example, if a person accessed an HR file all day long, the usual time is during business hours. Any access to a financial record at midnight is recorded as unusual behavior. This process guarantees that UEBA has something to compare with its determinations of what represents deviances [9].
4.2.2 Statistical and threshold-based detection
Statistical methods can be used by applying averages, standard deviations, and thresholds to detect outliers. For instance,
Variance monitoring : This detects spikes in activity, for example, excessive access to files or repeated login failures.Time-series analysis : This monitors activities over time to detect unusual activity patterns, such as attempting to log in from remote locations during off-hours.
Threshold-based rules can be particularly useful for certain applications, like identifying brute-force attacks when several failed login attempts exceed a defined threshold [16].
4.2.3 Machine learning techniques
Machine learning enhances anomaly detection through complex pattern discovery within data. The most important approaches include:
Unsupervised learning : Techniques such as K-means clustering and Isolation Forests identify anomalies in unlabeled data. For example, it can find an employee who suddenly accesses resources [10].Supervised learning : Models that are trained from historical labeled data classify the activities as normal or suspicious [10].Deep learning : Neural networks are used for finding slight yet consistent changes in the pattern of access, say, files over a period of time [10].
These methods work best in dynamic behavior environments or very large datasets where traditional techniques may not be able to capture the subtlety of anomalies [10].
4.2.4 Real-time detection and contextual analysis
Real-time monitoring provides for instant response to anomalies by continuously analyzing the log data and activity streams, thus alerting the activities, such as sudden spikes in data access or logins from geographically disparate locations. False positives are reduced through contextual analysis, including consideration of certain role changes or environmental variables like the reputation of an IP address or device fingerprint.
This amalgamation of approaches ensures that security teams identify and resolve the threats without being swayed by nonsensical alerts.
4.3 Key takeaway
UEBA systems identify threats such as insider misuse, compromised accounts, and malware activity through behavioral baselines, statistical models, machine learning, and contextual analysis. This advanced technique ensures proactive protection against a wide range of cyber risks.
4.4 Sample data analysis scenarios
4.4.1 Insider threat detection
Any employee accessing financial records above and beyond his normal job function would indicate insider misuse. Also, the logs and access patterns might reveal constant deviations, and thus, security teams might have to probe deeper.
4.4.2 Account compromise detection
If an account owned by a legitimate user is accessed from a strange location or at weird hours, then the UEBA system will look to check up on the login time, IP address, and type of device utilized for possible account compromise. It is made feasible because of the behavioral baselines.
Malware entering the system usually does things such as lateral movement across the servers or accessing files that are restricted. A UEBA system detects and prevents malware threats by comparing current patterns of system use to historical data.
4.4.3 Undocumented data exfiltration
Unusual data transfer from a user, especially during off-peak times or with some other sites, could raise suspicion of data exfiltration. This is detected by a UEBA system through access patterns and usage in the system.
4.4.4 Privilege escalation
Attackers often escalate privileges to access sensitive resources. By monitoring log data, UEBA systems can flag unusual behavior that suggests an escalation of privileges.
5. UEBA systems implementation
User and entity behavior analytics (UEBA) is an approach to enhancing the detection and mitigation of insider threats, such as account compromises, and the organization’s other security-related vulnerabilities. It uses different analysis techniques that include machine learning-based and behavioral analytics and has used advanced threat detection to notice these anomalies in behavior patterns within those organizations, thus enabling them to take preemptive measures.
5.1 Steps for implementation of UEBA in an organization
The successful implementation of UEBA systems requires a structured approach compatible with the organization’s security goals and infrastructure [17].
5.1.1 Step 1: Define objectives and scope
Objective setting : Organizations should determine the primary goals of deploying UEBA, including the detection of insider threats, unusual access patterns, and sensitive data leak prevention.Scope identification : It is essential to identify the assets and areas of the network to be monitored, including user access to critical systems, application logs, and network traffic flows. This clarity ensures targeted and efficient deployment.
5.1.2 Step 2: Data collection and integration
Data sources : UEBA systems require comprehensive data collection from diverse sources, such as user activity logs, endpoint data, network traffic, and existing SIEM platforms.Normalization : Data, which is gathered from various sources, needs to be normalized so that it can be uniformly analyzed by the UEBA system. This step covers preprocessing and arranging raw data for compatibility.
5.1.3 Step 3: Configure baseline behavioral models
Behavioral profiling : Using the historical data, machine learning algorithms create profiles of normal user and entity behaviors. These profiles form the basis on which anomalies are detected that diverge from expected activity.Behavioral analytics : This system uses analytics and compares behaviors in real time against pre-defined baselines, thereby pinpointing threats such as uncommon login hours, access to sensitive files, and escalations of privilege.
