SIEM Operations: Detection Engineering and Log Management

Part of the Cybersecurity Skills Guide — This article is one deep-dive in our complete guide series.

By HADESS Team | February 28, 2026 | Updated: February 28, 2026 | 5 min read

A SIEM is only as good as the data going into it and the rules running against that data. Deploying Splunk, Sentinel, or Elastic Security is step one. Getting it to reliably detect real threats without drowning analysts in false positives is where the actual work begins.

Log Ingestion

The first challenge is getting the right logs into the SIEM at the right volume and latency. Not all logs have equal value for security detection. Prioritize:

High-value log sources (ingest first):

• Authentication logs (Active Directory, LDAP, SSO providers)
• EDR/AV telemetry (process creation, file modifications, network connections)
• Firewall and proxy logs (connection permits/denies, URL categories)
• DNS query logs (from internal resolvers)
• Email gateway logs (sender, recipient, attachment hashes, disposition)

Medium-value log sources (ingest next):

• Web server access logs
• VPN authentication and session logs
• Cloud platform audit logs (AWS CloudTrail, Azure Activity Log, GCP Audit Logs)
• Database audit logs for sensitive systems

Lower-priority sources (ingest if budget allows):

• DHCP logs (IP-to-hostname mapping)
• Print server logs
• Badge access logs (useful for insider threat programs)

Normalize log formats during ingestion. Different sources use different field names for the same concept — source IP might be src_ip, SourceAddress, or c-ip depending on the vendor. Map everything to a common schema (CIM, ECS, or OCSF) so detection rules work across log sources.

Correlation Rules

Single-event detection rules catch known-bad indicators: a specific malware hash, a connection to a known C2 IP, or a login from an impossible geography. These are straightforward but limited.

Correlation rules combine multiple events to detect behaviors rather than indicators. Examples:

• Brute force detection: more than 10 failed logins from the same source to the same destination within 5 minutes, followed by a successful login
• Lateral movement: a single account authenticating to more than 5 systems within 10 minutes using network logon type
• Data exfiltration: large outbound data transfer from a system that does not normally send data externally, preceded by internal reconnaissance activity

Write correlation rules with enough specificity to reduce false positives. “More than 5 failed logins” fires constantly. “More than 5 failed logins followed by a successful login from the same source, where the destination is a domain controller” is far more actionable.

Detection Engineering

Detection engineering treats detection rules as code. Version-control your rules, test them against labeled datasets, and review them through the same process you use for application code.

Use the Sigma format to write detection rules that are platform-independent. A Sigma rule can be converted to Splunk SPL, Sentinel KQL, Elastic Query DSL, or other SIEM query languages. This decouples your detection logic from your SIEM vendor.

Map every detection rule to MITRE ATT&CK techniques. This lets you visualize your detection coverage as a heat map against the ATT&CK matrix, identifying gaps where you have no detection capability.

Test new rules against historical data before deploying to production. Run them in alert-only mode for a week before enabling automated response actions. Measure the true positive rate and tune thresholds before the rule generates cases for analysts.

False Positive Tuning

False positives destroy SOC efficiency and morale. Every analyst knows the feeling of investigating their hundredth “suspicious PowerShell execution” alert that turns out to be a legitimate sysadmin script.

Track false positive rates per rule. Rules above 70% false positive rate need immediate attention — either tune them, add exclusions, or replace them with better detection logic.

Common tuning approaches:

• Whitelist known-good sources (vulnerability scanners, admin workstations, monitoring systems)
• Add time-based conditions (this activity is normal during business hours but suspicious at 3 AM)
• Increase thresholds from static counts to statistical deviations from baseline
• Add enrichment lookups (is this user in the IT admin group? Is this IP in the CDN range?)

Next Steps

• Evaluate your detection engineering capabilities with the skills assessment
• Explore monitoring and response topics in the skills library
• Plan your path with the certificate roadmap — GCIA and BTL1 cover SIEM operations extensively

Related Guides in This Series

• EDR: Endpoint Detection, Response, and Threat Hunting — HADESS | 2026
• Firewall Management: Rules, Zones, and Change Control — HADESS | 2026
• Hardware Security Modules: Key Management and Compliance — HADESS | 2026

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