The Director of Machine Learning Security will play a pivotal role in securing our machine learning (ML) and artificial intelligence (AI) systems and extracting usable models from our cyber data sinks. Reporting directly to the Chief Security Architect, and being part of the Security Architecture pillar, will be responsible for developing, and implementing security strategies and frameworks that protect the integrity, confidentiality, and availability of our ML/AI assets.

Master’s Degree or Ph.D

CISSP, CISM, or CCSP or Certifications specific to ML/AI (e.g., Microsoft Certified: Azure AI Engineer Associate) are a plus

Demonstrates thought leader-level abilities with, and/or a proven record of success directing efforts:

• Evaluating opportunities for leveraging technology and innovation that are surfaced broadly throughout the NIS workforce to determine which have an ROI and support the development of such initiatives;
• Developing, and executing a security strategy for ML/AI systems in alignment with the PwC Tech & NIS overall security objectives;
• Researching, and contributing to opportunities for addressing emerging threats and security trends related to ML/AI;
• Working with PwC Tech & NIS AI teams, data scientists, ML engineers, and software developers to integrate security practices into the ML lifecycle, from data collection and preprocessing to model deployment and monitoring;
• Leveraging AI for advanced threat modeling, utilizing machine learning to analyze amounts of threat intelligence data, developing AI-driven models to predict potential attack vectors based on system architecture, and enhancing identification of emerging threats and attack patterns specific to the PwC technology architectures and digital assets;
• Implementing AI-powered risk assessment, employing AI algorithms to quantify risks more accurately by processing complex, interrelated factors, utilizing predictive analytics to forecast potential future risks based on current and proposed architectural decisions, and developing dynamic risk scoring models that adapt to changing threat landscapes;
• Enhancing architecture design and review processes with AI, using AI to assist in generating secure architecture patterns based on Industry leading practices, and current threat intelligence, implementing AI-driven analysis to identify potential weaknesses or inconsistencies in proposed architectures, and developing automated tools for continuous architecture assessment, and improvement recommendations;
• Mentoring, and coaching security architects, providing training to embed AI into security architecture practices;
• Fostering a culture of security awareness and continuous improvement;
• Implementing a strategic vision, working across various functions within PwC Network;
• Keeping up with the latest advancements in ML/AI security, participating in industry forums, conferences, and research initiatives; and,
• Driving innovation by exploring new technologies, and methodologies to enhance the security of ML/AI systems.