AI/ML Security Leader

Leverage technology to impact patients and ultimately save lives.

Do you have expertise in, and passion for, cyber security? Would you like to apply your expertise to impact the product security in a company that follows the science and turns ideas into life changing medicines? If so, AstraZeneca might be the one for you!

About AstraZeneca

AstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development, and commercialization of prescription medicines for some of the world’s most serious disease. But we are more than one of the world’s leading pharmaceutical companies. At AstraZeneca, we are dedicated to being a Great Place to Work.

About our team

It is a dynamic and outcome-driven environment to work in – but that’s why we like it. There are countless opportunities to learn and grow, whether that’s exploring new technologies in hackathons, or redefining the roles and work of colleagues, forever. Craft your own path, with support all the way. A diverse set of minds that work cross- functionally and broadly together.

AI/ML Security Leader

We are seeking a visionary and expert AI/ML Security Leader to be responsible for the development of a comprehensive security model for the adoption of AI/ML technologies, models, and risk reduction. In this critical role, you will design, implement, and continually refine the security posture of AI systems, focusing on securing both internally developed and partner-provided SaaS platforms. You will investigate emerging security threats to AI/ML models and data, develop new tools and techniques to address these threats, and ensure the smooth integration of AI capabilities with a focus on Security, Trust, and Privacy. As an AI/ML Security Leader, you will collaborate with multi-functional teams, including product, engineering, and data science, to ensure that AI and machine learning systems are robust, resilient, and secure. This role requires deep technical expertise in AI/ML security, a proactive approach to identifying and mitigating potential threats, and the ability to lead and guide teams in developing security solutions.

Accountabilities:

AI/ML Security Strategy & Leadership:

Develop and lead the execution of a comprehensive AI/ML security strategy across the organization. Create long-term roadmaps and architecture designs to secure both internally developed AI models and partner-provided AI SaaS solutions.

Threat Research & Mitigation:

Investigate and stay ahead of new and emerging threats to AI and ML systems, including adversarial attacks, model poisoning, data manipulation, privacy risks, and intellectual property theft. Evaluate and implement countermeasures to ensure the resilience and security of AI models.

Security Architecture & Best Practices:

Design and implement secure AI system architectures, providing guidelines, frameworks, and tools to integrate security into AI/ML pipelines. Establish security standards, policies, and protocols for the development, deployment, and monitoring of AI solutions.

AI/ML Model Protection & Privacy:

Develop and implement techniques to safeguard AI/ML models and data, such as model verification, robustness testing, data integrity checks, and privacy-preserving methods (e.g., differential privacy, federated learning).

Security Tools and Processes Development:

Lead efforts to create and refine security tools, frameworks, and processes that can effectively secure AI systems at scale. Work to automate security measures across the AI lifecycle, including model training, deployment, and monitoring.

Multi-functional Collaboration:

Work closely with data scientists, AI researchers, product teams, and security experts to ensure seamless integration of AI capabilities while maintaining a strong security posture. Provide guidance and consultation on securing AI solutions from the early stages of design to production.

Security Testing & Validation:

Oversee and lead AI security testing activities such as penetration testing, vulnerability assessments, and adversarial testing to validate the robustness and security of AI systems. Develop testing frameworks specific to AI/ML systems.

Incident Response & Risk Management:

Establish and lead the AI/ML incident response processes, ensuring rapid identification, containment, and resolution of any AI-specific security incidents. Monitor AI systems for signs of emerging threats and continuously improve risk management processes.

AI Governance & Compliance:

Ensure AI/ML systems align with relevant regulations, industry standards, and ethical guidelines, such as GDPR, CCPA, ISO 27001, and others. Ensure clarity, accountability, and fairness in AI model behavior an