Extended Detection and Response (XDR) platforms have quickly become a cornerstone in modern cybersecurity strategies. By unifying data across endpoints, networks, cloud, and identity systems, XDR offers unparalleled visibility and automated detection of threats. However, out-of-the-box detection capabilities can only go so far. Every organization has unique infrastructure, workflows, and risk profiles, which means pre-built detection content may not fully align with their specific needs.
That’s where custom detection rules come into play. By designing and implementing tailored rules within an XDR platform, security teams can enhance precision, reduce false positives, and identify threats that would otherwise slip through the cracks.
In this article, we’ll explore why custom detection rules matter, how they can be built effectively, and best practices to ensure they add real value to your threat detection strategy.
Why Custom Detection Rules Are Essential in XDR
- Every Environment Is Unique
Predefined detection rules are often designed with generalized use cases in mind. But organizations run custom applications, hybrid infrastructures, and unique business processes. Custom rules allow teams to fine-tune detection logic to fit these realities. - Evolving Threat Landscape
Attackers are constantly adapting. Threats often blend in with normal activity or exploit niche vulnerabilities. Custom rules let defenders rapidly adapt detection to emerging TTPs (tactics, techniques, and procedures) before vendors release new signatures. - Regulatory and Compliance Needs
Some industries require monitoring of very specific events (e.g., financial transactions, healthcare data access). Custom rules ensure compliance-driven detections can be aligned with regulatory requirements. - Reducing False Positives
Generalized rules may trigger alerts for benign activity, overwhelming analysts. Custom detection rules let SOC teams refine conditions to minimize noise while keeping detection efficacy high.
Core Components of Custom Detection Rules in XDR
Building effective custom rules requires an understanding of how XDR platforms interpret and correlate data. While each vendor may differ, most custom rules are built from similar components:
- Data Sources
XDR ingests logs and telemetry from endpoints, servers, firewalls, identity providers, cloud platforms, and more. Choosing the right data source is foundational to accurate detection. - Event Triggers
The specific type of event to monitor—such as login failures, file modifications, process executions, or unusual network traffic. - Conditions and Logic
Rules apply logical conditions (AND, OR, NOT) and thresholds to define what constitutes suspicious activity. For example:- More than 10 failed login attempts within 5 minutes from the same IP.
- A PowerShell script spawning a child process that attempts network communication.
- Correlation Across Domains
XDR’s strength lies in stitching signals from multiple domains. A custom rule might correlate endpoint activity with identity anomalies—for instance, a user logging in from two different geographic locations within an hour. - Severity and Risk Scoring
Each rule should be tied to severity levels and contextual risk scoring, ensuring that alerts are prioritized based on business impact.
Example Use Cases for Custom Detection Rules
Here are some practical scenarios where custom rules add real-world value:
- Credential Misuse: Alert if a privileged account logs in from an unusual country outside normal working hours.
- Insider Threat Detection: Trigger when a departing employee downloads large amounts of sensitive files.
- Advanced Persistence: Detect when a registry key linked to persistence mechanisms is modified outside of patching cycles.
- Cloud Security: Alert when new IAM roles with administrative privileges are created in a cloud account.
- Data Exfiltration: Detect when sensitive files are compressed and then immediately transferred over unusual ports.
Steps to Building Effective Custom Rules
- Understand Your Environment
Map your assets, user behaviors, and baseline activity. This helps define what “normal” looks like so anomalies can be detected accurately. - Leverage MITRE ATT&CK Mapping
Align custom rules with MITRE ATT&CK techniques for better coverage against adversary tactics. For example, creating rules that specifically monitor for T1059 (Command and Scripting Interpreter) or T1027 (Obfuscated Files or Information). - Start with High-Value Use Cases
Instead of building dozens of rules at once, start with a handful of high-priority scenarios—such as credential misuse, lateral movement, or privilege escalation. - Iterative Testing
Build, test, and refine rules to avoid flooding your SOC with alerts. Run rules in “silent mode” initially to observe how often they trigger before making them production alerts. - Use Threat Intelligence Feeds
Incorporate external threat intel for domain names, IP addresses, and file hashes to enhance rule precision. - Document and Review Regularly
Every rule should have clear documentation: what it detects, why it’s important, and how analysts should respond. Regular reviews ensure rules remain relevant as infrastructure and threats evolve.
Best Practices for Managing Custom Detection Rules
- Prioritize Maintainability: Too many rules create complexity and alert fatigue. Focus on quality over quantity.
- Automate Responses Where Possible: Pair custom rules with automated playbooks in the XDR platform to accelerate containment (e.g., isolate endpoint, disable account).
- Monitor Performance Impact: Ensure rules don’t create unnecessary overhead or latency in your detection pipeline.
- Collaboration Across Teams: Involve IT, compliance, and business units when defining rules to ensure they align with both security and business objectives.
- Continuous Improvement: Threats evolve, so rules must evolve too. Regularly update detection logic with lessons learned from incident investigations.
The Future of Custom Detection in XDR
As XDR platforms continue to integrate AI, machine learning, and behavioral analytics, the process of building custom rules will become even more powerful. Instead of relying solely on static logic, SOC teams will leverage adaptive models that refine detection automatically based on contextual learning.
Still, human-driven customization will remain essential. Automated detection can’t always account for business-specific risks, unique workflows, or compliance mandates. A hybrid model—AI-powered detections enriched by carefully crafted custom rules—will provide the strongest defense.
Conclusion
Building custom detection rules in XDR platforms is more than a technical exercise—it’s a strategic capability. By tailoring detection logic to your environment, you strengthen your ability to uncover hidden threats, reduce noise, and respond faster.
The organizations that thrive in today’s cyber battleground aren’t just the ones that deploy XDR—they’re the ones that make XDR their own through intelligent customization.
