Standard Technology expanded its responsible‑AI guardrails across model development and serving. The release includes policy enforcement points for input/output controls, restricted tool access, and human‑in‑the‑loop approvals where outcomes affect safety or compliance. Evaluation harnesses now run pre‑deployment and continuously in production‑like mirrors, checking factuality, robustness, bias, and security edge cases using task‑appropriate metrics. Results feed dashboards and alerts so teams can triage regressions quickly, and model lineage is captured to correlate behaviors with training data, prompts, and configuration changes. Privacy‑preserving patterns—federated learning, synthetic data augmentation, secure enclaves—are supported where sensitive data or jurisdictions require them. Documentation templates and decision logs help make design choices explicit and auditable, while de‑identification guidance reduces inadvertent exposure of personal or proprietary information. Inference gateways implement throttling, anomaly detection, and secrets management, and can disable high‑risk tools if policies are not met. The program’s goal is not to slow innovation but to make it observable, predictable, and correctable. Over the coming months, the team will publish additional test suites and examples so customers can replicate and adapt these guardrails to their own risk profiles and regulatory contexts.