AI Security Architect - Erlanger, KY , Decatur, IL OR Chicago IL

Job Description AI Security Architect Position SummaryADM’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.

Key Responsibilities 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 life cycles, 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…