Data Collection and Ingest
Data Sources Agent-based sources include EDR (Endpoint Detection and Response) agents. Agents provide deep visibility. Agentless sources include network monitoring and cloud APIs. Agentless sources reduce deployment burden. Identity sources include authentication logs and directory services. Identity data enables user behavior analytics. Cloud API sources include AWS CloudTrail, Azure Activity Log, and GCP Cloud Logging. Cloud APIs provide control plane visibility. Application logs provide application-level visibility. Application logs enable application security monitoring. Ingest Pipeline Backpressure handling prevents data loss during spikes. Backpressure protects pipeline. Replay capability enables reprocessing. Replay enables correction. Schema validation ensures data quality. Validation prevents bad data. Ingest should be resilient to source failures. Resilience ensures availability. Ingest metrics should be tracked. Metrics show pipeline health. Collection Architecture Distributed collection reduces latency. Distribution improves performance. Buffering handles temporary outages. Buffering prevents data loss. Compression reduces bandwidth. Compression reduces cost. Encryption in transit protects data. Encryption ensures confidentiality.Data Normalization and Enrichment
Common Schemas Common schemas enable consistent analysis. Consistency improves detection. Elastic Common Schema (ECS) provides standard field names. ECS enables cross-source correlation. Open Source Security Events Metadata (OSSEM) provides event taxonomy. OSSEM enables standardization. Schema mapping translates source formats to common schema. Mapping enables normalization. Enrichment Enrich with asset information. Asset context improves triage. Enrich with identity information. Identity context enables attribution. Enrich with geolocation. Geolocation enables geographic analysis. Enrich with threat intelligence. Threat intelligence provides threat context. Attach trace IDs for distributed tracing. Trace IDs enable request correlation. Attach tenant IDs for multi-tenancy. Tenant IDs enable isolation. Enrichment Architecture Enrichment should be near real-time. Real-time enrichment enables timely detection. Enrichment sources should be cached. Caching improves performance. Enrichment failures should be handled gracefully. Graceful handling prevents pipeline failures. Enrichment should be versioned. Versioning enables rollback.Storage and Query
Storage Tiers Hot tier provides fast search. Hot tier suits recent data. Cold tier provides cost-effective long-term storage. Cold tier suits historical data. Tiering should be automated based on age. Automation reduces cost. Data lifecycle policies manage tiering. Policies ensure appropriate storage. SIEM vs Data Lakehouse SIEM provides security-focused search and analytics. SIEM suits security operations. Data lakehouse provides flexible analytics on raw data. Lakehouse suits advanced analytics. Hybrid approach combines SIEM and lakehouse. Hybrid provides flexibility. SIEM should integrate with lakehouse. Integration enables comprehensive analysis. Storage Formats Columnar formats (Parquet, ORC) optimize analytics. Columnar formats improve query performance. Table partitioning by time improves query performance. Partitioning reduces data scanned. Compression reduces storage cost. Compression should be balanced with query performance. Indexing improves search performance. Indexing should be selective. Analytics Patterns Streaming analytics provide near-real-time detection. Streaming suits time-sensitive detections. Batch analytics provide deep forensics. Batch suits complex analysis. Backfill workflows enable historical analysis. Backfill enables retroactive detection. Query optimization reduces cost. Optimization should be continuous.Data Governance and Privacy
Data Classification Security data should be classified. Classification drives protection. Sensitive data requires stronger protection. Protection should match sensitivity. Data classification should be automated. Automation ensures consistency. Data Retention Retention policies should balance compliance and cost. Balance optimizes value. Regulatory requirements drive minimum retention. Compliance is mandatory. Retention should be automated. Automation ensures compliance. Deletion should be verified. Verification ensures compliance. PII Minimization and Tokenization Personally Identifiable Information (PII) should be minimized. Minimization reduces privacy risk. Tokenization replaces PII with tokens. Tokenization enables analysis while protecting privacy. Pseudonymization provides one-way transformation. Pseudonymization protects identity. PII handling should comply with regulations. Compliance is mandatory. Access Controls Access to security data should be controlled. Access control prevents unauthorized access. Role-based access control (RBAC) limits access by role. RBAC provides coarse-grained control. Attribute-based access control (ABAC) provides fine-grained control. ABAC enables context-aware access. Access should be logged. Logging enables audit. Audit Trails Data access should be audited. Audit enables accountability. Audit logs should be immutable. Immutability prevents tampering. Audit logs should be retained. Retention enables investigation. Audit logs should be monitored. Monitoring detects anomalies.Platform Reliability and Cost
Service Level Indicators (SLIs) Ingest latency measures time from event to availability. Latency should be minimized. Data completeness measures percentage of expected data received. Completeness should be high. Parse success rate measures percentage of successfully parsed events. Parse success should be high. Query performance measures query response time. Performance should meet SLOs. Autoscaling Autoscaling handles variable load. Autoscaling ensures availability. Autoscaling should be based on metrics. Metrics-based scaling is responsive. Autoscaling should have limits. Limits prevent runaway cost. Cost Optimization Sampling reduces data volume for high-volume sources. Sampling reduces cost. Deduplication removes duplicate events. Deduplication reduces storage. Compression reduces storage and bandwidth. Compression reduces cost. Tiering moves old data to cheaper storage. Tiering optimizes cost. Query optimization reduces compute cost. Optimization should be continuous. Reliability Platform should be highly available. Availability ensures operations. Platform should be resilient to failures. Resilience prevents data loss. Disaster recovery should be tested. Testing validates recovery. Monitoring should be comprehensive. Monitoring enables rapid response.Data Products and APIs
Detections-as-Code Detections should be stored in version control. Version control enables collaboration. Detections should be tested. Testing validates effectiveness. Detection deployment should be automated. Automation ensures consistency. Detection performance should be monitored. Monitoring shows effectiveness. Investigation Notebooks Investigation notebooks enable interactive analysis. Notebooks suit complex investigations. Notebooks should integrate with data platform. Integration enables access. Notebooks should be shareable. Sharing enables collaboration. Notebook templates accelerate investigations. Templates provide starting points. Case Management Integration Data platform should integrate with case management. Integration enables workflow. Evidence should be exportable. Export enables case documentation. Evidence should be tamper-evident. Tamper-evidence ensures integrity. APIs and Extensibility APIs enable programmatic access. APIs enable automation. APIs should be well-documented. Documentation enables adoption. APIs should be versioned. Versioning enables evolution. Rate limiting prevents abuse. Rate limiting protects platform.Conclusion
Security telemetry and data platform treats security data as foundational infrastructure powering detections, investigations, and audits. Security engineers design resilient pipelines and models for collection, normalization, enrichment, storage, analytics, governance, and cost control. Success requires resilient collection from agent and agentless sources, normalization to common schemas with enrichment, tiered storage with SIEM and lakehouse, governance with classification and retention, reliability with SLIs and autoscaling, and data products with detections-as-code and investigation notebooks. Organizations that invest in data platforms build effective security operations.References
- Elastic Common Schema (ECS)
- Open Source Security Events Metadata (OSSEM)
- Sigma Detection Rule Format
- MITRE ATT&CK Framework
- Modern Data Lakehouse Patterns (Delta Lake, Apache Iceberg)
- NIST SP 800-92 Guide to Computer Security Log Management
- Cloud Security Alliance (CSA) Security Guidance