# Custom Detection Logic
PDM Domain(s): TID, SPH, VSD
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Custom Detection Logic refers to bespoke security rules, analytics, and algorithms built specifically for an organization's unique environment, threat landscape, and business context. Unlike vendor-supplied signatures that target generic threats, custom detections address organization-specific risks such as abuse of internal applications, lateral movement patterns unique to the network topology, or data exfiltration channels particular to the business's data flows.
The need for custom detection logic emerges from a fundamental limitation of commercial security tools: they detect what vendors think you should worry about, not what actually threatens your specific organization. A manufacturing company running legacy SCADA systems faces different risks than a SaaS provider managing customer data across multiple cloud regions. Generic signature feeds cannot capture the nuanced ways adversaries would exploit the manufacturing company's air-gapped networks or abuse the SaaS provider's API authentication flows.
Custom detection logic fills this gap by encoding institutional knowledge about the environment into automated monitoring systems. It represents the security team's understanding of how their organization actually works, what normal behavior looks like, and where the most dangerous attack paths exist. When done correctly, custom detections provide coverage that no vendor can deliver because they reflect threats that only exist within that specific organizational context.
Building effective custom detection logic requires a systematic approach that begins with environmental profiling and threat modeling, then progresses through hypothesis development, implementation, testing, and continuous refinement.
The foundation of custom detection logic is understanding normal behavior within the organization. This means cataloging critical assets, mapping data flows, documenting authentication patterns, and establishing baseline metrics for network traffic, user behavior, and system activity. For example, a financial services firm might discover that legitimate wire transfer requests always originate from specific workstations during business hours and involve multiple approval steps in their ERP system. This knowledge becomes the basis for detecting fraudulent transfer attempts.
Environmental profiling also involves understanding the organization's unique architecture and business processes. A hospital running medical devices on isolated network segments has different normal patterns than a retail company managing point-of-sale systems across hundreds of locations. Custom detections must account for these architectural realities to avoid both false positives and coverage gaps.
Once the environment is understood, analysts develop hypotheses about how adversaries would operate within this specific context. This process combines threat intelligence about known attack groups with detailed knowledge of organizational vulnerabilities. The goal is to anticipate attack paths that generic security tools would miss.
For instance, an organization using Microsoft SharePoint for project management might hypothesize that adversaries would abuse SharePoint's external sharing features to exfiltrate data, since this activity would blend with normal business collaboration. Another hypothesis might focus on detecting credential stuffing attacks against the organization's custom web applications, which would not trigger vendor signatures designed for common platforms.
Custom detection logic is implemented across multiple security platforms, each serving different detection purposes:
SIEM Correlation Rules detect patterns across log sources that indicate suspicious activity specific to the organization. A custom SIEM rule might correlate VPN logins from unusual geographic locations with subsequent access to sensitive file shares, accounting for the organization's specific remote work patterns and data repositories.
EDR Custom Queries monitor endpoint behavior for organization-specific threat indicators. These might include detecting the execution of legitimate administrative tools in suspicious contexts, such as PowerShell scripts accessing customer databases outside normal maintenance windows.
Network Detection Rules identify malicious traffic patterns tailored to the organization's network architecture. A company with extensive use of encrypted tunnels between facilities might implement custom rules to detect command and control traffic masquerading as legitimate inter-site communication.
Cloud-Native Detections monitor infrastructure events specific to the organization's cloud deployment patterns. These rules might detect unusual API calls to cloud storage services or identify privilege escalation attempts that exploit the organization's specific identity and access management configurations.
Each custom detection undergoes rigorous testing before production deployment. This involves running the logic against historical data to validate that it would have detected known incidents while maintaining acceptable false positive rates. Testing also includes red team exercises where friendly attackers attempt to bypass the new detections, providing feedback on detection logic effectiveness.
Validation extends beyond technical testing to include business context verification. A detection rule that triggers hundreds of alerts during month-end financial processes may be technically accurate but operationally useless. Custom detections must balance security coverage with business reality.
Custom detection logic requires ongoing maintenance and refinement. As business processes evolve, network architecture changes, and new applications are deployed, detection logic must adapt accordingly. This maintenance cycle involves regular reviews of alert volume and accuracy, updates to account for environmental changes, and expansion of coverage based on new threat intelligence.
Organizations that rely solely on vendor-provided detection signatures operate with a fundamental disadvantage: they can only detect attacks that vendors have already seen and coded defenses against. This approach fails against targeted attacks, insider threats, and novel techniques designed specifically to evade common security tools.
