AIDRIN (AI Data Readiness Inspector) is a framework and set of tools designed to systematically assess whether datasets are ready for AI and machine learning workflows before model training begins. Within PALISADE-X, AIDRIN provides a critical trust and quality layer that ensures AI systems are built on data that is fit-for-purpose, reproducible, and governance-aware.
Many AI failures are driven by data issues rather than model design, including incomplete or inconsistent data, hidden bias or imbalance, missing metadata or provenance, data leakage across training and evaluation, and misalignment with intended use or policy constraints. AIDRIN addresses these challenges by providing automated, transparent, and repeatable inspections of datasets prior to AI execution.
Evaluates datasets across readiness dimensions such as completeness, consistency, distributional characteristics, and schema integrity.
Produces machine-readable readiness reports suitable for automation.
Enables enforcement of readiness checks before model training, federated learning rounds, or deployment.
Reduces downstream failures and wasted compute.
Inspects dataset metadata, lineage, and versioning.
Supports reproducibility and auditability of AI workflows.
AIDRIN is designed to operate in federated, cross-institution, and distributed environments where data cannot be centralized. Readiness inspections can be performed locally at each site, allowing institutions to share readiness signals without exposing raw data.
Within PALISADE-X, AIDRIN acts as the front door to AI and federated learning workflows. It ensures data quality before APPFL or APPFLx execution and supports trustworthy AI aligned with governance requirements.
CADRE (Customizable Assurance of Data Readiness) is a policy-aware framework that enables configurable, domain-specific data readiness standards for AI systems. While AIDRIN performs inspection and diagnostics, CADRE defines what “ready” means for a given domain, use case, or institution.
Data readiness requirements vary across domains such as biomedical research, clinical AI, infrastructure systems, and national-scale scientific collaborations. CADRE allows stakeholders to formalize readiness expectations and apply them consistently across datasets and AI workflows.
Defines readiness criteria tailored to domain standards, regulatory constraints, and institutional requirements.
Policies can evolve as standards and use cases change.
Applies different readiness thresholds based on AI task, risk profile, and deployment context.
Supports differentiated assurance for exploratory versus high-stakes AI.
Designed to work alongside AIDRIN.
Translates inspection outputs into enforceable readiness outcomes.
Enables automated decision-making in AI pipelines.
CADRE enables consistent enforcement of readiness standards, transparent documentation of assumptions, and auditable AI pipelines suitable for sensitive and regulated environments. In federated settings, CADRE supports site-specific readiness policies while maintaining federation-level consistency.
AIDRIN inspects data and produces diagnostics and metrics.
CADRE defines acceptable readiness standards and applies policy.
AIDRIN focuses on observed data characteristics, while CADRE enforces contextual acceptability.
Together they form a data trust layer for scalable and federated AI.
Within PALISADE-X, AIDRIN and CADRE reduce risk in collaborative AI workflows, enable reproducibility, and ensure that AI readiness begins with data rather than compute or models.