Senior Principal Architect, Cyber Security – AI

Senior Principal Architect, Cyber Security – AI

Reporting to the Director of Cyber Security Architecture, the Senior Principal Cyber Security Architect – AI will act as the lead security architecture advisor for AI‑related initiatives across the organisation.

• Provide senior security architecture leadership for AI initiatives, ensuring AI systems and services are secure by design and aligned with enterprise cyber security strategy, risk appetite, and regulatory expectations.
• Define and maintain AI security principles, standards, guardrails, reference architectures, design patterns, and production readiness criteria across the AI lifecycle.
• Advise on secure architecture for AI platforms, MLOps pipelines, data pipelines, model registries, vector databases, RAG solutions, prompt orchestration layers, APIs, AI agents, and cloud‑based AI services.
• Design and support implementation of security controls across identity and access management, privileged access, secrets management, encryption, key management, network security, secure APIs, logging, monitoring, resilience, and incident response.
• Conduct AI‑focused risk assessments, threat modelling, architecture reviews, and control gap assessments for new and existing AI use cases.
• Identify and support mitigation of AI‑specific risks, including data leakage, prompt injection, model extraction, model inversion, training data poisoning, insecure output handling, excessive agency, insecure RAG implementations, supply chain compromise, and unauthorised access.
• Partner with engineering, data science, platform, and security teams to embed security into AI development, MLOps, CI/CD pipelines, deployment workflows, and operational monitoring.
• Advise on secure handling of training, validation, test, and production data, including data minimisation, anonymisation, access control, retention, lineage, provenance, and protection of confidential or regulated information.
• Assess security risks associated with third‑party AI platforms, foundation models, SaaS AI tools, APIs, open‑source models, datasets, plugins, extensions, and model marketplaces.
• Define security requirements for AI vendor due diligence, procurement, onboarding, contractual review, and ongoing supplier assurance.
• Work with security operations and incident response teams to define AI‑specific logging, monitoring, detection, investigation, and response requirements.
• Monitor AI security developments, emerging threats, attack techniques, industry standards, and regulatory expectations, translating them into practical internal controls and guidance.
• Promote AI security awareness across engineering, production, data science, business, and technology teams through guidance, workshops, reusable patterns, and stakeholder engagement.
• Support the Director of Cyber Security Architecture with AI security strategy, governance, reporting, and other duties as required.

• Significant experience in cyber security architecture, application security, cloud security, platform security, data security, or technology risk.
• Practical understanding of AI, machine learning, generative AI, large language models, MLOps, cloud AI services, and AI‑enabled application architectures.
• Experience designing, reviewing, or implementing security controls for complex technology environments.
• Experience conducting risk assessments, threat modelling, security architecture reviews, and control gap assessments.
• Understanding of AI‑specific threats, including prompt injection, data leakage, data poisoning, model extraction, model inversion, insecure output handling, supply chain compromise, and misuse of AI agents.
• Knowledge of secure software development, Dev Sec Ops , CI/CD pipelines, APIs, identity and access management, encryption, logging, monitoring, and vulnerability management.
• Experience working with cloud platforms and modern data architectures.
• Ability to translate complex technical risks into clear business language for senior stakeholders.
• Strong communication, documentation, stakeholder management, and influencing skills.
• Preferred experience with AI/ML engineering teams, data science teams, AI platform teams, or product…