By nature, humans are influenced by cognitive biases resulting from your brain’s attempt to simplify information processing. We tend to make decisions based on our beliefs, surroundings, and personal experiences—whether intentional or not.
Understanding the psychological aspects of human behavior and recognizing biases is essential to developing effective cybersecurity strategies. For example, when a task is familiar, there may be a tendency to prioritize convenience over security. Cybersecurity professionals can adjust their approach to mitigate human error when risk is underestimated. That’s where behavioral monitoring comes into play.
What is Behavioral Monitoring?
Behavioral monitoring is a technique used to detect and prevent fraud or risky behavior. It can be applied in various fields and may involve observing, recording, and analyzing actions and patterns of individuals or groups—to identify potential issues or trends. Often referred to as real-time monitoring, this practice is also valuable for enhancing security, improving workplace efficiency, and personalizing the customer experience.
Core Concepts
Monitoring falls into two categories and each approach serves a different and distinct purpose. Behavioral monitoring focuses on patterns and anomalies, while traditional monitoring is often rule-based.
Behavioral monitoring systems typically consist of components that work in tandem to track, analyze, and respond to system or user behavior. This may include data collection, behavioral analytics, and reporting tools providing real-time alerts. Behavioral monitoring observes how users interact with systems to identify suspect behavior or threats.
Understanding normal baseline behavior or typical patterns is crucial for detecting anomalies. Anomalies are rare occurrences that seem suspicious because they vary from established behavior or rules. Context plays an important role in interpreting behavioral data to identify true anomalies and reduce false positives.
Traditional monitoring systems focus on operational metrics and system performance. They monitor hardware, software, and network traffic to ensure systems are running efficiently. Traditional monitoring uses reactive measures and rules to address known issues as they arrive.
Historical Development
Behavioral monitoring has greatly evolved and we’ve seen considerable advancements over the years. In the early days, manual observation was used, whereas human operators reviewed logs and activity records. With the advancement of computers and digital systems from the 1970s-1980s, rule-based systems were developed to trigger alerts.
From the 1990s-2000s, statistical analysis began to play a role in monitoring. Several methods were used to establish normal baselines and detect anomalies. Big data analytics improved the ability to identify trends in large datasets. Machine learning was later integrated from the 2000s-2010s to aid in recognizing patterns more effectively. Supervised learning was also implemented to train algorithms on how to recognize behavior.
Fast forward from 2010 to the present day, where the most recent advancements involve AI-driven systems—providing real-time analysis, self-learning models, and enhanced integration with other data sources.
Enhancements in Power and Storage
Increased computing power and storage capabilities have significantly impacted the efficacy of monitoring systems. These advancements have enhanced real-time processing and analysis of large volumes of data and improved anomaly detection accuracy. The expanded storage capabilities allow for greater retention and analysis of historical data.
Scalability and Accessibility
The rise of cloud computing has influenced the scalability and accessibility of monitoring solutions. On-demand resources allow systems to scale up or down based on real-time needs, and cloud platforms can support the adjustment of the workload. Cloud computing also enables a further global reach, which is particularly valuable for remote teams. Cloud-based monitoring systems allow for quick deployment and integration in comparison to on-premise systems.
A Shift from Reactive to Proactive Monitoring
In recent years, organizations have shifted from a reactive to a more proactive approach to monitoring. This can be attributed to advancements in technology and the complexity of today’s IT environments. The traditional approach to monitoring led to delayed response times, whereas proactive monitoring uses predictive analytics to anticipate trends and patterns in advance.
Applications of Behavioral Monitoring
Behavioral monitoring encompasses a wide range of applications across various industries. When advanced technology and data analytics are leveraged, organizations gain access to valuable insights—allowing them to detect potential issues and implement proactive strategies.
Cybersecurity and Threat Detection
Behavioral monitoring plays a huge role in identifying potential security threats. When patterns in system or user behavior are analyzed, organizations can effectively identify and react to these incidents. The most common risks include insider threats, account compromises, and advanced persistent threats (APTs).
- Insider threats: When someone with access to a company’s systems and data misuses information to harm the company, organizations can detect this behavior by establishing baselines.
- Account compromises: When an unauthorized user gains access to a user’s login credentials, analyzing behavior profiles, patterns, and activities can help pinpoint a compromised account.
- APTs: When attacks are instigated by an intruder(s) who maintain a long-term presence on a network and can harvest data, monitoring network traffic can help mitigate this risk.
Another important application of cybersecurity monitoring is user & entity behavior analytics (UEBA). This solution uses algorithms and machine learning to detect anomalies in the behavior of internal users, routers, servers, and endpoints within a network. User Behavior Analytics (UBA) focuses on the behavior of individuals, while Entity Behavior Analytics (EBA) monitors the behavior of servers and network devices.
This process is executed by building baseline profiles of normal user behavior and updating them as needed. UEBA also excels at detecting anomalies that might be missed by a traditional security system.
Behavioral monitoring is also effective in detecting and responding to zero-day attacks, recently discovered security vulnerabilities that hackers can use to attack systems—resulting in zero days to fix the issue. Real-time monitoring can help detect suspicious behavior when it happens, and machine learning recognizes patterns that deviate from the norm.
