Enterprise Security Architect

Enterprise Security AI Architect

Enterprise Security AI Architect is responsible for designing, governing, and enabling secure adoption of AI across the enterprise. This role defines and implements the security architecture for AI solutions. This includes GenAI assistants and AI-enabled business applications. It also ensures compliance with regulatory, privacy, and risk requirements.

You will work with many teams. These teams include Enterprise Architecture, Platform, Data, GRC, Product, and various security functions. Some of these security functions are AI Engineering, AppSec, Identity, Cloud Security, Threat & Vulnerability Management, and Security Operations. Your goal is to make sure that AI solutions are secure by design. They should also be scalable and meet enterprise standards. You will promote the secure use of AI for building applications and agents.

You will also help integrate AI security into architecture review boards, the software development life cycle (SDLC), and DevSecOps. This integration helps teams work quickly while keeping risks low. Your work will include enabling AI, which means safely adopting AI tools in the enterprise. It will also involve securing enterprise AI by protecting AI-enabled products, applications, and agents that we build.

This position operates in a hybrid work model based in Deerfield Beach, FL (3 days per week onsite).

Responsibilities:

• Define and lead the enterprise AI security strategy and roadmap, ensuring secure and scalable adoption of AI capabilities across the organization.
• Design and maintain enterprise reference architectures for AI solutions, covering SaaS AI, hosted models, GenAI, and AI-enabled applications.
• Establish and enforce AI security standards, policies, and guardrails, ensuring consistent implementation across all AI initiatives.
• Develop and operationalize secure AI design patterns (e.g., identity, data protection, model access, prompt security, output controls) that teams can reuse to accelerate delivery.
• Drive AI Enablement and Securing Enterprise AI: define the secure, sanctioned ways teams use AI to build applications and agents—covering developer tooling (AI coding assistants, agentic IDEs), agent frameworks, MCP/tool integrations, and runtime guardrails—so engineering can adopt AI productively without bypassing security.
• Lead and facilitate AI-focused threat modeling to identify risks such as data leakage, prompt injection, model abuse, and expanded blast radius, and define required mitigations.
• Build and maintain Threat Radars and AI risk intelligence, providing visibility into emerging threats, vulnerabilities, and industry trends impacting AI technologies.
• Partner across enterprise architecture, engineering, data, product, GRC, and security functions including AI Enablement, AppSec, Identity, Cloud Security, Threat & Vulnerability Management, and Security Operations to embed AI security into governance processes (e.g., intake, design reviews, approval boards).
• Provide architecture guidance and security oversight for AI use cases, ensuring solutions are secure-by-design and aligned with enterprise standards.
• Evaluate and assess third-party AI vendors and platforms, providing clear risk-based recommendations to leadership.
• Enable teams through documentation, patterns, and coaching, ensuring AI solutions can be delivered efficiently without increasing risk.
• Translate complex AI security risks into clear, executive-level insights and recommendations to support informed decision-making.

Qualifications:

• 15+ years of experience in cybersecurity, with a strong focus on enterprise security architecture within large, complex environments.
• 5+ years of experience specifically in Enterprise AI Security Architecture, including securing AI/ML, GenAI, or data-driven platforms and services.
• Proven expertise in designing and implementing enterprise-scale security architectures, including development of reference architectures, security standards, and reusable security patterns.
• Deep understanding of AI/LLM security risks and controls, including areas such as data protection, model access, prompt security, output handling, and emerging threat vectors.
• Experience securing AI-assisted software development and agentic systems, including AI coding assistants, agent frameworks, tool/MCP integrations, and the controls needed for safe enterprise use of AI in building applications and agents.
• Demonstrated experience leading threat modeling initiatives and developing threat intelligence or threat radar capabilities specific to evolving technology domains.
• Proficient experience with industry security frameworks and standards (e.g., NIST, ISO 27001, CIS, MITRE ATT&CK), and ability to apply them in modern AI and cloud environments.
• Expertise in cloud security and modern identity architectures (e.g., Zero Trust, SSO, OAuth/OIDC, RBAC/ABAC) across platforms such as Azure.
• Solid knowledge of data security and privacy principles, including encryption, data classification, DLP, and regulatory considerations.
• Experience influencing and defining enterprise security roadmaps and strategies, with the ability to align security initiatives to business priorities.
• Solid track record of partnering across Enterprise Architecture, Engineering, GRC, Data, Product, and other security functions (e.g., AI Enablement, AppSec, Identity, Cloud Security, Threat & Vulnerability Management, Security Operations) to embed security into design and delivery processes.
• Experience evaluating and securing third-party vendors and platforms, including AI/SaaS solutions, with a risk-based approach.
• Exemplary communication skills with the ability to translate complex technical risks into clear, executive-level insights and recommendations.
• Bachelor's degree in computer science, Cybersecurity, Engineering, or equivalent experience; relevant certifications (CISSP, CCSP, SABSA, cloud security certifications) are a plus.