Retargeting the Loopholes: Behavioral Analytics and How It Strengthens Cybersecurity

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Behavior analytics is a concept in cybersecurity used to describe the use of software tools that detect unusual patterns of data transmissions in a network. It targets vulnerabilities by examining the human factor and analyzing behavior, routines, habits, and patterns that cyber criminals exploit to stage attacks, breach defenses and infiltrate systems. When we analyze and understand these behaviors, we can develop new strategies to retarget loopholes and strengthen security.

Attackers leverage psychology and predictability more so than technical complexity. They assume people will take shortcuts, make exceptions, or overlook warnings if inconvenienced. Behavioral analytics aims to recognize, anticipate and counter these regular human tendencies towards laxity, complacency, or carelessness that leave networks exposed. It begins by gathering data on normal user behaviors, interactions, processes, and patterns within an organization. Algorithms then analyze this data to discern predictable patterns and points of potential exploitation. With these insights, security practices can be redesigned, access restricted, monitoring enhanced, and staff retrained to close existing gaps.

When we understand attacker behavior and psychology in new ways, we can reimagine security to counter not just external threats but internal vulnerabilities too. Retargeting loopholes at their root cause, behavioral analytics builds in protections resilient against each successive exploit by anticipating the next before it arrives. Security gets smarter, adapts faster, and ultimately, thrives.

What Is Behavioral Analytics?

Behavioral analytics refers to the analysis of human behavior and the detection of patterns in data, usually for predictive or advisory purposes. Some key aspects of behavioral analytics include:

● Tracking user behaviors, activities, and patterns over time. Things like page views, clicks, conversions, purchases, etc.

● Predicting future behaviors and behaviors based on past patterns. For example, predicting which users are most likely to convert or churn.

● Gaining insights into user motivations, preferences, habits, and key drivers of behavior. This can help optimize products, marketing, UX design, etc.

● Monitoring for security threats like suspicious logins, data breaches, fraud, malware, etc. Behavioral analytics can help detect cyber threats.

● Providing personalized recommendations and customized experiences based on a user’s behavioral profile. Things like recommendation systems rely on behavioral analytics.

● Optimization and automation. Behavioral analytics enables data-driven decision-making to improve key metrics. It helps organizations run more efficiently and effectively.

Behavioral analytics is not about distrust or suspicion but diligence and prevention. It aims to build habits and routines defensive by design through small changes with big impacts: simple policies, workflows, or prompts that make security a priority and conscientiousness a habit without reducing productivity.

Reshaping culture and perception can shore up defenses when combined with artificial cognition and intuitive human judgment. Behavioral analytics helps leadership see security as an evolved, continually adapting practice and not as some check-the-box compliance or reactive patching after breaches. With such inventive series of data-driven resources, management can practically see people as a solution rather than a problem to solving organizational risk; with awareness, vigilance, and proactiveness, rather than reactionary response, as the goals.

How Does Behavioral Analytics Augment Your Cybersecurity?

Behavior analytics in cybersecurity uses AI and ML to analyze large data sets and identify unusual patterns that may indicate malicious activities. It can detect security threats in advance and improve overall security posture by analyzing users, entities, applications, networks, and cloud environments. The most advanced tools provide real-time insights, data visualization, reporting, alerting, and recommendations for improving security.

Here are some of the ways behavioral analytics can augment your cybersecurity:

Anomaly Detection

Behavioral analytics establishes normal user and system baselines. It can then detect anomalies that could indicate threats like fraud, malware, or insider activity. Things like sudden location changes, access at odd hours, suspicious file downloads, etc. Behavioral analytics can flag these potential risks by analyzing digital trails.

Risk Profiling

By analyzing user behaviors over time, you can develop detailed risk profiles for different groups. This allows you to apply appropriate security controls and monitoring based on risk levels. High-risk users or devices can be watched more closely. Behavioral analytics improves risk-based defense.

Predictive Threat Detection

Analytic models that detect behavioral anomalies may also be able to predict the likelihood of real threats forming. These predictions can trigger human review or automatic responses to help block threats before they fully emerge. Behavioral analytics enables earlier detection and prevention.

User and Entity Behavior Analytics (UEBA)

UEBA analyzes activities across users, devices, accounts, apps, and their connections within an organization’s networks and systems. It provides a comprehensive view of risks that traditional SIEM alone misses. UEBA spotlights suspicious access patterns, communications, and other relationships that could indicate threats.

Insider Threat Detection

Behavioral analytics is particularly useful for finding threats that originate from within an organization. Things like data exfiltration, sabotage, fraud, and IP theft can often be discovered by analyzing users’ digital and physical behaviors, access patterns, device usage, and more. Behavioral analytics helps detect insider dangers.