Behavioral biometrics is another tool that uses patterns such as mouse movement and typing rhythms to verify individuals. Unlike one-time authentication methods such as entering a password, this process continuously monitors and verifies the user’s identity throughout a session.
When an organization integrates behavioral monitoring with security information and event management (SIEM) technology, it can more easily detect and respond to threats. SIEM collects and analyzes event data, while monitoring analyzes user behaviors—to identify potential threats.
Workplace Productivity and Safety
Behavioral monitoring can enhance workplace efficiency by offering insights into how employees interact with systems. These findings can be used to improve time management and enhance productivity but may raise employee privacy concerns.
Monitoring can also be used to identify potential safety hazards and prevent workplace accidents—by tracking real-time behavior and enforcing safety procedures and targeted training.
It’s a great tool for ensuring data security and supporting efficiency in remote work environments. Daily activities and tasks can be more accurately tracked, and any inactivity issues can be promptly addressed. Most importantly, behavioral monitoring can support wellness programs and improve work-life balance by identifying stressors and tracking work patterns.
Technologies Enabling Behavioral Monitoring
There are many technologies used to enable behavioral monitoring. Collection and analysis of employee behavior data, work patterns, and interactions all play an important part in this process.
Telemetry Data
Telemetry data collects and sends data from various sources and devices to a central location for analysis and monitoring. Data is gathered remotely, sent in real-time for review, or transmitted automatically. It offers numerous benefits, including an optimized user experience, health monitoring, and enhanced system management.
The four main types of telemetry data are often referred to as MELT:
- Metrics: Numerical data collected over time that can show the efficacy of an IT system
- Events: Data about specific incidents that occur within a system
- Logs: Text records that are time-stamped
- Traces: A collection of logs showing service interactions
Machine Learning and AI Algorithms
Both machine learning (ML) and artificial intelligence (AI) are used to analyze behavioral patterns and include supervised and unsupervised approaches to learning. This allows organizations to gain insights into behavioral patterns.
Anomaly detection often identifies patterns or behaviors that depart from the norm. This process can help detect fraud and security issues while identifying system malfunctions. Statistical methods of anomaly detection are more straightforward and rely on expected behavior, while deep learning techniques use more advanced algorithms to learn about complex patterns.
Two critical steps in building effective monitoring models are feature engineering and selection. Feature engineering relies on a combination of data analysis, business domain knowledge, and intuition. It involves creating and optimizing raw data to improve machine learning performance.
Feature selection isolates the most consistent, non-redundant, and relevant features to use in model construction. The end goal is to improve the overall model efficiency.
Natural language processing (NLP) is a technology that uses rule-based or machine-learning approaches to understand the structure and meaning of text. Textual data such as social media posts and survey responses often contain valuable insights about user behavior and preferences.
Another technology used to enable behavior monitoring is explainable AI (XAI). This set of processes and methods allows human users to understand and trust the results and output created by machine learning algorithms.
Data Visualization Tools
Data visualization is an effective tool for interpreting behavioral data and communicating insights to stakeholders. It helps enhance clarity and understanding of data, and visualization makes communicating complex information easier.
Storytelling creates a compelling narrative, and the use of heat maps, graphs, and charts enhances the presentation. That’s why interactive dashboards are a great tool for enabling real-time monitoring and decision–making.
However, visualizing high-dimensional behavioral data can present challenges due to the complexity and volume of information. One way to address this is by reducing the data into a lower-dimensional space via a technique known as principal component analysis (PCA).
Customizing visualizations to cater to different user roles and perspectives is recommended, as users have individual and specific needs. Tailoring this information helps users focus on the metrics and data points most relevant to them.
Challenges in Behavioral Monitoring
While providing valuable insights, behavioral monitoring also comes with its challenges. These range from data privacy and ethical concerns to accuracy and trust issues.
Data Privacy and Security
Data collection and storage in behavioral monitoring are two definite points of concern. Data minimization and purpose limitation principles, that collect only strictly necessary data, can help reduce privacy risks.
Regulatory compliance requirements such as General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), are designed to protect personal data. When these requirements are incorporated into monitoring processes, organizations can ensure ethical practices.
Privacy-preserving is another technique designed to minimize risks to individual privacy when analyzing personal data. Two key methods are federated learning, training AI without anyone seeing your data, and homomorphic encryption, which helps you achieve zero trust on untrusted domains without the need for decryption.
There are of course challenges to securing behavioral data throughout its lifecycle—from collection to storage and analysis, as each phase of the cycle involves its own set of risks. For example, transmitting data may make it vulnerable to tampering, and unauthorized access during collection could lead to a data leak.
It’s also important to have data governance frameworks to ensure data is used ethically and secured properly. These frameworks define clear policies and procedures as they relate to data management practices.
