CE Marking (AI) — EU Market Access Conformity Mark
CE marking (Conformité Européenne) is the regulatory conformity mark that indicates a product or system has been assessed and meets the safety, health, and environmental requirements…
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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Model Drift: where documented performance stops matching operational reality
Model drift refers to the gradual degradation of an AI model's performance over time as the statistical properties of the real-world data it encounters in production…
Agent Orchestration — Governance of AI Delegation and Coordination
Agent orchestration is the coordination mechanism through which an orchestrator agent — typically a large language model with planning capability — decomposes a high-level goal into…
Agent Scope Limitation — Containment Controls for Agentic AI Systems
Agent scope limitation is the governance and engineering practice of explicitly constraining the permissions, tools, data access, and action capabilities available to an AI agent to…
Agentic AI Trust Gap — Governance Maturity vs. Deployment Speed
The agentic AI trust gap refers to the systematic disparity between the speed at which organizations are deploying agentic AI systems and the maturity of the…
AI Governance Profession — Emerging Field, Roles, and Competencies
AI governance as a profession refers to the emerging organizational function and career field dedicated to designing, implementing, operating, and auditing the systems, processes, and structures…
Multi-Agent System (MAS) — Governance of Collaborative AI Architectures
A multi-agent system (MAS) is an AI architecture in which multiple autonomous AI agents operate collaboratively to accomplish tasks that exceed the capability, scope, or efficiency…
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…
Data Poisoning — Adversarial Attack on AI Training Data
Data poisoning is an adversarial attack on AI systems in which an attacker manipulates the training data to cause the model to learn incorrect patterns, produce…
Large Language Model (LLM) — Governance and Regulatory Obligations
A Large Language Model (LLM) is a type of deep learning AI model trained on large-scale text corpora to understand, generate, and transform natural language. LLMs…
Privacy by Design — Integrating Data Protection into AI Systems
Privacy by Design (PbD) is the principle and practice of integrating data protection and privacy considerations into the design of systems, processes, and products from the…
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