Ignacio Corrales
Principal Machine Learning Engineer at Cisco
Oakland, California, United States
Summary
🤩
Rockstar🎓
Top SchoolIgnacio Corrales is a Principal Machine Learning Engineer with 11 years of experience applying generative models and multi-agent AI to real-world security problems, now enabling AI to interact with Cisco products from Oakland. His career spans security-focused roles at Palo Alto Networks and Splunk, where he built and tested Splunk Security Analytics detections and improved attack simulation tooling by dramatically speeding data extraction and replay. Trained as a PhD researcher in telecommunications, he combines rigorous traffic characterization research with hands-on engineering across detection, automation, and DevOps. Ignacio is particularly drawn to multi-agent systems and agent-driven automation, leveraging generative approaches not just for detection but for operationalizing security workflows. He contributes to well-known open-source security projects, bringing practical improvements—like REST-API-based extraction—for measurable performance gains.
11 years of coding experience
12 years of employment as a software developer
Pontificia Universidad Católica de ValparaísoPhD, Telecommunication Engineering, PhD, Telecommunication Engineering at Politecnico di Torino
Github Skills (18)
python10
cybersecurity10
splunk10
detectors10
automation10
aws10
yaml10
ansible10
ansible-galaxy10
automations10
testing9
terraform9
security9
it-security9
terraformer9
Programming languages (14)
Github contributions (5)
splunk/attack_range
Jun 2020 - Jan 2021
A tool that allows you to create vulnerable instrumented local or cloud environments to simulate attacks against and collect the data into Splunk
Role in this project:
DevOps & Security Engineer
DevOps & Security EngineerContributions:13 reviews, 48 commits, 21 PRs in 7 months
Contributions summary:Ignacio primarily contributed to the automation and enhancement of the attack data collection and simulation processes. They implemented the ability to dump Splunk search results into files, allowing for easier analysis and replay. The user refactored the Splunk search dump, introduced the ability to replay attack data, and incorporated a method to dump attack data, making the attack range more functional. The user also focused on improving the efficiency and reliability of the data extraction process, including changing the extraction method from the Splunk Python SDK to directly querying the Splunk REST API, resulting in substantial performance gains.
simulationattack-simulationsimulationslabsimulate
splunk/security_content
Oct 2020 - Mar 2021
Splunk Security Content
Role in this project:Security Engineer
Security EngineerContributions:19 reviews, 186 commits, 55 PRs in 5 months
Contributions summary:Ignacio primarily contributed to the security content repository, specifically focusing on developing and testing Splunk Security Analytics (SSA) detections. Their commits involve creating and refining SPL2 queries designed to identify security threats, particularly in relation to the LOLBAS project and kerberoasting. The user also implemented a testing framework to validate these detections, ensuring accurate and reliable threat detection capabilities.
security-toolsdetectionsecuritycybersecuritysplunk
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