Results for “log-analytics”

Analyzes Claude Code session logs to report token usage, tool and skill rankings, and session search by prompt text.

Analyze Linux system logs including auth.log, syslog, systemd journal, and auditd to reconstruct user activity, detect unauthorized access, and establish event timelines on compromised systems.

Routes runtime-log requests into an evidence packet to isolate the first actionable blocker, repeated signature, blast radius, or safest next read-only check.

Choose and audit startup metrics using the Lean Analytics framework: separate actionable metrics from vanity metrics, identify the One Metric That Matters for your business model and stage, set targets, and plan instrumentation.

Parse Apache and Nginx access logs to detect SQL injection, LFI, XSS, scanner fingerprints, and brute-force patterns using regex-based detection, GeoIP enrichment, and statistical anomaly analysis.

Manage and monitor Heap product analytics by querying the Heap API for account info, defined events, user properties, segments, event volume, active users, and data health.

Set up, audit, and debug analytics tracking implementations including GA4, Google Tag Manager, event taxonomy, and conversion tracking.

Search and retrieve Gong call recordings, transcripts, users, and activity stats via the Gong API.

Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.

Search and retrieve Gong call recordings, transcripts, user lists, and activity statistics via the Gong API.

Hunt AADGraphActivityLogs and MicrosoftGraphActivityLogs in Microsoft Sentinel/Log Analytics for fingerprints of offensive Entra ID tools such as ROADtools, AADInternals, and AzureHound.

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs.

Detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis.

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls, and potential data exfiltration using statistical baselines.