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AI Security Architect
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AI Security Architect
Location: United Kingdom
Eligibility: UK citizens only
Role Overview
We are seeking an experienced AI Security Architect to design and own the end-to-end security architecture for Claude Code and future enterprise AI deployments. The successful candidate will act as a technical authority on AI security, leading threat modelling and architecture design for agentic AI workloads. The role will define enterprise-wide security standards, patterns, and guardrails for AI usage while supporting AI governance and the evolution of the organisation's AI security operating model.
Key Responsibilities
- Design and own end-to-end security architecture for Claude Code and future enterprise AI deployments.
- Lead threat modelling and architecture design for Claude Code and agentic AI workloads.
- Define enterprise-wide security standards, patterns, and guardrails for AI tool usage.
- Evaluate and select security tooling for AI use cases, including Data Loss Prevention (DLP), API gateways, and audit logging.
- Engage with AI vendors on security controls and contractual requirements.
- Provide expert security input to the AI Governance Committee.
- Define and evolve the security target operating model for AI.
- Apply security architecture frameworks and threat-modelling methodologies to AI deployments.
- Address AI-specific risks, including prompt injection, model inversion, and data poisoning.
- Work with stakeholders to align AI security controls with enterprise architecture, regulatory requirements, and secure development practices.
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Essential Skills and Experience
- 8+ years of experience in cybersecurity.
- At least 2 years of experience in AI/ML security or LLM deployment.
- Deep knowledge of Zero Trust, SASE, and API security architectures.
- Experience with STRIDE threat modelling and security architecture frameworks such as SABSA or TOGAF.
- Understanding of AI-specific security risks, including prompt injection, model inversion, and data poisoning.
- Knowledge of GDPR, the EU AI Act, and the NIST AI Risk Management Framework (AI RMF).
- Strong stakeholder communication skills, including the ability to present to C-suite audiences.
- Experience with Anthropic Claude API or similar LLM platforms.
- Background in DevSecOps or secure Software Development Life Cycle (SDLC).
- CISSP, CCSP, or SABSA certification.
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Additional Focus Areas
- Enterprise AI security architecture and governance.
- AI security controls, guardrails, and auditability.
- Secure integration of LLM platforms and enterprise systems.
- Security tooling evaluation and AI risk management.
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