Job Role: Security Architect – AI Cybersecurity Threat Monitoring
Location: Dallas, TX (Hybrid)
Position Overview
We are seeking a Security Architect specializing in AI Cybersecurity Threat Monitoring to establish and operationalize threat response capabilities targeting and leveraging AI systems. This role focuses on identifying, detecting, investigating, hunting, and responding to emerging threats across enterprise GenAI, agentic AI, foundation models, data sources, non-human identities, Model Context Protocol (MCP), and tool integrations.
Rather than siloing AI security, the Architect will integrate AI threat response, threat modeling, and detection engineering into existing SOC, IR, and Threat Hunting workflows using industry frameworks such as MITRE ATLAS, NIST AI RMF, and OWASP Top 10 for LLM Applications. This individual must move seamlessly between threat research, architecture road mapping, and hands-on detection engineering.
Key Responsibilities
- AI Threat Modeling & Attack Scenarios: Define and pilot an AI Threat Modeling capability; build a comprehensive library of AI attack scenarios mapped to the MITRE ATLAS framework to support Purple Teaming and Adversary Emulation.
- Detection Engineering & SIEM/SOAR: Design, build, test, and tune high-fidelity AI detection rules, correlation logic, and automated response playbooks within enterprise SIEM/SOAR platforms.
- AI Threat Hunting: Build proactive, AI-focused threat hunting playbooks and execute targeted hunts across enterprise GenAI platforms, cloud environments, and AI APIs.
- Telemetry & Logging Architecture: Define telemetry, logging, and observability requirements across AI systems (e.g., token usage, prompt/response telemetry, MCP integrations) and lead gap analyses to expand SOC visibility.
- Adversary Emulation & Purple Teaming: Partner with Threat Informed Defense and Purple Teams to validate AI-specific attack paths, test defense effectiveness, and tune alerting thresholds.
- Incident Response & Forensics Support: Collaborate with the IR team to author and refine AI-specific runbooks, investigation workflows, and digital forensics/evidence-gathering standards.
- Strategic Road mapping & Executive Advisory: Conduct organizational readiness assessments, track emerging AI attack vectors, and present risk mitigation roadmaps and investment recommendations to technology leadership.
Required Skills & Experience (Ranked by Importance)
- Security Operations, Detection Engineering & Threat Hunting: Deep background in SOC monitoring, detection engineering, SIEM/SOAR rule creation, alerting pipelines, and threat hunting workflows.
- AI & LLM Cybersecurity: Hands-on experience securing Generative AI, Agentic AI, Large Language Models (LLMs), Model Context Protocol (MCP), and AI APIs—especially within AWS cloud environments.
- Framework Proficiency: Practical application of MITRE ATLAS, OWASP for LLM Applications, and NIST AI RMF for threat modeling and control mapping.
- Purple Teaming & Adversary Emulation: Experience designing and testing attack scenarios against AI workloads to validate defensive controls.
- Telemetry & Observability Strategy: Proven ability to architect logging, telemetry pipelines, and data visibility requirements for cloud and AI/ML workloads.
- Non-Human Identity & Access Management: Understanding of security best practices for AI service accounts, automated agents, API tokens, and machine-to-machine integrations.
- Cross-Functional Architecture & Stakeholder Leadership: Experience driving security architecture reviews, leading executive briefings, and translating complex AI risks into business decisions.
Qualifications & Education
- Education: Bachelor’s degree in Cybersecurity, Computer Science, Information Systems, Data Science, Engineering, or equivalent practical experience. Advanced degree preferred.
- Basic Requirements: Must be at least 18 years old and legally authorized to work in the United States without sponsorship (Immigration Reform Act of 1986).
Preferred Certifications
- Security Architecture & Leadership: CISSP, CCSP, GIAC (e.g., GDSA, GCIA, GCIH), or AWS Certified Security – Specialty.
- AI Security & Governance: Advanced in AI Risk (AAIR), Advanced in AI Security Management (AAISM), CompTIA Security AI+, or equivalent specialized training.
- Cloud & AI Fundamentals: AWS Certified AI Practitioner, AWS Certified Cloud Practitioner.
Work Style & 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, VPs, and engineering peers.
- Collaborative and adaptable in fast-moving Agile environments with shifting enterprise priorities.
