Algorithmic Registry

A unified, searchable database cataloging every automated system in the enterprise. It eliminates shadow AI by providing a single pane of glass for all models, from prototype to production. Key capabilities include:

• Metadata indexing: Stores model owner, version, training data provenance, and deployment location.
• Federated discovery: Automatically scans cloud environments (AWS, Azure, GCP) and MLOps platforms to detect unregistered models.
• API-first architecture: Integrates with existing CI/CD pipelines and governance, risk, and compliance (GRC) tools via REST endpoints.

Automates the categorization of AI systems according to regulatory frameworks like the EU AI Act's risk tiers (Unacceptable, High, Limited, Minimal). The engine evaluates:

• Use-case analysis: Maps the system's intended purpose against regulatory risk categories.
• Conformity assessment tracking: Logs the status of required third-party audits and internal checks.
• Dynamic re-classification: Automatically flags a system for review if its inputs, outputs, or user base change, potentially altering its risk profile.

Directly associates each registered system with its mandatory documentation, creating an auditable chain of evidence. This includes:

• Model Cards: Structured reports on performance, limitations, and evaluation data.
• System Cards: Holistic safety evaluations covering the model, UI, and downstream effects.
• Datasheets for Datasets: Documents detailing training data motivation, composition, and collection processes.
• AI Bill of Materials (AI BOM): A formal inventory of the model's entire supply chain, including software dependencies and data provenance.

Enforces strict stage-gate controls as models move from development to decommissioning. The registry acts as the policy enforcement point:

• Immutable version history: Tracks every model iteration, including its training data hash, hyperparameters, and code commit.
• Deployment gating: Prevents a model from being promoted to production unless it has passed bias, robustness, and explainability checks.
• Sunsetting protocols: Manages the archival and decommissioning of obsolete models, ensuring compliance with data retention policies and preventing unauthorized use.

Continuously validates registered systems against evolving internal policies and external regulations through policy-as-code. This feature provides:

• Real-time drift detection: Monitors for unregistered model deployments or changes in data distributions that violate compliance rules.
• Regulatory mapping: Automatically links internal controls to specific articles in the EU AI Act, GDPR, or NIST AI RMF.
• Remediation workflows: Triggers automated tickets in Jira or ServiceNow when a system falls out of compliance, assigning tasks to the model owner.

Generates on-demand reports for diverse audiences, from technical auditors to executive leadership and regulators. The registry provides:

• Auditor dashboards: Pre-built views showing the complete evidence package for any high-risk system, including its risk classification, linked model card, and signed-off impact assessment.
• Executive summaries: Visualizations of the organization's aggregate risk posture, showing the distribution of AI systems by risk tier and business unit.
• Regulatory filings: Exports data in formats required for mandatory EU database registration of high-risk AI systems, ensuring a frictionless submission process.