From harness-claude
Correlates events across log sources like auth, cloud audits, network flows to detect multi-stage attacks in SIEM systems. Useful for designing SIEM, building correlation rules, reducing alert fatigue, and compliance.
npx claudepluginhub intense-visions/harness-engineering --plugin harness-claudeThis skill uses the workspace's default tool permissions.
> Correlate events across multiple log sources to detect attacks that are invisible in any
Designs SIEM detection rules using correlation, thresholds, and behavioral analytics mapped to MITRE ATT&CK for Splunk, Elastic, and Sentinel. For SOC coverage gaps and use case lifecycle.
Implements SIEM detection use cases with correlation rules, threshold alerts, and behavioral analytics mapped to MITRE ATT&CK for Splunk, Elastic, and Sentinel. For SOC teams expanding coverage or building detection libraries.
Creates, validates, converts Sigma detection rules for SIEM platforms including Splunk, Elastic, QRadar, Sentinel. Supports threat hunting, MITRE ATT&CK mapping, detection-as-code, and compliance monitoring.
Share bugs, ideas, or general feedback.
Correlate events across multiple log sources to detect attacks that are invisible in any single log stream -- because attackers do not confine their activities to one system
Modern attacks span multiple systems: an attacker phishes credentials (email logs), logs in from an anomalous location (authentication logs), escalates privileges (directory service logs), accesses sensitive data (application logs), and exfiltrates it (network flow logs). Each individual event may appear benign in isolation. A login from a new country is unusual but not necessarily malicious. A large data download might be a legitimate export. An admin privilege grant could be a routine onboarding. Only by correlating events across sources does the attack pattern emerge: new-location login -> privilege escalation -> bulk data access -> outbound data transfer = likely account compromise and data exfiltration.
The 2020 SolarWinds SUNBURST attack evaded detection for 9+ months because the attackers specifically designed their activity to blend into normal operations. They used legitimate credentials, operated during business hours, and limited the volume of their data exfiltration. Detection required correlating anomalous DNS queries to unusual API access patterns to unexpected cloud configuration changes -- a correlation that most organizations' monitoring infrastructure was not configured to perform.
The IBM/Ponemon 2023 Cost of a Data Breach Report found that organizations with security AI and automation (which depends on correlated, structured log data) identified and contained breaches 108 days faster than those without, saving an average of $1.76 million per breach. The detection gap is not a data problem -- organizations collect massive volumes of logs -- it is a correlation and analysis problem.
Identify and onboard critical log sources. Prioritize log sources by detection value: authentication systems (Active Directory, Okta, Auth0 -- login success, failure, MFA events), cloud audit logs (AWS CloudTrail, GCP Cloud Audit Logs, Azure Activity Log -- API calls, configuration changes, resource access), application audit logs (business logic events, data access, administrative actions), network flow logs (VPC Flow Logs, firewall logs -- lateral movement, data exfiltration indicators), DNS logs (query logs reveal C2 communication, data exfiltration via DNS tunneling, domain generation algorithm activity), and endpoint detection (EDR telemetry -- process execution, file access, registry changes). Start with authentication and cloud audit logs -- they provide the highest detection value per integration effort.
Normalize log formats into a common schema. Logs from different sources use different field names, timestamp formats, and structures. Normalize all ingested logs to a common schema so that correlation rules can reference consistent field names. Common schemas: Elastic Common Schema (ECS), Open Cybersecurity Schema Framework (OCSF), MITRE CAR (Cyber Analytics Repository). At minimum, normalize: timestamp (UTC ISO 8601), source IP, destination IP, user identity, action performed, outcome (success/failure), and resource accessed. Without normalization, every correlation rule must account for format differences across sources.
Build correlation rules for known attack patterns. Start with high-confidence, low-volume detections: account compromise indicators (failed login followed by successful login from a different IP within 10 minutes), impossible travel (successful logins from geographically distant locations within an impossible time window), brute force (more than N failed logins against a single account within M minutes), privilege escalation (admin role granted followed by unusual resource access), and lateral movement (authentication from an internal IP that has never accessed that service before, especially after an initial compromise indicator). Each rule should have a documented rationale, expected false-positive rate, and a runbook.
Align detections with MITRE ATT&CK. Map each detection rule to a MITRE ATT&CK technique (e.g., brute force detection maps to T1110, impossible travel maps to T1078.004). This provides coverage visibility: which ATT&CK techniques do your detections cover, and which are gaps? Use the ATT&CK Navigator to visualize coverage. Prioritize detections for techniques used by threat actors relevant to your industry (use MITRE ATT&CK Groups and Software databases). Detection engineering is not about covering every technique -- it is about covering the techniques most likely to be used against your specific organization.
Use Sigma rules for portable detection logic. Sigma is an open standard for writing
detection rules in a SIEM-agnostic YAML format. A single Sigma rule can be converted to
Splunk SPL, Elastic KQL, Microsoft Sentinel KQL, CrowdStrike, and other SIEM query
languages using the sigma-cli converter. Benefits: share detection rules across
organizations, leverage the community Sigma rule repository (thousands of rules covering
common attack techniques), write rules once and deploy to any SIEM. Store custom Sigma rules
in version control and treat them as code with review, testing, and deployment processes.
