# SIEM Use Case Development
Domain: Threat Intelligence & Defense (TID), Security Program Harmonization (SPH)
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SIEM Use Case Development is the systematic creation of detection logic within a Security Information and Event Management platform that transforms raw security telemetry into actionable threat intelligence. A use case translates a specific threat scenario into correlation rules, alert thresholds, or behavioral analytics that fire when suspicious activity matches predefined conditions.
Use cases exist because raw logs and events are noise without context. A Windows login event becomes meaningful only when it represents an authentication attempt from an impossible geographic location, occurs outside business hours from a privileged account, or follows a pattern consistent with credential stuffing. The use case provides that context by encoding threat hypotheses into detection logic that can operate at machine speed across enterprise-scale data volumes.
Effective use cases bridge the gap between threat intelligence and security operations. They embed knowledge about adversary tactics, techniques, and procedures directly into the monitoring infrastructure, ensuring that theoretical understanding of threats translates into practical detection capability. Each use case represents a hypothesis about how an attack might manifest in the organization's specific environment, tested continuously against incoming data streams.
The discipline fits within both the Threat Intelligence and Defense domain, where use cases operationalize threat intelligence, and the Security Program Harmonization domain, where they standardize detection capabilities across the organization. Well-developed use cases serve as institutional memory, capturing and codifying the security team's understanding of their environment and the threats it faces.
Modern SIEM use case development extends beyond simple rule creation to encompass machine learning models, user and entity behavior analytics, and threat hunting queries. The common thread is translating security expertise into automated detection logic that can identify threats at scale while minimizing analyst fatigue from false positives.
SIEM use case development follows a structured lifecycle that begins with threat modeling and ends with continuous tuning. The process starts with hypothesis generation, where security analysts identify specific threat scenarios relevant to their organization. These scenarios come from multiple sources: threat intelligence reports, incident post-mortems, penetration test findings, or observations from threat hunting activities.
The hypothesis phase requires answering fundamental questions about the threat scenario. What data sources would capture evidence of this attack? What normal business activity might generate similar signatures? What environmental factors might affect detection reliability? For example, detecting lateral movement via PsExec requires understanding legitimate administrative tool usage patterns, network segmentation, and the volume of system administration activity during maintenance windows.
Design follows hypothesis with the creation of detection logic specifications. Analysts define the data sources required, specify correlation criteria including event counts and time windows, establish severity ratings, and document expected false positive scenarios. A brute-force authentication use case might require failed login events from Windows Security logs and VPN gateway logs, correlate attempts by source IP address over a 10-minute window, trigger on 15 or more failures, and exclude service accounts with documented legitimate retry behavior.
Implementation translates design specifications into platform-specific syntax. Splunk use cases become SPL queries with scheduled searches. QRadar use cases become Custom Rule Engine (CRE) rules with event matching criteria. Sentinel use cases become KQL queries with analytics rules. The implementation must account for data normalization differences, field naming conventions, and platform performance characteristics. A use case that performs acceptably in a lab environment may require optimization for production data volumes.
Testing validates use case behavior in a controlled environment before production deployment. This includes positive testing with synthetic attack data to confirm detection capability and negative testing with normal business activity to identify false positive sources. Many organizations maintain attack simulation frameworks specifically for use case validation, generating controlled malicious activity that should trigger detections without impacting production systems.
Tuning represents the most time-intensive phase of the lifecycle. Initial thresholds and correlation logic almost always require adjustment after exposure to production data patterns. A insider threat use case detecting unusual file access patterns might initially alert on legitimate users accessing files outside their normal departments, requiring refinement to account for cross-functional project teams or temporary duty assignments. Effective tuning balances detection sensitivity against operational noise.
Documentation captures the rationale, technical specifications, and operational procedures for each use case. This includes mapping to MITRE ATT&CK techniques, defining escalation procedures, specifying data retention requirements, and documenting known limitations. Documentation quality directly affects analyst response effectiveness and use case maintainability over time.
Retirement removes use cases that no longer provide value due to environmental changes, threat landscape evolution, or improved detection capabilities. A use case designed to detect a specific malware family may become obsolete when endpoint protection platforms gain the ability to block that family entirely. Organizations should regularly review use case effectiveness and remove rules that generate more noise than signal.
Advanced implementations incorporate machine learning and behavioral analytics alongside traditional rule-based detection. These approaches can identify subtle anomalies that might escape rule-based detection but require more sophisticated tuning and interpretation. User and Entity Behavior Analytics (UEBA) use cases might establish baselines for normal user behavior and alert when individuals deviate significantly from established patterns.
Organizations that fail to develop effective SIEM use cases transform expensive security infrastructure investments into glorified log storage platforms. Raw security telemetry without analytical context provides no defensive value. Adversaries operate under the assumption that their activities generate security events; they count on organizations lacking the analytical capability to identify meaningful patterns within the noise.