5.1.4 Step 4: Install UEBA solution
Pilot testing : In preparation for full-fledged rollout, pilot test the UEBA in a controlled environment. Testing enables it to fine-tune the configuration so that the system does not trigger a lot of false positives during actual operation.Full deployment : After testing, the system is deployed throughout the organization to ensure continuous monitoring and real-time anomaly detection.
5.2 Well-known tools and platforms for UEBA [18]
Exabeam
Overview: Exabeam is a security operations platform that is AI-driven and utilizes machine learning and behavioral modeling for real-time detection and response to cyber threats.
Key features:
Real-time threat detection and risk-based alerts
Machine learning and behavioral modeling
Cloud-native, user-friendly platform
Streamlined threat detection, investigation, and response (TDIR)
Splunk User Behavior Analytics
Overview: Splunk User Behavior Analytics can be described as the detection of insider threats, fraud, and targeted attacks, correlating data from several sources that give an extensive view of user behavior.
Key features:
Data from multiple sources correlation
Deployment on-premises, cloud, hybrid
Detection of insider threats, fraud, and targeted attacks
IBM Security QRadar
Overview: IBM Security QRadar is the network and user activity visibility of security teams that can identify threats in a timely and effective manner.
Key features:
Real-time visibility of network activity and user behavior
Integration with threat intelligence
Centralized dashboard management of security posture
Microsoft Sentinel
Overview: Microsoft Sentinel is a cloud-based SIEM and SOAR solution that uses advanced analytics and AI to detect and respond to threats, prioritizing alerts based on risk.
Key features:
Real-time threat detection and prioritization
Centralized dashboard for monitoring and management
Integration with Microsoft’s Security Graph
6. Practical use cases of UEBA
6.1 Real-time threat detection in action
Real-time threat detection encompasses the act of using advanced security tools to detect and respond to threats as they occur. This includes identifying compromised accounts, spotting insider threats, and preventing data breaches. It is achieved by monitoring user behavior, comparing actions against those of their peers, and much more, all in an effort to quickly detect suspicious activities. A strategy is to take rapid actions, such as blocking high-risk users or escalating investigation processes, while integrating with other security systems like SIEM in bolstering overall security [19].
6.1.1 Account compromise detection
Indicators:
Improbable authentication patterns (e.g., dormant account use).
Concurrent Logins from Multiple Locations.
Account activity from unusual locations or times.
Customer peer group behavioral deviations.
Using peer group information to identify abnormal password behaviors, such as abnormal numbers of authentication errors or account lockouts.
Actions:
Detection of compromised credentials used for malicious purposes.
Identify generic account abuse.
6.1.2 Data exfiltration detection
Indicators:
USB data transfers after accessing private documents.
Blacklisted attempts at communication.
Abnormal traffic patterns that indicate unauthorized data movement.
Actions:
Detection of malicious attempts to remove sensitive data from the network and its prevention.
6.2 Real-time anomaly detection techniques
As for the analysis of sequences of actions, the basic principle is simple: opening a password-protected document followed by an upload to an external host might raise suspicion.
6.3 Risk-based real-time actions
Block a high-risk user immediately (disabling access to critical applications, for instance). Continuous monitoring and alerting of suspicious activity. Improved investigation workflows for flagged anomalies.
Integration with security operations:
These tools function in conjunction with existing systems, such as Security Information and Event Management, for investigation purposes.
Provides multi-layered defense strategies through continuous correlation of data from logs, network traffic, and applications used.
6.4 Insider attack prevention in finance
The finance sector is damagingly exposed to insider threats due to the general nature of its sensitive data and elevated levels of access that employees are granted. Insider threats might come from malicious insiders who abuse access for personal financial gain, negligent insiders, or individuals who intentionally wreak havoc. In view of the volume of data and transactions managed on an hourly and daily basis, the challenge of identifying and preventing these threats is huge for most financial institutions.
UEBA will, therefore, assist an organization in identifying anomalous activities that could indicate insider threats to allow for timely intervention to prevent breaches [20].
6.4.1 Use case 1: Detection of insider fraud attempts
6.4.1.1 Scenario
Andrew, a financial adviser with Goldguard Holdings, has been trying to use dormant bank accounts for money laundering [21]. His plan is to stop notifications from the account, use small amounts of illicit funds to make apparently legal deposits, reroute the deposits to external accounts, and erase any transactions in order not to be discovered. Such fraud is difficult to detect manually because it appears regular.
