Junior AI Security Analyst

Role Overview

• Own the daily evidence collection and control monitoring for our ISO 27001, 27701, 27018 and SOC 2 frameworks to ensure we remain audit-ready at all times.
• Assist in the rollout and maintenance of the NIST AI Risk Management Framework (RMF) and ISO 42001, helping to ensure our AI initiatives are safe, ethical, and compliant.
• Act as key contributor during external audits, providing technical documentation and demonstrating control effectiveness to auditors.
• Collaborate with the engineering team to design, code and deploy AI agents that automate repetitive security tasks, such as risk assessments and log monitoring.
• Use Python and API integrations to build “security-as-code” pipelines, reducing manual effort for broader security and engineering teams.
• Partner with the engineering team during design sessions and code reviews to ensure that security controls and automation are integrated into the foundation of our internal tools, rather than added as an afterthought.
• Design and maintain AI-driven workflows to handle routine helpdesk queries, software provisioning, and common troubleshooting so you can “automate yourself out” of manual tasks.
• Apply your understanding of Large Language Models (LLMs) to identify and fix AI-specific security vulnerabilities like prompt injection, data leakage, and model bias.
• Regularly review our internal AI tools and third-party integrations to ensure they meet our data privacy and security standards.
• Stay current with the rapidly evolving AI and security landscape, sharing research and best practices with the team to keep our defenses ahead of the curve.

Requirements

• 1-3 years of experience in security, IT, or engineering role, ideally with a focus on automation or process improvement.
• Bachelor’s degree in Computer Science, Cybersecurity, or a related technical field (or equivalent practical experience with a strong portfolio of GitHub projects).
• Proficiency in Python for scripting and building automations; you should be comfortable working with APIs to connect different software systems and automate data flows.
• Understanding of AI and LLMs, specifically how they process data and the unique security risks they introduce, such as prompt injection or data leakage.
• Working knowledge of modern CI/CD pipelines (e.g. GitHub Actions, Gitlab CI/CD) and an understanding of how to integrate automated security checks into the build process.
• Exposure to cloud platforms (AWS, Azure or GCP) and working knowledge of Infrastructure as a Code (IaC) concepts like Terraform or CloudFormation.
• Experience with containerization (docker) and how containers are used to deploy and scale applications securely.
• Eager to learn the “how and why” behind compliance evidence of frameworks such as ISO 27001, SOC2 or the NIST RMF.
• A thinker who is equally comfortable writing documentation for an audit as they are building an AI agent to automate that same audit risk.
• Strong communicator – you can help an employee with a tech issue one moment and discuss security control automation with an engineer the next.

Tech Stack

• AWS
• Azure
• Cloud
• Cyber Security
• Docker
• Google Cloud Platform
• Python
• Terraform

Benefits

• Competitive compensation
• Flexible hybrid working model