AI Safety — Technical and Governance Requirements for Safe AI Systems
AI safety refers to the set of technical and governance measures ensuring that AI systems behave as intended, do not cause unintended harm, remain within operational…
Resources
Insights on AI Governance & Compliance
Explore our latest resources and practical guides to navigate the evolving landscape of AI compliance and regulation.
LATEST RESOURCES
Audit Trail — Traceability and Accountability in AI Systems
An audit trail in AI systems is a chronologically ordered, tamper-evident record of events, decisions, and actions taken by or involving an AI system — sufficient…
Continuous Assurance — Ongoing AI Governance Monitoring
Continuous assurance in AI governance refers to the ongoing, systematic process of monitoring and evaluating AI systems and governance controls in real time or near-real time,…
AI Transparency — Disclosure and Documentation Requirements
AI transparency is the obligation and practice of providing clear, accurate, and accessible information about AI systems — their purpose, capabilities, limitations, decision logic, data sources,…
Deepfake — Synthetic Media and EU AI Act Disclosure Obligations
What is a deepfake under the EU AI Act? A deepfake is synthetic media in which a person's likeness, voice or behaviour is generated or manipulated…
Disparate Impact — Algorithmic Discrimination in AI Decision Systems
What is disparate impact in AI decision systems? Disparate impact is a legal and analytical concept describing the situation in which an apparently neutral decision system…
Explainable AI (XAI) — AI Decision Transparency and Interpretability
Explainable AI (XAI) refers to methods, techniques, and design principles that make the decisions, predictions, and behavior of AI systems understandable to human stakeholders — including…
Responsible AI for Investors — ESG and Due Diligence Criteria
Responsible AI for investors refers to the set of criteria, frameworks, and assessment methodologies through which institutional investors, venture capitalists, private equity firms, and asset managers…
Shadow AI — Unmanaged AI Use and Governance Gaps
Shadow AI refers to the use of AI tools, systems, and services within an organization without the knowledge, approval, or oversight of IT, legal, compliance, or…
AI Alignment — Ensuring AI Behaviour Matches Intended Objectives
What is AI alignment in production AI systems? AI alignment is the discipline of ensuring that an AI system's behaviour matches the goals and values its…
AI Impact Assessment — Evaluating Consequences Before Deployment
An AI Impact Assessment (AIA) is a structured evaluation of the actual and potential impacts of an AI system on individuals, groups, organizations, and society —…
AI Incident Response — Governance When AI Systems Fail
AI incident response is the structured organizational process for detecting, classifying, containing, investigating, remediating, and reporting incidents involving AI systems — including system failures, unexpected behaviors,…
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