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Not all threats come with a warning sign. Most adversaries lurk silently in a network for long periods — waiting for the right moment to attack. Hence, Endpoint Detection and Response (EDR) tools come into play. By collecting and analyzing behavioral data, EDR can unearth hidden dangers in your network that might otherwise remain undetected.
Understanding Behavioral Data in Cybersecurity
Behavioral data are activity patterns by your users and IT systems (what’s being accessed, sent, used, etc). For cybersecurity, knowing what “normal” behavior is is paramount to prevent breaches. By having a baseline of usual activity, you can easily spot deviations or anomalies that need further investigation to identify potential risks.
What is Behavioral Data?
Behavioral data showcases how users interact with their tech stack and network day-to-day. Common data often tracked includes:
- Login frequency and timing
- Location of login Internet Protocol (IP) addresses
- File or data access and transfer patterns
- Network activity and login volume
- Devices used for accessing files
- Communication activity (when and how they communicate through email, call, chat, etc.)
Ideally, user behaviors are somewhat consistent. For example, an employee might habitually log into their email account every Monday around 9:00 AM. If there’s a deviation, however, such as that employee logging in at 4:00 AM on a new device, that could warrant further investigation.
Why Behavioral Data Matters
Collecting and interpreting behavioral data helps you improve threat detection. It powers EDR tools to differentiate between normal, everyday network behavior and signs of malicious activity. Behavioral data is far more proactive than static information used by traditional cybersecurity measures.
For example, file hash signatures are used in anti-virus software. If a known malicious signature is found in a file, it’s likely corrupted with malware. If the signature was unknown, however, you could use behavioral data, such as the context in which the file was sent (who sent it, IP address, time, location, etc.) to decipher whether it’s a threat.
How EDR Tools Use Behavioral Data to Detect Threats
EDR tools have intelligence-gathering capabilities. They can analyze behavioral data in real-time and flag anything “abnormal.” This analysis lets you mitigate threats much faster by spotting indicators early in the attack cycle. The average length of time needed to identify a breach is 194 days. EDR (through behavioral data) can trim that down to seconds.
Identifying Anomalies and Suspicious Patterns
EDR sits in your tech stack to continuously monitor user activities. From there, a security team can set thresholds for a baseline of “normal” operations. These could include a typical number of daily logins, IP locations, or types of IT resources accessed by a user.
If someone does something outside these norms, the EDR system can trigger an alert for investigation. Cyber attacks often use basic vectors like logging into an account or emailing someone. So, seeing these deviations lets you spot early indicators of potential malicious activity. EDR looks for key behavioral attributes that could indicate something threatening. These could be suspicious user activities like:
- Anomalous login patterns such as unusual login times, numerous failed login attempts, or logins from unfamiliar locations or devices
- Privilege abuse like when users do things exceeding their normal permissions, such as accessing sensitive files
- Credential theft and misusing login credentials following a phishing, keylogging, or social engineering attack
EDR can also spot tactics linked to Advanced Persistent Threats (ATPs). For instance:
- Someone uses non-standard methods (covert channels) to communicate or exfiltrate data without detection.
- Someone delivers phishing or spear-phishing attacks to manipulate individuals into granting access or downloading malicious code.
- A person sends “beacons” from a compromised device to a command-and-control server
System configuration changes can also be suspicious. For example, tampering with security software controls, network logs, or security settings (like disabling encryption or altering firewall rules) to cover up an attack. Similarly, unusual processes in a network are linked to odd behavior. This often includes injecting code into a system to evade detection, suspicious Command Line activity, and delivering malicious payloads to exploit known system vulnerabilities.
Learning and Adapting Over Time
Algorithms aren’t always perfect, especially early on. So, EDR incorporates machine learning (ML) to constantly refine its detection capabilities.
Through more exposure to activity data and threat indicators over time, EDR can learn from the nuances of how users behave and how attacks take shape. This data allows EDR tools to remain effective even as user behavior and cybersecurity threats evolve.
Key Mechanisms of Behavioral Analysis in EDR
EDR has a lot of tricks up its sleeve. Here are some sophisticated mechanisms EDR uses to unlock the full potential of behavioral data:
Machine Learning and AI in Behavioral Detection
ML doesn’t just help EDR improve threat detection. It’s also at the heart of effective behavioral analysis. Among many use cases in cybersecurity, artificial intelligence (AI) can process vast amounts of (user and network) data in real-time and spot things that would often slip past human security analysts.
AI categorizes behaviors and flags those that appear “out of the ordinary.”
Real-Time Analysis for Proactive Threat Alerts
AI also helps EDR tools perform real-time data analysis. Instead of waiting for the end of a cycle to assess network events, user activity, and access logs, EDR provides continuous analysis to spot suspicious activities as they occur.
From there, it can trigger alerts for your Security Operation Center (SOC) team (or MDR service provider) to investigate further. All of this occurs quickly through automation — letting you stay one step ahead of adversaries and rapidly respond to threats.
Real-World Use Cases: Behavioral Data in Action
Hopefully, by now, you have the gist of how EDR uses behavioral data to spot threats. Now, let’s show EDR in action:
Insider Threat Detection
83% of businesses have reported at least one insider threat in 2024. These are particularly dangerous because the culprit already has access to your network.
Let’s consider a situation in which a marketing employee for a healthcare organization wants to steal and sell patient records on the black market. Per their role, there’s no reason for them to access an EHR system. So, accordingly, you configure your EDR tools to make accessing software outside their scope of work and a suspicious event.
During off-hours, the employee tries to log into the EHR — triggering the EDR system to flag it as an unusual access pattern. This flag prompts an investigation that ultimately leads to your company letting that employee go. Thanks to this behavioral data, you were able to prevent an insider threat from causing significant damage.
Early Detection of Malware and Ransomware
In another example, let’s say you manage IT for a financial services company. And over time, you start seeing an influx of emails going to employees with files that appear important with client information.
The EDR finds that these communications were sourced outside the country’s IP address (something out of the ordinary). And after prompting a further investigation, you learn those files were corrupted with ransomware. Thanks to behavioral analytics, you were able to squash a cyber attack early on.
The CyberMaxx Advantage in Behavioral Threat Detection
Our MaxxMDR services use advanced behavioral analytics in its EDR tools to provide organizations with smarter, adaptive threat detection. Here’s how:
Smarter Alerts Through Behavioral Insights
False positives can overwhelm your SOC team with unnecessary alerts — potentially letting legitimate threats sneak through. We filter out the noise. When using behavioral analytics, our system quickly spots early threat indicators. From there, it deploys only highly relevant notifications that need immediate investigation.
Continuous Improvement and Adaptation
Cyber threats don’t stay still. And neither should your threat detection systems. Our tools use ML to learn from shifting user behaviors and changing threat patterns continuously.
The result: Your EDR stays effective even as new tactics take shape.
CyberMaxx Excels at Comprehensive Behavioral Monitoring
EDR is the eyes and ears of your network. However, only by analyzing behavioral data can you baseline “normal” activity and spot the abnormal. CyberMaxx’s EDR tools harness these insights to detect threats proactively and effectively — keeping you vigilant against cyber attacks.