Traditional security measures like antivirus software and firewalls are essential, but they alone are no longer sufficient to counter the increasingly sophisticated cyber threats faced by businesses today. Managed Security Providers (MSPs) need an active strategy that not only defends network perimeters and addresses internal threats but also involves continuous monitoring of user accounts to detect unusual behaviour.
A survey conducted by the SANS Institute revealed that 35% of organisations lack adequate visibility into insider threats. To mitigate these risks, analysing user behaviour becomes crucial in understanding user interactions with systems, applications, and data. Through user behaviour analysis (UBA), MSPs can use data analysis and machine learning to identify anomalies, reduce risks, and enhance security measures.
User Behaviour Analysis (UBA) in Cybersecurity
Exploring user behavioural analysis in cybersecurity reveals its significance for a robust security strategy. UBA focuses on monitoring and analysing user activities within an organisation's network, using data from sources such as system, network, and application logs. Its primary objective is to detect security breaches by recognising deviations from normal behaviour patterns. UBA also offers a comprehensive view of user actions across different systems to optimise security measures.
Case Study: Identifying Anomalies
Consider a scenario where SaaS Alerts is used to safeguard clients' systems. When reviewing application logs, an anomaly is detected involving an employee, John, who customarily accesses financial data during office hours from approved locations. However, the UBA system finds John's account accessing sensitive information late at night from an unrecognized location, triggering a security alert. The client is immediately notified, and actions are taken to mitigate the threat, such as temporarily blocking John's access, changing his credentials, and conducting a detailed security review.
Why Behavioural Analytics is Essential
Behavioural analytics plays a critical role in proactive threat detection, especially given the human element involved in 74% of data breaches, as noted in the Verizon 2023 Data Breach Investigation Report. UBA identifies unusual activities by trusted insiders and detects signs of compromise by continuously observing user behaviour and recognising deviations from established patterns.
Machine Learning and Adapting to Threats
UBA utilises machine learning to adapt to evolving threats by learning from historical data and adjusting its perceptions of "normal" behaviour. This adaptability helps combat sophisticated attack methods and reduces false positives by prioritising behaviour-based detection over mere signature-based methods. By considering contextual factors like user roles, locations, and application accesses, UBA enhances accuracy and cuts down on alert fatigue.
Compliance and Incident Response
Non-compliance with industry regulations can lead to significant issues such as slower sales cycles, security incidents, and fines. UBA contributes to regulatory compliance by providing comprehensive logs and reports of user activities, crucial for industries with stringent data protection laws. Additionally, its continuous monitoring and alerting capabilities enable swift incident response, helping security teams promptly address threats.
Implementing Effective Behavioural Analytics
Successful implementation of behavioural analytics in cybersecurity requires strategic planning. Key steps include defining objectives, integrating data across systems, establishing security baselines, fine-tuning detection thresholds, and integrating UBA with existing security measures. This integration enhances data correlation and enriches security insights.
SaaS Alerts: Enhancing MSP Security Strategies
SaaS Alerts provides MSPs with advanced behavioural analytics tools, offering a holistic view of user activities and boosting threat detection. The platform allows for customised alerts tailored to specific security needs and integrates seamlessly with existing MSP tools, creating a more unified cybersecurity strategy. Utilising advanced machine learning, SaaS Alerts continuously adapts to behavioural changes, empowering MSPs to fortify client security.
Traditional security approaches, such as antivirus software and firewalls, while crucial, no longer suffice in the face of increasingly sophisticated cyber attacks. MSPs need a proactive approach that not only secures their clients’ network perimeter and responds to internal threats, but also monitors user accounts and detects anomalous behaviour.
A survey by the SANS Institute found that 35% of respondents lack visibility into insider threats. Analysing user behaviour is essential to understanding how users interact with systems, applications and data. By harnessing the power of data analysis and machine learning, user behaviour analysis (UBA) empowers MSPs to detect anomalies, mitigate risks and optimise security posture.
User behaviour analysis
Let’s explore the relevance of user behavioural analysis in cybersecurity — how it works and why it is essential for a comprehensive security strategy.
In cybersecurity, user behaviour analytics focuses on monitoring and analysing the activities of users within an organisation’s network or applications. UBA analyses user data from various sources, such as:
- System logs
- Network logs
- Application logs
The primary goal of behavioural analysis is to identify and mitigate security breaches by detecting deviations from established behaviour patterns. UBA also provides a holistic view of user activity across multiple systems and tools to achieve this goal of enhanced security.
Accessing financial transaction
Users promptly notify the client about the situation and take action to mitigate the threat
Let’s say users leverage SaaS Alerts to secure the clients’ systems. In one of the client’s application logs, users notice an anomaly. An employee, John, typically accesses financial transaction records during business hours and only from approved locations. The UBA system, however, detects that John is accessing sensitive information late at night from an unfamiliar location. This deviation triggers a security alert and provides details about the login.
