Agentic AI Governance Framework — AIGN Framework 1.0 and EU AI Act
An Agentic AI Governance Framework is a structured set of principles, requirements, and controls specifically designed for the governance of autonomous and multi-agent AI systems —…
Resources
Insights on AI Governance & Compliance
Explore our latest resources and practical guides to navigate the evolving landscape of AI compliance and regulation.
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AI Monitoring Challenges — NIST AI 800-4 Six-Category Framework
AI monitoring challenges refers to the structured set of difficulties that organizations encounter when attempting to maintain effective oversight of AI systems after deployment in production…
Data Lineage — Provenance and Traceability for AI Training Data
Data lineage in AI systems refers to the documented record of where data originates, how it has been transformed, what processes have been applied to it,…
Data Minimization — Necessary Data Principle for AI Systems
Data minimization is the data protection principle, enshrined in GDPR Article 5(1)(c), that personal data must be adequate, relevant, and limited to what is necessary in…
Technical Documentation — EU AI Act Annex IV Requirements
Technical documentation, in the context of the EU AI Act, refers to the comprehensive set of documents that providers of high-risk AI systems must prepare and…
Agentic AI — Autonomous AI Systems and Governance Implications
What is Agentic AI? Agentic AI refers to AI systems that can plan, make decisions, and execute sequences of actions autonomously to achieve specified goals, without…
Data Governance — Foundation of Trustworthy AI Systems
Data governance is the organizational framework of policies, processes, standards, roles, and controls that determines how data is collected, stored, managed, used, shared, and retired across…
Model Card — Standardized AI Model Documentation for Transparency
A model card is a structured documentation artifact that provides concise, standardized information about a machine learning model: its intended use, performance characteristics, evaluation results across…
Model Interpretability — Understanding AI Decision Mechanisms
Model interpretability refers to the degree to which the internal mechanisms of an AI model can be understood by humans — specifically, the extent to which…
Model Lifecycle Management — Governance Across the AI Model Pipeline
AI model lifecycle management is the structured governance framework that oversees an AI model from its initial conception through development, validation, deployment, operation, monitoring, retraining, and…
Model Testing — Comprehensive AI System Evaluation Requirements
Model testing in AI systems is the systematic process of evaluating AI model performance, behavior, robustness, fairness, and safety against defined criteria before deployment and throughout…
Model Validation — Pre-Deployment AI System Evaluation
Model validation is the structured process of evaluating whether an AI model meets its intended purpose, performs within acceptable parameters, and is suitable for deployment in…
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