AI-Native Security Engineer - Senior

Location: Anywhere in Country At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. The opportunity AI systems are moving from generating content to taking action across data, tools, and business processes. You will make that agency defensible by embedding security throughout the lifecycle, turning emerging threats into practical controls that help teams pursue ambitious AI use cases safely. This is a builder role for someone who enjoys both finding flaws and creating the safer path. You will work alongside product and engineering teams, prove controls under adversarial conditions, and replace one-off reviews with reusable patterns, measurable coverage, clear evidence, and rehearsed ways to respond when systems behave unexpectedly. About Challenger Challenger is a new EY program focused on building the firm’s next generation of services. It pursues opportunities where AI enables a fundamentally different service and way of delivering it, including rethinking existing offerings and developing new ones. Small, dedicated teams build and test ideas in real conditions. Solutions that demonstrate clear business value and operational readiness transition to the business for adoption and scale. Challenger Core develops and maintains the shared technology, methods, and risk and quality guardrails that support this work. Each team contributes reusable tools and practices, helping make the next service faster and less costly to build. Your Key Responsibilities Set the security direction. Own reusable AI security patterns, control objectives, reference architectures, and threat models aligned to the risk and autonomy of each use case. Join builders early. Work with teams before key design choices are fixed, identifying credible attack paths and shaping controls that fit the product and delivery model. Design scoped authority. Create practical patterns for identity, access, isolation, approvals, and recovery so tool-using AI acts within clear and enforceable boundaries. Protect sensitive information. Embed security across the data lifecycle, preserving confidentiality, integrity, provenance, permitted use, and monitored access. Secure the delivery path. Integrate automated security tests, policy gates, trusted artifacts, traceable changes, and safe rollback into engineering workflows. Test adversarially. Run focused red-team exercises against AI-specific abuse cases and convert findings into repeatable tests, stronger platform patterns, and actionable guidance. Build runtime protection. Use end-to-end evidence and behavioral signals to detect abnormal actions, privilege misuse, attempted exfiltration, and degrading controls. Strengthen response. Support vulnerability triage and incidents with clear evidence, containment options, recovery actions, and lessons that improve future designs. Advise on residual risk. Give product, security, evaluation, and risk leaders concise technical recommendations while keeping approval and risk-acceptance decisions with accountable owners. Build shared capability. Create reference implementations, coach engineers, and turn production lessons into reusable controls that make the safer path easier to adopt. Skills And Attributes For Success Security engineering depth: Strong foundations in application, cloud, platform, identity, data, and software delivery security. AI threat judgment: Ability to model how prompts, retrieval, agents, tools, users, and external services create new attack paths and failure modes. Control engineering: Experience translating threats into preventive, detective, and responsive measures that engineers can implement and operate. Adversarial mindset: Practical skill in red teaming, reproducible testing, vulnerability analysis, detection, and incident learning. Automation capability: Ability to build security tests, controls, telemetry, and reference components and integrate them into delivery pipelines. Pragmatic influence: Clear communication and product judgment, with the ability to explain risk without drama, vagueness, or implied approval authority. To qualify you must have 5+ years of relevant experience. Bachelor’s Degree in a relevant field. Relevant experience: Substantial experience in product, application, cloud, platform, or detection security, including hands-on ownership of production controls. Threat modeling: Demonstrated ability to assess complex systems and turn attack paths into practical engineering measures. Production security: Experience securing modern applications, cloud environments, identities, data, networks, and software delivery pipelines. AI systems knowledge: Working understanding of generative and agentic AI, including retrieval, model access, tool use, evaluation, and runtime behavior. Hands-on automation: Experience building or extending security tests, controls, telemetry, reference implementations, or adversarial assessments. Response experience: Evidence of supporting vulnerability or incident response and translating lessons into stronger technical controls. Cross-functional delivery: A record of partnering with engineers and risk functions to improve security while respecting user value and delivery constraints. Travel and On-site requirements: This role requires up to 50% travel and flexibility to go into the office in a hybrid capacity. Ideally, you’ll also have Agentic security: Experience securing tool-using agents, AI gateways, model-serving platforms, retrieval systems, or other high-autonomy applications. High-assurance delivery: Experience in regulated, safety-relevant, or multi-region environments with formal evidence and response requirements. Detection engineering: Experience designing behavioral analytics and investigation evidence for distributed or AI-enabled systems. Supplier and supply-chain risk: