AI government

Transversal AI layer as an extension of Data Governance, covering the entire lifecycle (data, models, agents, APIs/services) with traceability, transparency, accountability and risk control. Anjana Data Platform operationalises this in a practical and sustainable way, minimising bureaucracy and enabling responsible innovation.

Regulatory framework and reference standards

Independent microservices and decoupled, with standardised communications and specialised storage per service. The design facilitates horizontal scalability, deployments VM o Kubernetes and operation in on-premises, IaaS/PaaS, or SaaS. The platform displays three layers of RESTful APIs (public, administrative, and configuration) documented (Swagger) and a SDK for extensions and proprietary agents.

Key capabilities

Inventory and centralised management of AI artefacts

Unified and governed registration of models, datasets, prompts, agents, APIs, and pipelines, managed as information assets within the Metadata Lake, with associated lifecycle, versioning, taxonomies, and policies.

Policies, approval flows, and notifications

Native BPM for defining policies and procedures (e.g., impact assessment requirements, bias reviews, deployment gating) with role-based workflows, alerts, and automatic evidence.

Traceability and multi-layer lineage

Hybrid lineage (technical–functional) from training data and features to inferences and decisions, with impact analysis prior to change and knowledge of dependencies between components.

Dynamic auditing and continuous evidence

Recording of actions, configuration changes, and device statuses; capture of metrics and control results for internal/external auditing and compliance testing.

Risk management and monitoring (drift & degradation)

Risk modelling, thresholds and response plans; operational monitoring to anticipate data and model drift and activate remediation through automation.

Explainability and accountability

Collection/management of metadata on explainability and justifiability associated with predictions and decision reports; traceability of who uses what, for what purpose, under what conditions.

Data Contracts and Data Sharing Agreements

Definition of data contracts and governed sharing agreements for training, validation, and production datasets, with control of permissions and conditions of use, integrated into the Information Marketplace and Data Portal.

Native integrations and open APIs

Open architecture, no black boxes, with REST APIs and plugins to extract/inject metadata, orchestrate permissions, import quality/observability metrics, and federate IAM/SSO across public clouds and data/ML platforms.

Data lifecycle (E2E) governed

Concept & Design

Registration of use case, objectives, legal basis, risk level, and required controls (AI Act). Initial approval gate.

Data sets & Features

Cataloguing, sensitivity, and licences; data contracts and default privacy policies.

Training & Validation

Artifact versioning, test evidence (bias, robustness, security), compliance checklist.

Approval & Deployment

Ethical/legal/technical review workflow; publication governed as an AI Product in the Marketplace with terms of use.

Operation & Monitoring

Metrics, alerts, drift, and MLOps integrated with the governance framework and response automation.

Incidents & Breaches

Registration, notification, investigation, and remediation with full traceability.

Retraining & Obsolescence

Version management, change impact assessment, and controlled deprecation.

Typical use cases

Compliance with the EU AI Act with living evidence, risk classification, EIM (impact assessments), controls, and integrated audits.

Model Registry governance and MLOps pipelines with compliance gates and end-to-end lineage.

Marketplace for AI models/services with consumption contracts and automated permissions by role/domain.

Mitigation of bias and drift through risk taxonomies, KPIs, and operational alerts.

AI Governance Metrics

Compliance

% of artefacts with approved EIM; % of controls passed by release.

Risk

Exposures open by category (bias, robustness, security); MTTR of incidents.

Operation

Drift rate (data/model), retraining frequency, lineage coverage, and contracts.

Responsible Use

Approved vs. denied access requests by policy; active adherence to DSAs.

Differential benefits

Compliance “by design” and continuous auditable evidence, without friction for business.

Scalability and technology agnosticism in hybrid/multi-cloud ecosystems, without vendor lock-in.

Reduction of operational and regulatory risk thanks to lineage, workflows, and end-to-end automation.

Accelerated time-to-value with an incremental model by use cases and Data&AI Ops support.

How it fits into your architecture

Anjana Data integrates bidirectionally with data/ML platforms and cloud services (IAM, catalogues, quality, observability) to build a common, cross-functional Data & AI Governance layer that complements native technical catalogues with organisational capabilities (domains, roles, policies, contracts, workflows), avoiding dependencies and maximising ROI.

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