Conduct organizational readiness assessments, track emerging AI attack vectors, and present risk mitigation roadmaps and investment recommendations to technology leadership.
Qualifications
Required
Deep background in SOC monitoring, detection engineering, SIEM/SOAR rule creation, alerting pipelines, and threat hunting workflows.
Hands-on experience securing Generative AI, Agentic AI, Large Language Models (LLMs), Model Context Protocol (MCP), and AI APIs—especially within AWS cloud environments.
Practical application of MITRE ATLAS, OWASP for LLM Applications, and NIST AI RMF for threat modeling and control mapping.
Experience designing and testing attack scenarios against AI workloads to validate defensive controls.
Proven ability to architect logging, telemetry pipelines, and data visibility requirements for cloud and AI/ML workloads.
Understanding of security best practices for AI service accounts, automated agents, API tokens, and machine-to-machine integrations.
Experience driving security architecture reviews, leading executive briefings, and translating complex AI risks into business decisions.
Bachelor's degree in Cybersecurity, Computer Science, Information Systems, Data Science, Engineering, or equivalent practical experience.
Preferred
Advanced degree in a related field.
CISSP, CCSP, GIAC (e.g., GDSA, GCIA, GCIH), or AWS Certified Security – Specialty.
Advanced in AI Risk (AAIR), Advanced in AI Security Management (AAISM), CompTIA Security AI+, or equivalent specialized training.
AWS Certified AI Practitioner or AWS Certified Cloud Practitioner.
Core Competencies
Strong analytical mindset to parse complex telemetry and detect subtle, novel adversary techniques.
Executive-level communication skills to present roadmaps, project health, and threat intelligence to directors, vice presidents, and engineering peers.
Collaborative and adaptable approach in fast-moving Agile environments with shifting enterprise priorities.