False Positives and Accuracy
A false positive occurs when a system incorrectly identifies normal behavior as suspicious. This can lead to consequences affecting the reliability of the monitoring system. For example, a fraud alert may be wrongly flagged based on location, when in actuality, the cardholder is just traveling. Inevitably, false alarms happen, but improving accuracy and reducing these events is a crucial part of maintaining the efficiency of security measures. One way to combat this is through the use of multi-factor authentication (MFA) and contextual analysis.
Adaptive thresholding is a method used to adjust the criteria for these triggering alerts and can help reduce false positives and better detect true anomalies. Maintaining accuracy in dynamic environments can pose challenges, but addressing evolving behaviors can ensure system reliability.
Due to their decision-making capabilities, human analysts also play a vital role in validating and refining these behavioral monitoring alerts. They can perform essential tasks such as conducting in-depth investigations and documenting incidents.
Implementing Behavioral Monitoring Systems
Effective implementation of behavioral monitoring systems involves taking several strategic and technical steps, such as defining objectives and establishing policies and procedures. This will ensure that the system is scalable and aligned with the organization’s goals.
Planning and Assessment
Organizations should take appropriate steps to assess their behavioral monitoring needs, such as conducting stakeholder interviews and performing risk assessments. When selecting the appropriate monitoring tools and technologies, organizational requirements such as accessibility and budget should be taken into account.
Pilot projects are also key to evaluating monitoring systems before full deployment. This offers a safe environment where the system’s functionality can be tested and informed decisions can be made. Creating a cross-functional team to oversee implementation can also be beneficial, as many departments and integrations may be affected.
Integration with Existing Systems
Successful integration with current infrastructure, such as legacy systems and cloud-based services, requires a thoughtful approach—to ensure functionality and minimize disruption. It’s important to understand the current framework and determine how behavioral monitoring will fit into the existing systems.
There are potential challenges associated with integration, such as data format incompatibilities, as data transformation tools can be used to convert data from one format to another. API limitations may also cause constraints, but the use of middleware can help facilitate communication by providing seamless integration between behavioral monitoring and existing systems.
Scalable architecture is also important in accommodating growing data volumes and user bases—to ensure that systems can handle the increased demands, without compromising performance. Adopting a microservices architecture, an organizational approach to software development may also be beneficial due to its flexibility and scalability. There may however be challenges with managing and coordinating multiple services and ensuring data consistency.
Ethical Considerations in Behavioral Monitoring
Ethical considerations are pivotal to establishing practices that respect individual rights, foster trust, and comply with legal standards. Only data that is necessary for monitoring should be collected, and obtaining consent from individuals is imperative.
Transparency and Consent
Clear communication with employees is critical, as users need to be well-informed about the monitoring systems in place and their objectives. Individuals should be facilitated for regular updates, and privacy policies need to be readily accessible.
Obtaining meaningful consent in complex monitoring environments can be challenging due to the sensitive nature of data collection and usage. The complexity of the information can also overwhelm users. Dynamic consent, or regularly engaging individuals, allows users to make more informed decisions. Providing individuals with control over their behavioral data fosters an environment of empowerment and respect.
Balancing Security and Privacy
The balancing act between security and privacy can be complex because it requires careful strategy. Privacy rights protect individuals from unauthorized use of their personal information, whereas data must only be collected to achieve security goals.
One approach is the principle of privacy by design, which ensures privacy is an integral part of the system’s foundation. The concept is proactive versus reactive—addressing privacy concerns before they arise. It’s user-centric and empowers individuals to manage their privacy, thus enhancing user trust and confidence.
Additional data processing techniques for security include anonymization and pseudonymization. Both are used to help mitigate privacy risks by making personal data less identifiable. Regular privacy impact assessments are crucial to maintaining an ethical monitoring program to ensure compliance with regulations and facilitate continuous improvement.
How To Use Teramind for Behaviorial Monitoring
Looking to elevate your organization’s efficiency and security? Teramind offers a powerful solution for behavioral monitoring that delivers actionable insights and robust protection. With its advanced features, Teramind helps you optimize productivity, secure your assets, and ensure compliance effortlessly. Here’s how Teramind can transform your business:
- Boost Productivity with Insightful Analytics: Teramind’s advanced behavioral monitoring tools provide real-time insights into employee activities, helping you identify and eliminate inefficiencies to maximize productivity across your team.
- Enhance Security with Comprehensive Activity Tracking: Protect your organization from internal threats and data breaches by leveraging Teramind’s detailed tracking capabilities, which monitor user behavior and detect suspicious activities before they escalate.
- Streamline Compliance and Reporting: Easily adhere to industry regulations and internal policies with Teramind’s robust compliance features, offering automated reporting and auditing to ensure you meet legal and organizational standards effortlessly.
- Customize Monitoring to Fit Your Needs: Tailor Teramind’s monitoring features to your specific business requirements with customizable settings and alerts, allowing you to focus on critical areas while maintaining a supportive and non-intrusive work environment.
Conclusion
While behavioral monitoring in cybersecurity focuses on technology and data to detect potential threats, the human element is equally important in ensuring the effectiveness of these systems.
Automation should be balanced with human involvement so that organizations can effectively respond to threats while maintaining respect for privacy.
