Ambrish Rawat

Ambrish Rawat is a senior research professional, specialising in Securing Generative AI and ensuring its responsible and trustworthy deployment. He leads efforts in red-teaming, security safeguards, and risk assesment, developing enterprise-grade solutions that help organisations confidently adopt AI by improving trust and reliability in deployed systems.

Ambrish has played a key role in enhancing the security of IBM’s flagship open-source AI models, Granite, and is a core contributor to the Trustworthy AI toolkit, Granite Guardian. His expertise spans Adversarial AI, AI Security, and AI Governance, with impactful contributions to open-source AI security, including Linux Foundation’s Adversarial Robustness Toolbox and IBM Federated Learning.

Since joining IBM in 2016, Ambrish has successfully scaled AI security research from an academic pursuit to real-world business impact, integrating advanced security capabilities into products that enhance customer confidence in AI-driven solutions. He has led cross-functional initiatives, driving execution at scale across IBM Research and product teams, balancing deep technical innovation with practical business adoption. Beyond open-source and product security, Ambrish has contributed to AI risk management frameworks, including work in EU-funded projects, helping enterprises deploy AI with greater assurance. His work has been showcased at top AI and security venues such as NeurIPS, AISTATS, ESORICS, and BlackHat USA.

Recognized as an IBM Master Inventor, Ambrish has made significant contributions to AI security patents. He holds an MPhil in Machine Learning and Machine Intelligence from the University of Cambridge and an MTech in Mathematics and Computing from IIT Delhi.

At the intersection of AI security, responsible AI, and business impact, Ambrish continues to drive trustworthy AI innovation, ensuring that enterprises can deploy AI solutions with confidence and reliability.