AI Security Architect
Listed on 2026-09-08
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IT/Tech
AI Engineer (Applied/Software), Information Security & Data Protection, Cybersecurity, IT Consultant
AI Security Architect
Our client requires a dedicated AI Security Architect to address the unique and rapidly expanding security risks introduced by artificial intelligence technologies. AI introduces distinct risk domains that are not fully covered by traditional security, cloud, data, or product architecture roles. As AI adoption accelerates and becomes a permanent operational capability, the demands on existing teams continue to grow beyond current capacity and skill sets.
This role provides focused ownership and accountability for AI security architecture, governance, and risk mitigation across the enterprise, enabling secure innovation while protecting the organization's data, systems, and reputation.
Position Responsibilities
AI Security Strategy & Enablement
Drive secure adoption of AI technologies and data science best practices (e.g., machine learning models, Claude and other LLMs, model training practices, data quality requirements, data literacy, etc.) within the Security organization.
Partner with AI, innovation, and engineering teams to enable AI usage aligned with security expectations.
Establish guidelines for AI usage within the organization.
Governance, Risk & Compliance
Support and actively participate in the GRC AI Workgroup and AI Taskforce.
Create and maintain governance frameworks, policies, and controls to mitigate AI system risks.
Support demand reviews by assessing AI initiatives for security, privacy, and risk impact.
Provide AI security requirements and guidelines to AI innovation teams.
Architecture & Design
Design and maintain secure reference architectures for AI systems, including models, platforms, and integrations.
Ensure AI systems align with established enterprise security principles across:
- Cloud
- Identity and access management
- Network security
- Dev Sec Ops
- Data security
Data Protection & AI Risk Mitigation
Support ongoing efforts to secure AI model data usage enterprise platforms (i.e.Snowflake and Databricks).
Drive controls to prevent data loss through public and external LLMs.
Partner with monitoring and security tooling teams to enhance visibility into AI usage and risk.
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