Michael Maysonet

Built Python and SQL triage workflows for security/reliability alerts, cutting investigator handoff time for high-priority incidents from hours to under 30 minutes. Created detection logic in Splunk and CloudWatch for suspicious authentication, unusual API access, and privilege changes, generating 20+ actionable alerts per week and reducing false positives by 25%.

Owned security investigations across large-scale Kubernetes and AWS workloads by correlating EDR, SIEM, and application logs to trace anomalous behavior. Built Python automation for case enrichment/evidence collection (reducing repetitive analyst work by 50%) and improved detection rules for login abuse and data exfiltration indicators in a high-volume SaaS environment.

Built detections for application, cloud, and identity activity using SQL and Splunk, increasing visibility into suspicious access patterns. Investigated security anomalies in AWS and Kubernetes and documented repeatable response steps that cut mean investigation time by 30%, while automating alert enrichment to prioritize high-risk cases from a daily queue of 100+ events.

Built monitoring and logging features for SaaS services using Python, JavaScript, and SQL, improving traceability for production issues across customer environments. Developed internal diagnostics and alerting for APIs, databases, and infrastructure events, reducing troubleshooting time from days to hours for support and engineering.

Earned a Bachelor of Science in Computer Science from Inter American University of Puerto Rico–Bayamón from 2011 to 2016.