6.4.1.2 Detection
Goldguard Holdings is integrating a UEBA system to continuously monitor worker behavior. In Andrew’s case, the UEBA system raises some unusual activities, such as abnormal queries in the database with high-frequency deactivations of the notifications for certain accounts. The deviations from normal working behavior and role responsibilities are high.
6.4.1.3 Reaction
UEBA alerts notified the security personnel of the suspicious behavior for closer investigation. With an intensive investigation, the team was able to uncover Andrew’s nefarious scheme. Real-time termination of his access privileges to sensitive systems leaves him with little opportunity to hinder further action, as everything is acted upon with no other transaction being done.
Furthermore, they reported the issue to the appropriate authorities to help avert financial loss, legal liability, and damage to the reputation of the organization.
6.4.2 Use case 2: Prevention of spear phishing attacks
6.4.2.1 Scenario
A marketing manager at Alpha Financial receives an email that appears to come from a legitimate advertising agency [20]. The email contains an attachment—a Word document—formulated to perform the task of stealing sensitive customer data through a malicious macro.
6.4.2.2 Detection
The UEBA system of Alpha Financial analyzes user and system activity in real time, catching some anomalies ranging from multiple PowerShell commands being executed from Margaret’s computer to quick access to sensitive customer data files. This behavior strays away from her normality, and the automatic alert is triggered on the rise of her risk score.
6.4.2.3 Action
The UEBA system informs the security operators that they were given an approach to block Margaret’s computer—it was disconnected from the network to stop any extent of loss. The latter on the probe affirmed the attack as phishing, and after measures were taken to have those files made safe, it became the organization’s initiative to train Margaret and company on recognizing phishing attempts and lessening the chances of yet another occurrence.
6.4.3 Use case 3: Stopping data exfiltration by a disgruntled employee
6.4.3.1 Scenario
A disgruntled employee in a financial institution is attempting to exfiltrate sensitive customer data [22]. This employee goes ahead to download several files within a very short time and later encrypts them with unauthorized software so as to send them out.
6.4.3.2 Detection
The UEBA system of the shop keeps an eye on and analyzes user behavior continuously, with red flags like large-scale file downloads occurring in a very short time, access to files outside the ambit of an employee’s duties, and improper usage of encryption software—that is, compared to the employee’s baselines.
6.4.3.3 Action
The UEBA solution alerted, and the security team reacted by suspending access with immediate effect from critical systems. Investigations were conducted, confirming that it was an exfiltration operation. Necessary steps were undertaken to ensure the data was secured, the employee was taken through the disciplinary procedure, and new access policies were stated to corral such practices in the future.
These situations demonstrate how financial institutions can mitigate insider threats with the aid of UEBA systems. These technologies detect anomalous employee behavior that may portend trouble, watching in real time for suspicious activities before they can create serious problems. Timely interventions and consciousness-raising efforts are also key to protecting sensitive financial data, thereby putting financial institutions in compliance with GLBA in the long run. By combining Data Loss Prevention (DLP) technologies, advanced UEBA analytics, and employee awareness training, financial organizations can effectively defend against insider threats, minimizing the risk of data breaches and financial losses.
6.5 Mitigating data breaches in healthcare
The ongoing increase in the attacks on healthcare institutions by cybercriminals is primarily due to the highly marketable value of personal health information (PHI) that can be utilized for identity theft or sold on the black market. User and Entity Behavior Analytics (UEBA) is indispensable in behavioral deviation detection that would trigger an alert about a possible data breach, allowing mitigation controls for that particular breach [23].
6.5.1 Use case 1: Preventing insider threats in patient data exfiltration
6.5.1.1 Scenario
A young medical student working for a hospital, Mark has access to patient records on a need-to-know basis directly related to his studies [20]. He attempts to copy the sensitive information to transfer it out on the black market to pay off some of his debts—a crime he committed in some hours of the night after working hours.
6.5.1.2 Detection
Mark’s unusual behavior is deftly picked up by the medical organization’s UEBA system, monitoring all user activities. Mark has the required access powers, but in light of what was customary behavior for him, the system annotated his behavior—much as that Mark hardly ever accessed patient records outside normal hours. His huge volume of records being copied late at night creates an aberration, triggering a suspicion.
6.5.1.3 Action
Once the UEBA system raises the alarm and locks out Mark’s account, the IT Security team investigates at great speed, recovers the USB device, and determines that Mark’s intention was to exfiltrate sensitive patient data. Revoked user access; an investigation is done against him as well. The access controls will be reviewed and tightened by the hospital to avert further incidents.
