Data Source Strategy
Source Prioritization Log collection should prioritize high-value sources that provide greatest security visibility. Identity and authentication logs detect credential compromise and unauthorized access. Endpoint logs capture malware execution and suspicious process behavior. Network edge logs including firewall, proxy, and VPN logs detect network-based attacks and data exfiltration. Critical application logs capture business logic abuse and application-layer attacks. Cloud control plane logs detect infrastructure misconfigurations and unauthorized changes. Comprehensive coverage requires logs from all security-relevant systems, but not all logs have equal value. Prioritization ensures that limited resources focus on highest-value sources. Schema Standardization Standardized log schemas using Elastic Common Schema (ECS) or Open Source Security Events Metadata (OSSEM) enable consistent querying and correlation across diverse log sources. Schema standardization reduces complexity and enables reusable detection rules. Timestamps should be standardized to UTC with consistent format, enabling accurate event correlation. Timestamp normalization handles diverse source timestamp formats. Trace IDs and correlation IDs enable tracking requests across distributed systems, supporting investigation of multi-stage attacks. Subject and tenant identifiers enable multi-tenant log analysis.Log Pipeline Architecture
Pipeline Stages Log pipelines follow collect, parse, normalize, enrich, route, and store pattern. Collection agents gather logs from sources using protocols including syslog, HTTP, and file tailing. Parsing extracts structured fields from unstructured log messages using regular expressions, grok patterns, or custom parsers. Parsing failures should be logged and monitored, as unparsed logs create detection blind spots. Normalization maps source-specific fields to standardized schema, enabling consistent analysis. Enrichment adds context including geolocation, threat intelligence reputation, and asset information. Routing directs logs to appropriate storage tiers and analysis pipelines based on log type, priority, and retention requirements. Storage persists logs for analysis, investigation, and compliance. Storage Tiers Hot storage provides fast access for recent logs used in active detection and investigation, typically using SSDs or in-memory databases. Cold storage provides cost-effective long-term retention for compliance and historical analysis. Tiered retention aligns storage duration with risk and legal requirements. Critical security logs may require 90-day hot retention and one-year cold retention, while verbose application logs may have shorter retention. Automated lifecycle management transitions logs from hot to cold storage based on age, optimizing costs while maintaining required retention. Cost Management Sampling reduces log volume for verbose sources while maintaining statistical representativeness. Sampling should be applied carefully to avoid missing security events. Suppression eliminates duplicate or low-value logs, reducing storage and processing costs. Deduplication identifies and removes duplicate log entries. Log volume monitoring and alerting detect unexpected volume increases that may indicate attacks, misconfigurations, or cost overruns.Detection Engineering
Use Case Development Detection use cases should be tied to MITRE ATT&CK framework, ensuring coverage of relevant attack techniques. Use case library documents detection logic, data sources, expected false positive rates, and response procedures. Versioned detection rules with tests and test datasets enable continuous improvement and regression testing. Rule changes should be tested before production deployment. Detection rules should include metadata documenting rule purpose, severity, data sources, and expected alert volume. Correlation and Precision Single-source detections often produce high false positive rates. Correlation across identity, endpoint, and network logs provides higher precision by requiring multiple indicators. Multi-stage attack detection identifies attack patterns spanning multiple events over time. Correlation windows define how long to wait for related events. Precision and recall metrics measure detection effectiveness. Precision indicates what percentage of alerts are true positives, while recall indicates what percentage of actual attacks are detected. Iterative Tuning Detection rules require continuous tuning based on false positive analysis and threat landscape changes. High false positive rates create alert fatigue and reduce detection effectiveness. Low-value rules that consistently produce false positives without detecting genuine threats should be deprecated or significantly modified. Rule effectiveness should be measured and reviewed regularly. Tuning should balance false positive reduction with maintaining detection coverage. Overly aggressive tuning may eliminate genuine detections.SIEM Governance
Access Control and Audit SIEM access should be controlled based on least privilege, with role-based access control limiting who can view which logs. Sensitive logs including authentication and financial transactions require stricter access controls. SIEM access should be audited comprehensively, logging who accessed which logs when. Audit logs enable detection of unauthorized access and compliance reporting. Tamper-evident log storage prevents attackers from covering tracks by modifying logs. Append-only storage or cryptographic hashing provides tamper evidence. Privacy and Compliance PII minimization reduces privacy risks by eliminating unnecessary personal information from logs. Tokenization replaces sensitive data with tokens, enabling analysis without exposing sensitive information. Data retention policies should comply with legal and regulatory requirements while minimizing data retention to reduce privacy risks. Automated retention enforcement prevents accidental over-retention. Cross-border data transfer restrictions may require regional log storage, complicating centralized SIEM architectures. Data Quality Data quality Service Level Indicators (SLIs) measure log pipeline health including ingestion latency, log completeness, and parsing success rates. Poor data quality creates detection blind spots. Pipeline health dashboards provide visibility into log source status, parsing errors, and storage utilization. Alerts on pipeline issues enable rapid remediation before detection gaps emerge. Log source monitoring detects when expected logs stop arriving, indicating source failures or network issues.Operational Excellence
Playbook Integration SIEM alerts should integrate with incident response playbooks documenting investigation and response procedures. Playbooks ensure consistent response and reduce mean time to respond. Automated enrichment adds context to alerts including asset criticality, user risk scores, and related events. Enrichment enables faster triage and investigation. Case Management Alert-to-case workflows ensure that alerts are tracked through investigation and resolution. Case management provides audit trails for compliance and continuous improvement. Case metrics including time to triage, time to investigate, and time to resolve measure operational efficiency. Trending metrics identify improvement opportunities. Continuous Improvement Post-incident reviews identify detection gaps and false negatives, driving detection rule improvements. Lessons learned should be incorporated into detection use cases. Detection coverage mapping against MITRE ATT&CK identifies gaps where attacks may go undetected. Coverage expansion should prioritize highest-risk gaps.Conclusion
SIEM and log management require treating logging as an internal product with clear data strategy, scalable pipelines, and high-quality detections. Security engineers design SIEM architectures that balance comprehensive coverage with cost management and detection engineering that produces actionable alerts. Success requires continuous investment in detection engineering, pipeline optimization, and data quality management. Organizations that invest in SIEM fundamentals build detection capabilities that scale with organizational growth while maintaining high signal-to-noise ratios.References
- MITRE ATT&CK Framework
- Elastic Common Schema (ECS)
- Open Source Security Events Metadata (OSSEM)
- NIST SP 800-92 Guide to Computer Security Log Management
- SANS SIEM Best Practices