What are behavioral detection rules?

Cyber threats are constantly evolving, with attackers devising ingenious new techniques at a rapid pace. Even the most sophisticated preventative defenses eventually become less effective as adversaries find ways to circumvent them. It's an ongoing battle of wits, with malicious actors perpetually plotting their next move. However, there's one security solution that can help organizations stay a step ahead: behavioral detection rules.

What are behavioral detection rules?

Behavioral detection rules are predefined criteria used by cybersecurity systems to identify potentially malicious activity based on anomalies in user behavior. These rules leverage machine learning (ML) algorithms, a form of artificial intelligence, to build profiles of typical user conduct over time. When a user's actions stray significantly from their established patterns, such as unusual authentication attempts or access to sensitive data, the system triggers an alert for security teams to investigate.

For example, imagine you have an employee named John who works in your accounting department. John typically logs in around 9 a.m. from your Dallas office, accesses a specific set of financial records, and closes everything out by 6 p.m. If one day John suddenly authenticates at 2 a.m. from an IP address in China and starts downloading troves of unrelated HR file names, that would set off alarm bells. Behavioral detection rules would immediately flag this abnormal activity and send notifications to your SOC (security operations center) team.

How do behavioral detection rules work?

In a nutshell, behavioral detection systems continuously monitor a vast array of user activity data sources, such as:

• Login times, locations, and devices
• Network traffic patterns and volume
• File access and data transfers
• Application usage and time spent
• DNS queries

That raw data is then fed through sophisticated ML models that analyze each user's actions over time to build individualized "digital fingerprints." Whenever a user does something that veers substantially from their usual patterns, the system instantly compares it to the preset behavioral detection rules. If the anomaly exceeds a defined risk threshold, an alert is automatically triggered for the security team to assess.

Of course, not every deviation represents a real threat. That's why fine-tuning behavioral rules is both an art and a science. Set your thresholds too loosely and you'll be drowning in false alarms. But make them too restrictive and you might miss actual attacks. The key is striking a balance and continuously refining your rules based on your organization's risk tolerance, real-world experiences, and threat intelligence.

Customize security rules for your business

See Rippling SSO

Benefits of behavioral detection rules

Traditional security tools like firewalls and antivirus software are essential but struggle with novel and insider threats. Behavioral detection offers several key advantages:

Protects against unknown and insider threats

Traditional tools rely on pre-defined threat indicators, which means they can only spot attacks they've seen before. Behavioral detection, on the other hand, flags any abnormal activity, even if it doesn't match a known malware signature or exploit pattern. This allows it to catch zero-day attacks and malicious insiders who already have legitimate access.

Detects targeted and subtle attacks

Malicious insiders with unauthorized access can be difficult to distinguish from an outside perspective. However, insiders still exhibit anomalous behaviors when engaging in data theft or sabotage. Behavioral monitoring can catch them red-handed, even if they attempt privilege escalation.

Enables proactive response

Behavioral rules identify threats in real time, allowing security teams to respond immediately before attacks escalate. Behavioral detection's adaptive ML models evolve in lockstep, picking up on new anomalies as they emerge. 

Provides user-centric visibility

By building individual baselines for each user's typical activity, behavioral rules yield granular insights into potentially compromised accounts or malicious insiders. This user-centric lens provides invaluable context for accurately assessing and prioritizing risk.

Challenges of behavioral detection rules

While a powerful tool, behavioral detection isn't foolproof. Security teams should be aware of potential limitations and pitfalls:

Establishing accurate baselines

Building reliable behavioral profiles requires analyzing user activity over a period of time. During this learning phase, detection may be less precise. Profiles must also adapt as user roles and behaviors evolve to avoid false positives.

Coping with remote work:

With many employees now working outside the office, behavioral patterns are more fluid than when everyone's in an office. Remote workers may log in at odd hours from different locations and devices. Behavioral rules must account for this variability to avoid alert fatigue.

Balancing sensitivity

Setting detection thresholds requires striking a balance. If rules are too strict, false positives will overwhelm analysts. But if too loose, actual threats may slip through. Thresholds must be carefully tuned and continuously adjusted.

Detecting slow attacks

Clever attackers may attempt to evade behavioral rules by making changes slowly over time to avoid triggering anomaly detection. If malicious activity is too gradual and subtle, it may not trigger alerts. Combining behavioral detection with other methods like data loss prevention (DLP) helps mitigate this "low and slow" risk.

Automate threat detection with Rippling

See Rippling SSO

Top use cases for behavioral detection rules

Behavioral analysis is incredibly versatile and can detect a wide spectrum of threats. However, there are a few particular use cases where behavioral detection truly shines:

• Compromised credentials: Hackers often leverage stolen usernames and passwords to masquerade as legitimate users. Behavioral biometrics can identify impostors even if they have valid login credentials.
• Insider threats: Rogue employees can be incredibly difficult to distinguish since their illicit activity is cloaked beneath a veneer of legitimate access. Behavioral anomalies like accessing files outside their purview or exfiltrating data provide suggesting signals.
• Advanced persistent threats (APTs): Sophisticated attackers penetrate networks and lurk for months or years, siphoning off data or establishing footholds for later strikes. Subtle behavioral signs often provide the only clues to their presence.
• Lateral movement: After gaining initial access, attackers frequently hop between machines to escalate privileges and reach high-value targets. Detecting unusual user movement and access patterns is crucial for containing breaches.

By monitoring for telltale behavioral shifts across these high-risk scenarios, security teams can quickly spot and stop attacks that might otherwise slip by unnoticed. The faster you can detect, the faster you can respond.

Frequently asked questions

What are the key characteristics of behavioral detection?

Behavioral detection systems spot potential threats by:

• Utilizing AI and machine learning to analyze user activity
• Establishing baseline of normal behavior patterns for each user
• Identifying anomalies and deviations from typical conduct
• Enabling real-time alerts for fast incident response
• Detecting insider threats as well as external attacks

Where do behavioral detection rules fit in a cybersecurity program?

Behavioral detection rules are a crucial component of a comprehensive cybersecurity program, but they should not be implemented in isolation. For optimal protection, these rules should be integrated with other security layers such as SIEM systems, EDR capabilities, incident response processes, and access management controls. This integration allows for centralized analysis, comprehensive threat detection, and more efficient incident response to strengthen your overall security posture.

What to look for when choosing a behavioral detection solution

When choosing a behavioral detection solution, look for advanced ML capabilities with adaptive learning and domain-specific detections tailored to your industry. Prioritize solutions with flexible deployment options that integrate seamlessly with existing security systems, such as endpoint protection platforms, and offer an intuitive user interface that makes it easy to configure rules and save time. Seek tools that provide scalable performance and demonstrate continuous innovation to address emerging threats.

Protect against insider and external threats

See Rippling