Data readiness, privacy, security, cloud, compute and model evaluation.
A data readiness framework covering quality, access, lawful basis, stewardship and security for Government AI deployment.
Read GuidanceKey personal data protection considerations for Indian Government AI deployments under the DPDP framework, translated into departmental practice.
Read GuidanceThe principal AI security risks facing Government departments and the controls that reduce exposure across tools, integrations and agents.
Read GuidanceThe data governance foundations government AI programmes require: lawful basis, classification, quality, stewardship, lineage and retention.
Read GuidanceThe questions government buyers should ask about cloud and compute for AI: data location, empanelment, security, portability, cost and scaling.
Read GuidanceWhat sovereign AI means in practice for Indian government, which workloads justify it, and what capability it demands from the buyer.
Read GuidanceHow to evaluate Indian-language and indigenous AI models for government workloads, including testing, coverage and deployment trade-offs.
Read GuidanceAn explanation of AIKosha, India's AI datasets and resources platform, and how government agencies can use and contribute to it.
Read GuidanceA practical way for government agencies to size AI compute needs based on workload type, volume, latency and sensitivity rather than vendor guidance.
Read GuidanceA practical evaluation checklist for government teams assessing AI models for accuracy, robustness, fairness and fit.
Read GuidanceSpecific evaluation considerations for large language models in government: factuality, safety, language coverage, cost and control.
Read Guidance