ETSI EN 304 223 Securing AI 5.2.2-3: Dedicated, segregated environments for development and model tuning

What does 5.2.2-3 require?

Developers set up dedicated environments for development work and for model tuning, backed by technical controls that enforce separation and least privilege. Training data that is not drawn from public sources is kept only within the training and development environments.

Maintained by Gerard Blokdyk

Other controls in Clause 5.2: Secure development (Principles 5 to 9) – ETSI EN 304 223 Securing AI

5.2.1-1 Comprehensive asset inventory including interdependencies
5.2.1-2 Processes and tools to track, authenticate, version and secure AI assets
5.2.1-3 Disaster recovery plans tailored to attacks on AI systems
5.2.1-4 Protect sensitive data and sanitise inputs, with proportionate protection of weights
5.2.2-1 Evaluate access control frameworks for APIs, models, data and pipelines
5.2.2-2 Controls on externally offered APIs against attacks on the AI system
5.2.2-4 Publish a clear vulnerability disclosure policy
5.2.2-5 AI system incident management plan and recovery plan

Query this from an agent

The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.

MCP endpoint All ETSI EN 304 223 Securing AI controls