About This Role
This leadership role focuses on establishing and leading the security capabilities that enable responsible AI adoption across the enterprise. The Director will define the strategy, architecture, governance, and operational controls to secure Machine Learning, Generative AI, LLMs, AI agents, copilots, and autonomous systems throughout their lifecycle, allowing the company to innovate quickly while protecting sensitive data, customers, patients, and the business. This is not solely an AI governance or compliance role; it owns the security architecture and control environment governing AI systems as enterprise platforms and AI agents as autonomous identities that access data, invoke tools, and act on the organization’s behalf.
Responsibilities
- Define and lead the enterprise AI security strategy, operating model, governance, and roadmap while overseeing the function's performance, resources, and capabilities
- Extend and align enterprise security architecture and standards to AI technologies, ensuring appropriate controls, risk management, and integration with broader security frameworks
- Establish and manage AI security controls, architectures, and operational practices across AI platforms, applications, agents, and supporting technologies, ensuring secure development, deployment, and monitoring
- Partner with product and technology teams to secure AI-enabled and connected systems, applying appropriate safeguards, risk management practices, and regulatory considerations
- Lead AI security governance, risk oversight, and compliance activities, ensuring alignment with applicable security, privacy, regulatory, and industry frameworks
Requirements
- 15+ years of progressive experience in cybersecurity, security architecture, engineering, or technology risk
- 5+ years leading enterprise security architecture, engineering, product, cloud, application, or AI security capabilities
- Experience operating within a formal enterprise security architecture practice (ARB participation, reference architecture ownership; SABSA/TOGAF or equivalent), designing controls that span identity, network, data, and application layers across hybrid and multi-cloud estates
- Strong knowledge of GenAI, LLMs, agentic AI, RAG, and vector databases, with the ability to translate AI risk into practical architecture and technical controls
- Deep understanding of IAM, privileged access, Zero Trust, cloud, application/API security, threat modeling, and security operations
- Experience in healthcare, medical device, or another highly regulated environment
- Strong executive communication and cross-functional influence
Important Details
- First 12 months: Enterprise AI security strategy, governance, and operating model established; AI inventory, reference architecture, and detection capabilities established (AI and AI-agent inventory with an agent identity and authorization standard; secure GenAI/RAG and MCP/tool reference architecture integrated with the ARB; AI threat-modeling methodology, red-team program, and vendor/model assessment process; and AI Detection & Response capability with incident-response playbooks); joint operating model with Product Security for AI touching connected devices; executive metrics and risk reporting in place; enterprise security reference architecture extended to AI platforms, agents, and model services (identity fabric, Zero Trust segmentation, dataflow and trust-boundary standards), integrated with the ARB and enterprise risk register; Cyber-Physical Systems (CPS) and connected-device security controls established for AI embedded in or interfacing with Omnicell pharmacy automation, including IEC 62443/Purdue-aligned segmentation and fail-safe/human-approval requirements for device- and medication-affecting decisions