Users promptly notify the client about the situation and take action to mitigate the threat, such as temporarily blocking John’s access, changing his credentials and launching a comprehensive security review to ensure no data breaches have occurred.
Safeguard customer information
In this case, UBA detects suspicious user behaviour, allowing users to respond quickly to a potential security threat and safeguard customer information.
User behaviour analytics (UBA) and user and entity behaviour analytics (UEBA) are related concepts in cybersecurity, but they have distinct differences. While UBA focuses on individual user behaviour, UEBA also factors in the behaviour of entities like devices, servers and applications within a network. Incorporating UBA into the cyber strategy strengthens the overall security posture and helps to prevent data breaches, financial losses and reputational damage.
Signs of compromise early
Here’s why behavioural analytics is important:
Proactive Threat Detection - Insider threats, whether unintentional or intentional, are a significant concern. The Verizon 2023 Data Breach Investigation Report found that 74% of data breaches involve a human element, such as privilege misuse, stolen credentials or social engineering.
UBA can detect unusual activities by trusted insiders, such as employees or contractors, who may abuse their access privileges or have their accounts compromised. By continuously monitoring user behaviour, it identifies deviations from established patterns, helping to spot indicators of compromise and signs of compromise early.
Machine learning models
UBA relies on machine learning models to improve its adaptability to evolving automated threats
Adaptive Security - UBA relies on machine learning models to improve its adaptability to evolving automated threats. It learns from historical data and continuously adjusts its understanding of what constitutes “normal” behaviour. This adaptability is essential to deal with sophisticated attack techniques.
Reduction in False Positives - UBA reduces the number of false positives by focusing on behaviour rather than just signature-based detection. It considers factors such as user roles, location, time and application access to enhance accuracy. This contextual analysis enables security teams to concentrate on genuine threats and reduce alert fatigue.
Following negative consequences
Compliance and Reporting - Non-compliance with industry regulations leads to direct losses from business disruption and impacts future revenue. According to Drata, four out of five organisations deal with the following negative consequences due to non-compliance:
- Slower sales cycles (41%)
- Security incidents (40%)
- Fines (24%)
Strict data protection
UBA aids in meeting regulatory compliance requirements by providing detailed logs and reports of user activities. This reporting is crucial for industries with strict data protection and privacy regulations.
Incident Response - The continuous monitoring and alerting capabilities empower security teams to investigate threats and implement mitigations with minimal delay.
User behavioural analysis accelerates incident response because it tracks which data was accessed by whom and when. It also shows how the information was used, modified or deleted. This information is essential to understand the nature and extent of an attack and implement long-term remediation efforts by pinpointing suspicious activity patterns.
Suspicious activity patterns
Implementing behavioural analytics in cybersecurity requires careful planning and execution to maximise effectiveness. Here are the top five tips for a successful implementation:
- Determine objectives and use cases: Identify the specific threats or challenges to address. Whether it’s insider threats, business email compromise or advanced persistent threat (APT) detection, having a well-defined purpose ensures UBA systems meet security goals effectively.
- Collect and integrate data: Gather data from various sources across their network, including logs from applications, network traffic and user access. Ensure that the data collected is comprehensive, accurate and up to date.
- Create and refine security baselines: Establish baselines of normal behaviour for users. Initially, this step may involve historical data analysis, but over time, refine these baselines using machine learning and AI algorithms. Baselines should be role-specific and consider factors such as working hours, access patterns and locations.
- Tune the threshold setting: Fine-tune behavioural analytics system by setting appropriate thresholds for anomaly detection. It’s essential to balance between not missing real threats and minimising false positives.
Advanced behavioural analytics
SaaS Alerts empowers MSPs like users with advanced behavioural analytics in cybersecurity
Integrate with existing security systems: Incorporating UBA into pre-existing systems such as antivirus, firewalls and intrusion detection systems enables data sharing and correlation. UBA can consume data generated by these tools, adding another layer of analysis.
SaaS Alerts: Trusted Partner for User Behaviour Analysis
SaaS Alerts empowers MSPs like users with advanced behavioural analytics in cybersecurity. With their SaaS security software, users gain deeper insights into the clients’ user activities and significantly improve threat detection.
Comprehensive user monitoring
They help users supercharge the clients’ security strategy with the following capabilities:
- Comprehensive user monitoring: SaaS Alerts provides a comprehensive view of user behaviour, allowing users to monitor activities and detect anomalies.
- Customised alerting: Their platform allows users to tailor alerts to the clients’ specific use cases and security requirements. This capability ensures users only get alerts about important events.
- Integration with existing tools: We offer seamless integration with the existing MSP tools, enabling a more cohesive approach to cybersecurity.
- Advanced machine learning: SaaS Alerts leverages machine learning to adapt to evolving user behaviors, boosting threat detection capabilities.
