Security in AI and MCP: Protecting Models and Data

Security in AI requires protecting both model assets and the data they process. Start with strict access control, least privilege for database and compute credentials, and rotating keys. Apply data encryption at-rest and in-transit and anonymize or pseudonymize sensitive records. For model security, use authentication and rate limiting for APIs, monitor for unusual inference patterns (model extraction), and use token scopes for model endpoints. When using Managed Compute Platforms (MCPs), isolate workloads, apply network policies and VPC controls, and keep runtime images minimal and patched. Set up logging and alerting for suspicious activities, and make your CI/CD pipeline verify secrets and enforce scans before deployments. Finally, follow compliance guidelines (e.g., GDPR) and run regular penetration tests to validate your controls.