The business impact of this detection gap is substantial. Adversaries conducting targeted attacks spend significant time understanding their target's environment before launching operations. They identify the specific applications, network topology, and business processes they will exploit. Their attack techniques are often tailored to blend with normal activity within that environment. Generic detection tools, which have no knowledge of organizational context, cannot reliably identify this tailored malicious activity.
Custom detection logic addresses this challenge by encoding organizational knowledge into security monitoring systems. It enables detection of attacks that are specifically designed to exploit the organization's unique characteristics. For example, a custom detection rule might identify data exfiltration attempts that abuse the organization's legitimate cloud backup processes, or detect lateral movement that follows the organization's specific network segmentation patterns.
The failure consequences of inadequate custom detection capabilities are severe. Organizations without environment-specific detections often discover breaches months after initial compromise, when adversaries have had time to establish persistent access, map internal systems, and identify valuable data. The extended dwell time increases both the scope of compromise and the difficulty of remediation.
A common misconception is that custom detection logic requires extensive development resources or advanced technical capabilities. While sophisticated custom detections can involve complex analytics and machine learning, many effective custom rules are simple modifications of existing detection templates, adapted to account for organizational specifics. The key is systematic thinking about environmental context rather than advanced technical implementation.
Another misconception is that custom detections are only valuable for large enterprises with unique architectures. In reality, even small organizations benefit from custom detection logic because their specific combination of applications, user patterns, and business processes creates unique attack surfaces that generic tools cannot fully address.
Within CDA's Predictive Defense Methodology (PDM), custom detection logic represents the operationalization of threat intelligence into defensive capabilities. The Threat Intelligence & Defense (TID) domain owns the development of custom detection logic, working closely with the Security Program Health (SPH) domain to ensure detections integrate with existing security operations, and the Vulnerability & Surface Defense (VSD) domain to ensure coverage aligns with actual attack surface characteristics.
CDA's approach to custom detection logic follows the Predictive Defense Intelligence (PDI) methodology: "See the threat before it sees you." This means developing detections based on anticipated adversary behavior rather than reacting to observed attacks. CDA Theater missions begin with environmental assessment and threat modeling to identify the most likely attack paths within the client's specific context, then develop custom detection logic to monitor for early indicators of these attack patterns.
CDA's implementation differs significantly from conventional approaches in several key ways. First, CDA integrates custom detection development directly with threat intelligence collection and analysis, ensuring detections evolve with the threat landscape. Second, CDA emphasizes detection logic that identifies attack progression rather than isolated malicious events, providing earlier warning of sophisticated threats. Third, CDA designs custom detections to support active defense operations, not just passive monitoring.
Every CDA Theater engagement produces tailored detection logic as a core deliverable. These detections are mapped to specific ATT&CK techniques relevant to the client's threat model and validated against realistic attack simulations conducted during the engagement. CDA believes that generic detection capabilities represent baseline security hygiene, but effective defense against targeted threats requires bespoke detection engineering that accounts for organizational context.
CDA's custom detection logic extends beyond traditional signature-based approaches to include behavioral analytics, anomaly detection, and predictive indicators. The goal is to identify adversary activity at the earliest possible stage of the attack lifecycle, preferably during initial reconnaissance or access attempts rather than after objectives have been achieved.
• Custom detection logic addresses organization-specific attack patterns that generic security tools cannot identify, providing coverage for targeted threats and insider attacks.
• Effective custom detections require systematic environmental profiling, threat modeling, and hypothesis development about adversary behavior within the specific organizational context.
• Implementation spans multiple security platforms including SIEM, EDR, network monitoring, and cloud security tools, each serving different detection purposes.
• Custom detections must balance security coverage with operational reality, requiring ongoing testing, validation, and refinement to maintain effectiveness without overwhelming security teams.
• Organizations without custom detection capabilities operate with significant blind spots against targeted attacks and environment-specific threats.
• Predictive Defense Intelligence (PDI): See the Threat First • SIEM Engineering and Operations • Behavioral Analytics Implementation • Threat Modeling for Detection Engineering • Security Operations Center (SOC) Optimization
• NIST Special Publication 800-61 Rev. 2: Computer Security Incident Handling Guide • MITRE ATT&CK Framework: Detection and Analytics • SANS Institute: Practical Guide to Custom Detection Rule Development • NIST Cybersecurity Framework: Detect Function Implementation Guidance • ISO/IEC 27035: Information Security Incident Management