Behavioral Analytics

What is behavioral analytics?

Behavioral analytics involves studying the tendencies and activity patterns of an organization’s users. In the context of cybersecurity, behavioral analytics focuses on user behavior within networks and applications, watching for unusual activity that may signify a security threat.

As modern cyber threats grow in complexity and subtlety, the role of behavioral analytics in cybersecurity likewise grows more significant. It offers an additional layer of protection by identifying anomalies that traditional security measures might miss.

In this post, we’ll explore the concept of behavioral analytics, its applications within cybersecurity, and some of the challenges it brings. Let’s start by laying a foundational understanding.

Understanding behavioral analytics

Behavioral analytics goes far beyond the mere collection of data. It involves a deep analysis of user and system activities within an organization, unraveling the how, when, and why. The real-time evaluation of activity helps pinpoint patterns, thereby surfacing usage anomalies or potentially harmful behavior.

Artificial intelligence (AI) and machine learning (ML) often augment behavioral analytics. AI/ML can sift through massive amounts of data to identify patterns and irregularities. By integrating AI/ML, an organization’s behavioral analytics processes benefit from automation and efficiency.

Naturally, behavioral analytics performs best when working with diverse sets of data. This helps create a 360-degree understanding of user and system behaviors. Types of data used in behavioral analytics include:

• Network traffic
• Database activity
• User activity
• System events

Behavioral analytics begins by establishing a behavioral baseline: standard activities that are considered normal within an organization’s network. Once the baseline has been identified, your behavioral analytics engine can identify deviations from the baseline that could indicate a security threat or vulnerability. Deviations can be flagged based on how widely they differ from the behavioral baseline.

With this basic understanding in hand, let’s consider how behavioral analytics is applied in cybersecurity.

Applications in cybersecurity

Behavioral analytics has a range of applications in cybersecurity. One of the most valuable resources in the cybersecurity industry for behavioral analysis is the MITRE ATT&CK^® framework, which represents the industry’s knowledge of adversary tactics, techniques, and procedures (TTPs). Mapping activity to the MITRE ATT&CK framework allows organizations to understand why adversaries may be performing certain actions and how these behaviors enable them to achieve their objectives. Below are a few examples of applications of behavioral analysis in cybersecurity:

• Detecting insider threats: Behavioral analytics helps identify unusual activity that could signify malicious behavior from inside the organization — perhaps by employees or contractors.
• Detecting advanced persistent threats (APTs): An APT is a cyberattack where an intruder establishes an undetected presence in a network to steal sensitive data over a prolonged period of time. Behavioral analytics can spot an APT by flagging unusual patterns that might otherwise go unnoticed.
• Anomaly detection and threat hunting: By recognizing patterns of activity that deviate from the behavioral baseline, behavioral analytics aids in the proactive search for potential threats.
• Incident response and investigation: After an incident, organizations use behavioral analytics to assist in forensic analysis by investigating anomalies that occurred during the time of an attack.

Though its applications in cybersecurity are extensive, behavioral analytics also comes with some challenges and limitations.

Challenges and limitations of behavioral analytics

Along with the advantages that behavioral analytics offers for enhancing an enterprise’s cybersecurity measures, an organization should also consider the associated challenges.

False positives and negatives

Although behavioral analytics is powerful and offers incredible security insights, it is not immune to false positives or false negatives. False positives — which occur when harmless activities are flagged as malicious — can lead to wasted resources in investigation and mitigation. Conversely, false negatives — which occur when genuine threats go undetected — can lead to security incidents and undermine trust in the system altogether.

Privacy concerns

Behavioral analytics relies on the comprehensive collection of user activity data in your organization’s networks. This can raise concerns regarding user privacy. Behavioral analytics tools could potentially collect sensitive user information. For this reason, organizations must be transparent and meticulous about the kind of data they collect to address ethical considerations and compliance requirements.

Complexities of integrating tools

When an organization begins implementing behavioral analytics, it inevitably encounters the friction of integrating new tools into an existing security infrastructure. Tool integration can be complex and time-consuming. This expends an organization’s resources and introduces the risk of security gaps during the transition phase.

Introducing behavioral analytics from CrowdStrike

Behavioral analytics is an essential part of your cybersecurity arsenal. To identify subtle patterns of behavior, detect threats, and aid in post-incident investigation, organizations need behavioral analytics. Coupled with AI/ML, behavioral analytics offers an additional layer of security that supersedes traditional security methods. Although its implementation introduces some challenges, the benefits for your cybersecurity posture are substantial.

The CrowdStrike Falcon^® platform provides AI-native protection for your systems. Runtime threat detection leverages behavioral analytics, combining sensor telemetry with cloud-based threat intelligence and AI-powered indicators of attack.

In addition, CrowdStrike Falcon^® Identity Protection helps enterprises guard against identity-based incidents and anomalies. By comparing live traffic against behavioral baselines, the platform aids in the real-time detection of threats and lateral movement.