6.5.2 Use case 2: How to stop unauthorized access to PHI after device theft
6.5.2.1 Situation
Dr. Sarah Jones, a pediatrician, has her laptop stolen, which has access to an online portal that collects patient data for the hospital [24]. Although the laptop was password-protected, it remained logged into the hospital system, leaving sensitive information vulnerable to an attacker.
6.5.2.2 Detection
The hospital’s UEBA system detects such unusual activity immediately upon its commencement. The system noted several unsuccessful login attempts from the device, and this anomalous behavior indicates a risk score spike in Dr. Jones’s account. The system correlates the access attempts with the laptop’s theft, which had been reported by Dr. Jones, and flags the activity as a breach risk.
6.5.2.3 Response
Once the system identifies the anomaly associated with the theft, it automatically locks Dr. Jones out of her account in order to stop any further transmissions. The hospital IT team wipes the laptop of sensitive data from afar. The hospital also files a police report with respect to the stolen laptop and implements corrective actions involving stronger two-factor authentication and enhanced security training.
These scenarios describe how UEBA is able to identify and interdict risks associated with unauthorized access, insider threats, or data breaches in healthcare to protect patient data while ensuring compliance with regulatory requirements such as HIPAA.
7. Overcoming challenges in UEBA implementation
The prospect of possible use is laced with challenges that have to be attended to. The barriers are few, however, that can be flashing between and within environments for widespread and meaningful adoption of UEBA:
7.1 Managing data quality and preprocessing
UEBA effectiveness is proportional to the input data quality. Noisy, incomplete records or stale baselines yield subpar results. Solutions include robustly established preprocessing pipelines and automated feature extraction to minimize human error and data inconsistency [25].
7.2 False positives—managing alerts
False positives remain as one of the most overbearing challenges besetting them. In fact, this overwhelms security teams and, to some extent, lose their ability to respond effectively. The detection can be refined by merging machine learning models with contextual analysis—e.g., where alerts nephrology-hospital—that is, known threat vectors, or real-world use cases would be correlated with some threat [26].
7.3 Activity of privileged user and developer
Irregular patterns associated with privileged users and developers offer unique challenges for the UEBA systems. They can also employ advanced role-based profiling and adaptive baselines that can customize the thresholds of the detections specific to the roles or job functions [25].
7.4 Scalability and computational overhead
As organizations tend to grow, UEBA systems also need to scale up toward the continuous quantities of data to process without losing performance. The scalability can be ensured, and the achieved cumulated computation costs can be minimized when resources available through a cloud-based and distributed processing models like Apache Kafka and Spark are used [26].
7.5 No standardized rules across vendors
Being no standardized frameworks for UEBA tools by which procuring customers will be exposed to inconsistencies in implementation and integration, one would need advocacy for industry-wide best practices and open standards as a means of facilitating interoperability and improving the overall efficiencies.
8. UEBA and future directions
The future of User and Entity Behavior Analytics (UEBA) is geared toward addressing increasingly sophisticated threats by leveraging cutting-edge technologies and methodologies.
8.1 Integration with AI and big data
Over the past few years, UEBA systems have increasingly completed AI and Big Data Analytics to enhance actionable insights regarding behavioral anomalies. AI models such as Deep Neural Networks and Ensemble learning techniques will enable the processing of large-scale and unstructured datasets for more accurate anomaly detection. Big data capability allows a UEBA solution to feed, in real time, extensive logs, traffic, and user activity [25].
8.2 More effective countering of insider threats
Insider threats are likely to continue being the forefront of UEBA advancement focus areas. In future systems, one expects the inclusion of context-aware mechanisms capable of making the distinction between benign anomalies and malfeasance. This might include better role-based baselining with the addition of sentiment analysis found to detect disgruntled or potentially malicious insiders [26].
8.3 Hybrid models like improved SIEM collaboration
The integration of UEBA systems with Security Information or Event Management (SIEM) platforms is likely to continue. A hybrid approach would mean that organizations would be able to track behavior against that of other security events in the broader context of threat. While SIEM can greatly aid UEBA by providing the enriched data needed for more accurate threat detection, UEBA adds an additional layer of anomaly detection capabilities [26].
8.4 Automation and continuous learning
Future UEBA systems will emphasize automation in such a way that they will self-update malintent behavior profiles and automatically resolve anomalies, which involves deploying reinforcement learning algorithms to update detection procedures genuinely during ongoing operations and performing this with minimal manual intervention [25].
8.5 Explainable AI (XAI) adoption
As UEBA systems get more intricate, embracing Explainable AI will become paramount. The capability of the system would enable security officers to comprehend the reasons behind detection results more convincingly, which would garner trust that would turn prediction results from being mere crystal balls into platforms for knowledge-based actions for AI models [26].
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