Manage alert fatigue systematically. Alert fatigue is the primary failure mode of SIEM deployments. When analysts receive more than 50-100 alerts per day, they begin ignoring alerts, missing real incidents among the noise. Management strategies: severity classification (critical alerts page on-call, high alerts go to the queue, medium alerts go to a daily review batch, low alerts are logged but not alerted), progressive escalation (first occurrence goes to Slack, repeated pattern pages), tuning cadence (review the top 10 noisiest rules monthly, disable or refine rules with false-positive rates above 90%), and alert-to-incident ratio tracking (target at least 1 true positive per 10 alerts; below 1:100 means the rule is noise).
Attach runbooks to every alert. Every alert that fires should include a link to a runbook that tells the analyst: what this alert means, what to investigate first (specific queries, dashboards, or data to check), how to determine if it is a true positive, what the escalation path is if confirmed, and what the containment actions are. Alerts without runbooks force analysts to investigate from scratch every time, increasing response time and analyst burnout. Runbooks should be living documents updated after every investigation.
SIEM architecture components: Collection layer (Fluentd, Filebeat, Logstash, cloud-native log forwarders -- collect logs from sources and forward to the SIEM), normalization layer (parse raw logs into structured fields using the common schema), storage layer (hot storage for recent logs with fast query performance, warm storage for older logs with slower queries, cold storage for archive and compliance), correlation engine (evaluates rules against incoming events in real time or near-real-time), alerting layer (routes triggered rules to notification channels with severity-based routing), and investigation layer (search interface, dashboards, and forensic tools for analyst investigation).
SIEM tool landscape: Splunk (market leader, powerful SPL query language, expensive at scale), Elastic SIEM (open-source core, ECS normalization, good for organizations with existing Elasticsearch investment), Microsoft Sentinel (cloud-native, strong Azure and M365 integration, KQL query language), CrowdStrike Falcon LogScale (high-ingestion performance, integrated with CrowdStrike EDR), Wazuh (open-source, combined SIEM and endpoint detection), Security Onion (open-source, combines multiple tools for network security monitoring). Choice depends on: budget, existing infrastructure, log volume, team expertise, and integration requirements.
Detection engineering as a practice: Detection engineering treats detection rules as software: version-controlled, tested, reviewed, and deployed through a CI/CD pipeline. Each detection has: a hypothesis (what attack behavior it detects), a rule (the query logic), test data (synthetic events that should and should not trigger the rule), a tuning history (false-positive analysis and adjustments), and performance metrics (true positive rate, mean time to detect, analyst feedback). The Palantir Detection-as-Code framework and Florian Roth's Sigma project are leading examples of this practice.
Correlation rule complexity levels: Level 1 -- single-source, single-event (failed login count > threshold). Level 2 -- single-source, multi-event (failed logins followed by successful login). Level 3 -- multi-source, multi-event (authentication anomaly + privilege change + data access). Level 4 -- behavioral baseline deviation (user accessing resources they have never accessed before, at unusual times, in unusual volumes). Start with Level 1 and 2 rules, which are easier to implement and tune. Level 3 and 4 require more mature log ingestion and normalization.
Collecting logs without correlation rules -- a data lake, not a SIEM. Ingesting terabytes of logs into a central store without writing detection rules means you have an expensive log archive, not a security monitoring capability. Logs provide detection value only when correlation rules actively analyze them. Start with 10-20 high-confidence detection rules rather than attempting to ingest every possible log source first.
Alert storms from untuned rules. Deploying default detection rules without tuning for the environment generates hundreds or thousands of alerts daily. Analysts quickly learn to ignore the SIEM, and real incidents are buried in noise. Research consistently shows that more than 100 alerts per analyst per day leads to alert fatigue and missed detections. Start with a small set of tuned, high-confidence rules and expand gradually.
SIEM as a compliance checkbox without active monitoring. Deploying a SIEM to satisfy SOC2 or PCI-DSS audit requirements but assigning no one to monitor it. Alerts fire into an unmonitored inbox. The SIEM provides forensic value (logs are available for post-incident investigation) but zero detection value. A SIEM without dedicated analysts or an MSSP (Managed Security Service Provider) monitoring it is a waste of the investment.
No runbooks attached to alerts. Alerts that provide a title and a severity but no investigation guidance. The analyst receives "Impossible Travel Detected -- High" and must figure out from scratch what to check, where to look, and how to determine if it is real. Every alert must link to a runbook with specific investigation steps, escalation criteria, and containment actions.
Alerting on everything instead of known attack patterns. Attempting to detect every possible anomaly rather than focusing on known attack patterns mapped to MITRE ATT&CK. Generic anomaly detection produces enormous volumes of alerts with low signal-to-noise ratio. Start with detections for specific, known attack techniques relevant to your threat model, and expand coverage based on threat intelligence and incident learnings.
No feedback loop from investigations to rule tuning. Analysts investigate alerts, resolve them, and move on without feeding their findings back to the detection engineering team. Each investigation should produce one of: a confirmed true positive (document the attack pattern for training), a false positive (tune the rule to exclude this pattern), or a detection gap (the alert fired for the wrong reason, but the investigation revealed a real issue that needs a new detection rule). Without this feedback loop, detection quality stagnates.