Dernière mise à jour :
Integrating security controls and best practices into AI systems from the earliest design phases to prevent vulnerabilities and data breaches.
A proactive approach that embeds security considerations into every layer - data ingestion, model training, API design, and deployment. It includes threat modeling during requirements, secure coding standards, encryption at rest and in transit, rigorous access controls, and continuous security testing (static analysis, penetration testing). Governance ensures security requirements are mandatory project milestones, with sign-offs before each phase transition.
Exemple concret :
A healthcare AI team built “security by design” into their patient-diagnosis system by encrypting all data inputs, running static-security scans on model code before training, and conducting monthly pen tests on the inference API - catching and patching vulnerabilities long before any live data was exposed.
