Description

This curriculum spans the design and operationalization of a production-grade ELK Stack deployment for security logging, comparable in scope to a multi-phase infrastructure hardening initiative or an internal SOC capability buildout, covering end-to-end workflows from ingestion and normalization to detection, access control, and compliance alignment.

Module 1: Architecture Design for Scalable Log Ingestion

Selecting between Filebeat, Logstash, or Fluentd based on parsing complexity, resource constraints, and protocol requirements in heterogeneous environments.
Designing log forwarder placement strategies (sidecar vs. host-level vs. centralized collectors) to balance network overhead and operational manageability.
Configuring persistent queues in Logstash to prevent data loss during downstream Elasticsearch outages or indexing spikes.
Implementing TLS encryption and mutual authentication between Beats and Logstash to secure log transmission across untrusted networks.
Partitioning log ingestion pipelines by source type or security domain to isolate parsing logic and prevent cross-contamination of parsing failures.
Evaluating buffer mechanisms (in-memory vs. disk-based) in Logstash for resilience during Elasticsearch cluster maintenance or GC pauses.

Module 2: Parsing and Normalization of Security Events

Writing Grok patterns to extract structured fields from non-standard firewall, IDS, and endpoint detection logs while minimizing CPU overhead.
Mapping disparate timestamp formats from Windows Event Logs, Unix syslog, and cloud provider audit trails into a unified @timestamp field.
Using dissect filters in Logstash for high-performance parsing of fixed-format logs when regex is unnecessary.
Handling multi-line log entries from application security scanners or stack traces using Filebeat's multiline configuration with precise pattern anchoring.
Enriching logs with GeoIP data at ingestion time using Logstash’s geoip filter, considering accuracy trade-offs and update frequency.
Standardizing field names across vendors using ECS (Elastic Common Schema) while preserving original fields for forensic traceability.

Module 3: Elasticsearch Index Management and Data Lifecycle

Defining index templates with appropriate shard counts and mappings to optimize search performance for high-cardinality security fields like IP addresses and user IDs.
Implementing ILM (Index Lifecycle Management) policies to automate rollover, shrink, and deletion of security indices based on retention compliance requirements.
Configuring cold and frozen tiers to archive historical security logs for audit access while minimizing storage costs.
Setting up data streams to manage time-series security logs with automated indexing and versioning.
Allocating dedicated ingest nodes to isolate parsing load from search and storage roles in large-scale deployments.
Monitoring and tuning refresh intervals and translog settings to balance indexing throughput with search latency for real-time detection use cases.

Module 4: Secure Access Control and Audit Logging

Configuring role-based access control (RBAC) in Kibana to restrict log visibility by team, sensitivity level, and incident response role.
Implementing field-level security to mask sensitive data such as passwords or PII in raw logs while allowing authorized analysts full access.
Enabling audit logging in Elasticsearch to track user queries, configuration changes, and authentication attempts for compliance reporting.
Integrating with external identity providers (LDAP, SAML, or Okta) to enforce centralized authentication and group synchronization.
Defining index patterns in Kibana that align with security domains (e.g., network, endpoint, cloud) to prevent accidental cross-domain queries.
Rotating API keys and service account credentials used by ingestion pipelines on a defined schedule, with automated rotation scripts.

Module 5: Detection Engineering and Alerting Workflows

Writing Elasticsearch queries using bool, range, and aggregations to detect brute-force attacks across multiple failed authentication logs.
Configuring Kibana Watcher alerts with throttling to prevent notification storms during widespread scanning events.
Building detection rules that correlate events across sources (e.g., firewall deny followed by successful SSH login) using sequence matching.
Setting up threshold-based alerts for anomalous volume spikes in DNS query logs indicative of data exfiltration.
Using machine learning jobs in Elastic to baseline normal user behavior and flag deviations in login times or geographic locations.
Validating alert logic with historical log data to reduce false positives before enabling real-time notifications.

Module 6: Performance Tuning and Operational Monitoring

Identifying slow Logstash filters using monitoring APIs and replacing regex-heavy patterns with dissect or CSV filters.
Adjusting bulk request sizes and flush intervals in Beats to maximize throughput without overwhelming Elasticsearch indexing capacity.
Monitoring Elasticsearch thread pool rejections and tuning queue sizes or scaling data nodes to handle ingestion bursts.
Using Kibana’s monitoring dashboards to correlate JVM memory pressure with garbage collection frequency in long-running nodes.
Implementing log sampling for low-priority sources during peak loads to preserve resources for critical security feeds.
Scheduling regular snapshot backups of critical indices to a remote repository with access controls and integrity checks.

Module 7: Integration with Security Ecosystems

Forwarding detection alerts from Kibana to SIEMs or SOAR platforms via webhook or syslog with structured JSON payloads.
Ingesting threat intelligence feeds (STIX/TAXII or CSV) into Elasticsearch to enrich logs with known malicious IPs or hashes.
Configuring Elastic Agent integrations to collect and parse cloud security logs from AWS CloudTrail, Azure Monitor, or GCP Audit Logs.
Using Elasticsearch’s _search API to allow external incident response tools to programmatically query logs during investigations.
Mapping Elastic detection rules to MITRE ATT&CK techniques for standardized threat modeling and reporting.
Synchronizing case management data between Kibana and external ticketing systems (e.g., Jira) using bidirectional webhooks.

Module 8: Compliance and Forensic Readiness

Implementing write-once, read-many (WORM) storage policies using Index State Management to meet legal hold requirements.
Generating immutable audit trails by signing log batches with HMAC during ingestion for later integrity verification.
Documenting data lineage from source to index, including parsing transformations, for regulatory audits.
Configuring field and document-level encryption for logs containing regulated data (e.g., PCI, HIPAA) using Elasticsearch’s security features.
Preserving raw log messages alongside parsed fields to support forensic reprocessing when detection logic evolves.
Conducting periodic log retention reviews to align index deletion policies with changing compliance obligations.