The business impact of poor use case development manifests in multiple ways. Security teams drown in false positive alerts, leading to alert fatigue and analyst burnout. Critical threats go undetected because effective detection logic was never developed. Incident response teams lack the contextual information needed to assess threat scope and impact. Compliance auditors find evidence of data collection but no corresponding evidence of active monitoring and analysis.
Effective use case development produces measurable business outcomes. Organizations detect threats earlier in the attack lifecycle, when containment and remediation costs remain manageable. Security analysts can focus on high-fidelity alerts rather than sorting through thousands of irrelevant events. Incident response teams receive enriched alerts that include threat context, suggested containment actions, and links to relevant playbooks. Executive leadership gains visibility into threat detection coverage and security program effectiveness.
The failure consequences extend beyond missed detections. Regulatory frameworks increasingly require evidence of active security monitoring, not just data retention. The FFIEC Cybersecurity Assessment Tool specifically evaluates financial institutions' ability to detect cybersecurity events and unauthorized activity. NIST Cybersecurity Framework implementation depends heavily on detection capabilities that can only be achieved through systematic use case development.
Common misconceptions include treating use case development as a one-time configuration activity rather than an ongoing operational discipline. Threat tactics evolve continuously, requiring corresponding updates to detection logic. Environmental changes such as cloud migrations, application deployments, or business process modifications can invalidate existing use cases or create new detection requirements.
Another misconception involves prioritizing use case quantity over quality. Organizations sometimes deploy hundreds of poorly-tuned rules rather than focusing on a smaller number of high-fidelity detections. The most effective security teams maintain ruthless focus on use case quality, preferring 20 highly-tuned rules that reliably detect real threats over 200 rules that generate mostly noise.
CDA's Predictive Defense Intelligence methodology treats SIEM use case development as a continuous operational discipline rather than a configuration management activity. The approach emphasizes predictive intelligence integration, where use cases embody forward-looking threat intelligence rather than reactive indicators. This means developing detection logic for attack techniques that adversaries are likely to employ against the specific organization, not just techniques that have been observed in the industry generally.
The TID domain owns the threat intelligence integration aspects of use case development, ensuring each rule reflects current adversary tactics, techniques, and procedures relevant to the organization's risk profile. TID practitioners maintain use case mappings to current threat intelligence, updating detection logic as adversary behavior evolves. This differs from conventional approaches that treat use cases as static rules requiring only periodic review.
The SPH domain addresses use case standardization and quality assurance across the organization. This includes establishing use case development standards, maintaining detection coverage matrices, and ensuring use case documentation meets operational requirements. SPH practitioners prevent use case sprawl by enforcing development standards and retirement procedures.
CDA's methodology emphasizes detection coverage validation against real adversary playbooks rather than theoretical attack scenarios. Each Theater mission includes deliverables that test use case effectiveness against simulated adversary campaigns, ensuring detection logic performs against realistic attack sequences rather than isolated techniques. This operational testing approach identifies gaps in detection coverage that might escape traditional validation methods.
The predictive aspect manifests in use case design that anticipates adversary adaptation. Rather than developing detection logic optimized for known attack patterns, CDA practitioners design use cases that remain effective as adversaries modify their tactics. This requires understanding the fundamental constraints that adversaries face in the target environment and developing detection logic that exploits those constraints.
CDA's approach differs from conventional thinking by treating use case development as intelligence analysis rather than technical configuration. Each use case represents an intelligence hypothesis about adversary behavior, tested continuously against incoming data. This analytical approach produces higher-quality detection logic and more accurate threat assessments.
• SIEM use case development transforms raw security telemetry into actionable threat intelligence through systematic creation of detection logic that identifies specific threat scenarios.
• Effective use case development follows a structured lifecycle of hypothesis, design, implementation, testing, tuning, and retirement, with continuous refinement based on threat intelligence and environmental changes.
• Organizations with poor use case development capabilities miss critical threats while drowning security teams in false positive alerts, undermining both security effectiveness and operational efficiency.
• Use case quality matters more than quantity; fewer high-fidelity rules that reliably detect real threats provide more defensive value than numerous poorly-tuned rules generating noise.
• Successful use case development requires treating detection logic as intelligence hypotheses that must be validated against real adversary behavior, not just theoretical attack scenarios.
• Predictive Defense Intelligence (PDI): See the Threat First • Security Operations Center (SOC) Maturation • Threat Intelligence Integration • Detection Engineering Fundamentals • Behavioral Analytics and UEBA
• NIST Special Publication 800-61 Rev. 2: Computer Security Incident Handling Guide • MITRE ATT&CK Framework: Tactics, Techniques, and Common Knowledge • SANS Institute: "Developing and Maintaining Effective SIEM Use Cases" • Gartner: "Market Guide for Security Information and Event Management"