AI Security Researcher | Expert in Lightweight Intrusion Detection & Threat Modeling

Jisoo Park

Professional Summary

AI security researcher specializing in lightweight intrusion detection and threat modeling for IoT and cloud environments. Skilled in developing innovative AI-driven solutions to enhance cybersecurity defenses.

Work Experience

AI Security Research Intern at RedHawk Research Labs

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• Conducted comparative analysis of 90+ lightweight intrusion detection systems (IDS) for edge IoT networks. • Designed a hybrid DL-based anomaly detector (RAM < 10MB, CPU < 5%) showing 96+% detection on live IoT traffic. • Co-authored a technical whitepaper introducing adaptive IDS retraining using continual learning and privacy-preserving updates. • Developed benchmark suite for evaluating AI IDS models under adversarial, low-data, and concept-drift scenarios.

Agentic AI Threat Analyst at SecForge Open Initiative

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• Developed a full-stack AI security lifecycle model addressing agentic risks from deployment to inference pipelines. • Analyzed security risks including prompt injection, AI supply chain attacks, and synthetic data poisoning. • Published framework on “eBPF Observability for AI Pipelines” and contributed to an industry-wide AI threat model. • Co-lead author on a public guidance report for large enterprise LLMOps security (in use by two Fortune 500 companies).

Capture-the-Flag Competitor (Top 5% Global) at Cyber Security Org

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• Active on HackMeNow (Top 10 U.S.) and VulnZone CTF (Top 25 Global), solving 1,000+ challenges across threat emulation, crypto, reverse engineering, and red-team ops. • Winner of 2024 Cyber Collegiate Cup – AI x Threat Detection track.

Cloud Security Intern at CloudZen Security Inc.

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• Researched root causes of API misconfigurations in public cloud environments; proposed detection rules using automated CSPM tooling. • Created AWS IAM vulnerability maps and presented to client CISOs during tabletop exercises. • Co-created a scoring model to prioritize infrastructure vulnerabilities by exploitability and business impact.

Automation Engineering Intern at DeltaSky Systems

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• Built automated test infrastructure for security microservices, integrating Python-based tests into CI pipelines. • Implemented headless browser validation for login workflows using Selenium and test orchestration with Jenkins. • Identified and patched a session-fixation vulnerability in pre-prod login system.

Education

B.S. in Computer Science

Oklahoma Institute of Technology

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GPA: 3.9

Skills

Technical Skills

  • AI Threat Modeling
  • Penetration Testing
  • Network Security
  • LLMOps Security
  • CI/CD Security
  • Python
  • Bash
  • Docker
  • GitHub Actions
  • Zeek
  • YARA
  • MLflow

Certifications

Cybersecurity Specialization

Coursera (offered by University of Maryland)

Score
85/100
Education
80/100
Work Experience
85/100
Profile
88/100
Certifications
70/100
Header
90/100
Skills
75/100
Projects
82/100
Add consistent employment types and precise start/end dates for all work experiences to improve timeline clarity and ATS parsing.
Enhance skills section by including soft skills and relevant tools, organized by category and proficiency.
Incorporate links to professional profiles (LinkedIn, GitHub) in the header for recruiter convenience and ATS enrichment.
Expand education with location details, additional academic achievements, and consistent date formatting.
Include dates and technology stacks in project descriptions to increase keyword density and context.
Strong technical expertise in AI-driven lightweight intrusion detection with measurable performance metrics.
Proven research and publication experience including co-authoring whitepapers and industry reports.
Demonstrated competitive cybersecurity skills with top global Capture-the-Flag ranking and extensive challenge solving.
Diverse hands-on experience across AI security lifecycle, cloud security, and automation engineering internships.

Education

80%
Education section includes degree, institution, expected graduation date, and GPA, which is strong for a student or early-career candidate.
Listing relevant coursework supports technical background but could be expanded with academic projects or honors to strengthen this section.
Adding location of the institution and consistent date formatting would improve ATS parsing.

Work Experience

85%
Descriptions contain strong action verbs and quantifiable achievements, e.g., '96+% detection accuracy' and '1,000+ challenges solved', which boost ATS and recruiter interest.
Use of bullet points improves readability; however, including consistent employment type and more precise start/end dates would enhance clarity.
Consider adding a brief context or company description for lesser-known organizations to aid recruiter understanding.

Profile

88%
Summary is concise and keyword-rich, emphasizing specialization in lightweight intrusion detection and AI-driven cybersecurity solutions.
Profile highlights effectively showcase technical expertise, achievements, and relevant skills, enhancing recruiter appeal and ATS scoring.
Consider integrating measurable impact metrics directly in the summary for stronger initial impression.

Certifications

70%
Certification listed is relevant and recent, supporting candidate’s cybersecurity knowledge.
Include certification ID or link to credential verification if available to enhance credibility.
Adding more certifications or professional training related to AI security would increase competitiveness.
Clarify if certification is completed or in progress to avoid ambiguity.

Header

90%
Header includes a clear professional headline with keywords like 'AI Security Researcher' and 'Intrusion Detection' which improve ATS keyword matching.
Contact information is complete with email and phone number, but adding a LinkedIn or professional portfolio link could enhance recruiter access.

Skills

75%
Hard skills are well listed with relevant technical terms such as 'AI Threat Modeling', 'LLMOps Security', and 'Zeek' that align with job roles.
Soft skills and tools sections are empty; adding relevant soft skills (e.g., teamwork, communication) and tools (e.g., cloud platforms) would improve completeness.
Organize skills by categories or proficiency levels to improve ATS parsing and recruiter scanning.

Projects

82%
Projects are technically detailed with clear descriptions of objectives, methods, and results, demonstrating hands-on expertise.
Including measurable outcomes like '7.5% boost in AUROC' strengthens impact statements for recruiters and ATS.
Consider adding dates and technologies used for each project to provide better context and keyword density.
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