Pipeline Security: Designing and implementing protocols to secure model training and deployment pipelines against unauthorized access or tampering.
Adversarial Defense: Proactively identifying and preventing adversarial attacks, including input manipulation, model inversion, and data poisoning.
Intellectual Property & Privacy: Implementing advanced measures to protect sensitive dataset privacy and safeguard our proprietary model intellectual property.
Threat Modeling & Red Teaming: Conducting AI-specific threat modeling and internal "red team" exercises to discover vulnerabilities before they can be exploited.
Governance & Compliance: Ensuring all AI initiatives align with global security standards (e.g., ISO/IEC 42001, NIST AI RMF), ethical guidelines, and emerging AI governance frameworks.

AI/ML Proficiency: Strong understanding of machine learning frameworks (e.g., PyTorch, TensorFlow) and the underlying mathematics of model architectures.
Adversarial AI Knowledge: Proven experience with adversarial machine learning techniques, such as Gradient-based attacks, Evasion attacks, and Model Extraction.
Secure Software Development: Expertise in securing CI/CD pipelines and containerized environments (Docker, Kubernetes) specifically for ML workloads.
Data Protection: Proficiency in privacy-preserving technologies such as Differential Privacy, Homomorphic Encryption, or Federated Learning.
Cloud Security: Deep experience with security configurations in AWS, Azure, or GCP, specifically regarding managed AI services (e.g., SageMaker, Vertex AI).
Education: A Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Data Science, or a related field.
Professional Background: 5+ years of experience in Cybersecurity, with at least 2 years specifically focused on AI/ML security or research.
Certifications: Relevant certifications such as CISSP, CISM, or specialized AI certifications (e.g., Certified AI Security Professional) are highly regarded.

AI Security Expert

Key skills