Automated Responses

Anomalies and threats detected through behavioral analytics can trigger automatic responses such as alerts, account locks, quarantines, password resets, and other mitigation steps. This facilitates faster, scalable responses to improve security. Behavioral analytics enables more proactive security automation.

Threat Hunting Insights

Security analysts can leverage the information provided by behavioral analytics to enhance threat-hunting effectiveness. Things like finding lateral movement paths, discovering C&C communication patterns, or spotting indicators of compromise can provide key leads and context. Behavioral data amplifies analysts’ ability to search out threats.

Reduced False Positives

By analyzing behaviors, behavioral analytics helps distinguish real threats from false alarms. This improves accuracy, minimizes wasted effort, and ensures that analysts focus on legitimate dangers. Over time, the techniques behind behavioral analytics get smarter through experience. False positives decrease while true threats are more reliably identified.

Does Behavioral Analytics Differ From Security and Information Event Management (SIEM)?

Behavioral analytics and SIEM (Security and Information Event Management) are related but are of different concepts:

SIEM specifically focuses on monitoring IT systems and networks for security threats, cybe-attacks, compliance issues, and operational risks. It collects event data across an organization’s IT infrastructure and analyzes the data for suspicious activities or anomalies.

On the other hand, behavioral analytics has a broader scope. It includes analyzing user and business behaviors, not just IT events. Behavioral analytics aims to gain insights into things like user journeys, motivations, preferences, and key metrics that drive business results.

Below are some key differences between behavioral analytics and SIEM:

Domain of Analysis

SIEM analyzes IT network and system events while behavioral analytics examines user behaviors and activities across both digital and physical worlds.


SIEM is primarily focused on security, risk management, and compliance. Behavioral analytics can be used for a wider range of goals like personalization, optimization, fraud detection, churn prediction, recommendation systems, etc.

Data Sources

SIEM collects events from IT infrastructure like servers, network devices, endpoints, etc. Behavioral analytics incorporates data from various channels including websites, mobile apps, IoT devices, offline transactions, sensors, and more.

Analytics Techniques

SIEM often relies on correlation analysis, anomaly detection, and threat hunting. Behavioral analytics employs a mix of pattern analysis, machine learning, predictive modeling, segmentation, optimization, and other advanced techniques.

Time Horizon

SIEM analyzes events in real-time or near real-time. Behavioral analytics can analyze historical trends, and patterns over long periods as well as predict future trends.

In a nutshell, while SIEM and behavioral analytics are complementary and often used together, they represent quite different approaches to monitoring and gaining insights from data. Behavioral analytics provides a more broad, multidimensional perspective on business and user behaviors.

How to Implement Behavioral Analytics in Your Cybersecurity

Implementing behavioral analytics in your cybersecurity is quite beneficial for your company as it helps your company to evade some cyber threats which in turn, can enhance productivity and skyrocket innovation. Here is a closer look at methods you can employ to implement behavioral analytics in your cybersecurity

Determine Your Goals

What specifically do you want to achieve with behavioral analytics? Things like insider threat detection, fraud prevention, anomaly detection, risk profiling, predictive threat detection, etc. Clear goals will guide your implementation efforts.

Choose an Analytics Framework

Options include user and entity behavior analytics (UEBA), security orchestration automation and response (SOAR), and proprietary solutions. Evaluate based on your needs, data sources, and technical capabilities.

Establish Data Sources

The more data the better for behavioral analytics. Integrate events from SIEM, endpoints, networks, IAM systems, cloud providers, applications, logs, and more. Include both digital and physical user activities when possible.

Establish Baselines

Analyze normal user and system behaviors to develop baselines. Look for access patterns, locations, devices, times of use, relationships, and common sequences of steps. Baselines will make anomalous behaviors stand out.

Enable Predictions

Build models that analyze behaviors to predict threats, risks, fraud probabilities, insider dangers, and other security issues before they manifest. Predictions should trigger alerts, notifications, reviews, or automated responses.

Automate Responses

Have anomalous behaviors and likely threats automatically trigger appropriate mitigation steps like alerts, account locks, quarantines, password resets, escalations, etc. Automation helps ensure fast, consistent responses at scale.

Summing It Up

As discussed, behavioral analytics is a powerful tool that can significantly strengthen cybersecurity. By analyzing user behavior, organizations can detect anomalous activities and potential threats before they can cause any harm. Behavioral analytics can also help organizations understand the context behind user actions, which can lead to more accurate threat detection and faster incident response times. However, it is important to note that behavioral analytics is not a silver bullet solution and should be seen as part of a larger cybersecurity strategy that includes other tools and practices such as network monitoring, vulnerability management, and employee training. As cyber threats continue to evolve, it is essential for organizations to stay vigilant and adapt their cybersecurity strategies to keep their data and systems safe.

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