AI Security Architect
ADM’s Global Information & Cyber Security (GICS), Security Architecture & Engineering team is seeking an AI Security Architect with an Engineering/Analyst background. This role safeguards enterprise AI systems by applying Industry guidance on AI risk standards and principles. The position ensures secure, resilient, and compliant AI adoption across cloud and enterprise environments, focusing on confidentiality, integrity, availability, safety, and ethical use of AI/ML systems. The role proactively identifies and mitigates risks from adversarial machine learning, data poisoning, model leakage, and unauthorized access, while collaborating cross-functionally to build secure and trustworthy AI systems.
This role serves as a trusted advisor for both internally developed AI capabilities and third-party AI platforms, ensuring secure, compliant, and responsible adoption of AI technologies across the enterprise. The architect partners with Data & AI Governance, Legal, Privacy, Procurement, and Enterprise Architecture teams to evaluate AI solutions, mitigate emerging risks, and establish security standards aligned with business objectives and regulatory requirements.
- Consult, Recommend and Implement practices aligned with joint internal and external guidance, including understanding AI risks, securing the AI lifecycle, ensuring resilience, and establishing accountability.
- Contribute to the development and maintenance of ADM's AI security architecture and roadmap, ensuring alignment with business priorities, Data & AI governance requirements, responsible AI principles, and evolving regulatory obligations.
- Partner with Data & AI governance stakeholders to establish and maintain security standards for model integrity, data lineage, access control, auditability, transparency, and AI risk management throughout the AI lifecycle.
- Establish AI security testing requirements across development and deployment lifecycles, including validation of model integrity, access controls, prompt injection resilience, and adversarial attack resistance.
- Executive Communication & Risk Advisory: Translate AI security risks into business impact and provide recommendations to technical, operational, and leadership stakeholders. Develop security assessments, architecture recommendations, and governance reporting to support risk-based decision making.
- Threat Modeling & Adversarial Testing: Conduct threat modeling for AI/ML models and pipelines, lead adversarial testing, red teaming, and stress testing on AI models.
- Participate in the evaluation and security review of AI platforms, models, tools, and services. Assess AI-specific risks including data handling, model provenance, supply chain integrity, API security, third-party model exposure, and regulatory compliance. Collaborate with Legal, Privacy, Procurement, and Vendor Risk Management teams to define security requirements and controls for AI vendors and service providers.
- Review current internal capabilities, process, tooling and provide strategic and tactical recommendations to meet requirements to Secure, Defend, Thwart for Ai capabilities.
- Develop secure AI design patterns, reference architectures, and implementation guidance. Partner with AI engineering, MLOps, and cloud platform teams to integrate security controls into model development, deployment pipelines, model registries, data platforms, and operational workflows.
- Threat Detection & Response: Monitor AI systems for adversarial ML attacks, prompt injection, model misuse, unauthorized access, and emerging AI threats. Partner with Security Operations and Incident Response teams to develop and maintain response procedures and playbooks for AI-related security events.
- Provide security architecture guidance for both internally developed AI solutions and externally acquired AI products and services, ensuring consistent security controls, governance, and risk management practices across the AI technology ecosystem.
- Documentation & Best Practices: Develop and maintain documentation for AI security best practices.
- Cross-team Collaboration: Partner with AI engineers, architects, compliance officers, Technologists, and stakeholders to embed controls and guide secure AI development.
- Continuous Improvement: Stay up to date with advancements in AI and automation technologies to continuously improve security engineering.
- AI Risk Management: Apply AI risk management standards to assess and mitigate risks in AI pipelines.
- Mentor architects, engineers, and analysts in AI security best practices and contribute to the advancement of AI security capabilities, governance practices, and responsible AI initiatives across the organization
- Support Enterprise Architecture governance process administration.
- Support EA Technical Design Services.
- Support EA Technical Design Review Services, include reporting on reviews.
- Maintain knowledge of industry trends and utilize this knowledge to educate both IT and the business on opportunities to build better target architectures that support and drive business decisions.
- Strong knowledge of CISA Secure AI principles and ISO/IEC 23894.
- Hands-on experience with Microsoft Purview, Defender for Cloud, Entra ID, and Sentinel.
- Understanding of AI/ML fundamentals (model training, inference, adversarial ML, secure data pipelines).
- Understanding of Generative AI and AI-agent security risks, including prompt injection, model inversion, model extraction, retrieval augmented generation (RAG) poisoning, model supply chain risks, agentic AI risks, and AI misuse scenario
- Experience with ML platforms (TensorFlow, PyTorch, Scikit-learn) and MLOps tools (MLflow, Kubeflow, Azure Machine Learning, Databricks, or equivalent enterprise AI platforms).
- Familiarity with adversarial ML concepts and tools (Pyrit, IBM Adversarial Robustness Toolbox, CleverHans).
- Proficiency in scripting or programming languages (Python, Bash, .Net).
- Knowledge of security methodologies and frameworks including STRIDE, MITRE ATLAS, MITRE ATT&CK, OWASP Machine Learning Top 10, vulnerability management platforms, threat modeling methodologies, and SIEM technologies.
- Expertise in securing workloads in Azure; AWS/GCP experience is a plus.
- Ability to assess and mitigate AI-specific risks (bias, poisoning, data leakage).
- Familiarity with regulatory frameworks (GDPR, HIPAA, FedRAMP, CCPA).
- Strong analytical, communication, and documentation skills.
- Ability to explain complex AI security concepts to technical and non-technical audiences.
- Collaborative mindset with experience working across multidisciplinary teams.
- 7+ years’ experience in IT
- 5+ years in cybersecurity, with at least 2 years focused on AI/ML or cloud security and 2 within the role of an Architect.
- Certifications: Azure Solutions Architect, GIAC Machine Learning Security Essentials (GMLE), CISSP, CCSP, or equivalent AI/ML security credentials, CAISP.
- Project management experience.
- Experience with lifecycle and licensing within cloud environments.
- Current holder of security certifications.
- ISO/IEC 23894
- Practical Experience leveraging CISA Secure AI Principles, and/or NIST IR 8596 guidance and Cybersecurity Framework 2.0
- Ownership mindset.
- Commitment to helping others thrive.
- Continuous learning.
- Fostering diversity, equity, and inclusion.
This position offers a complete benefit package, including 401K/ESOP, pension, health, life, and dental insurance. ADM is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, and veteran status.
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