【Key Responsibilities】
AI Security Governance Risk Management
Evaluate security and privacy risks associated with AI applications, including large language models, generative AI services, and machine learning systems.
Conduct threat modeling and risk assessments across the AI lifecycle, covering areas such as model misuse, data exposure, model integrity, and inference security.
Partner with legal, compliance, and governance teams to ensure AI initiatives align with internal security standards and emerging regulatory requirements.
Define and recommend security controls, mitigation strategies, and implementation plans to address identified risks and improve overall AI security posture.
AI Security Engineering
Design and implement security mechanisms for AI platforms and services, including model protection, content safety controls, and secure deployment architectures.
Establish guardrails and security practices for AI-assisted development tools to reduce risks related to insecure code generation, data leakage, and intellectual property exposure.
Collaborate with AI/ML engineering teams to integrate security practices into model development and deployment workflows, promoting a security-by-design approach.
Develop standards and best practices for secure AI adoption across engineering teams.
Research Technical Advisory
Continuously track emerging threats, attack techniques, and industry trends in AI security and MLSecOps.
Provide technical guidance and consultation to product and engineering teams on secure AI architecture, risk mitigation, and responsible AI implementation.
Drive internal awareness and knowledge sharing related to AI security, governance, and operational best practices.