AI Research, Regulation, Knowledge & Practical Resources

Explore INTAI research and analysis examining the strategic, governance, regulatory, operational and societal implications of artificial intelligence.

Read focused INTAI whitepapers exploring important developments in AI governance, risk, assurance, regulation, leadership and organisational adoption.

Explore practical examples of how organisations can approach AI governance, implementation, risk management, assurance and responsible adoption.

Access annual INTAI publications examining major developments, emerging risks and strategic priorities shaping enterprise AI.

Understand the EU AI Act, its risk-based framework, organisational responsibilities and the practical implications for providers and deployers of AI.

Explore the international AI management system standard and what organisations should understand when building structured AI governance arrangements.

Understand the NIST AI Risk Management Framework and how its Govern, Map, Measure and Manage functions can support practical AI risk management.

Follow the evolving UK approach to AI regulation and understand the implications for organisational governance, accountability, risk and regulatory readiness.

Explore academic research and analysis examining artificial intelligence, machine learning, generative AI, responsible AI and emerging governance challenges.

Build practical understanding of AI governance, risk, regulation, assurance and responsible AI through accessible learning and guidance.

Practical guidance for executives and boards navigating AI strategy, investment, organisational adoption, governance, risk and assurance.

Read accessible reviews of academic research and emerging thinking relevant to artificial intelligence, governance, risk and organisational practice.

Read INTAI perspectives on artificial intelligence, governance, emerging risks, organisational adoption and the changing AI landscape.

Explore how AI adoption, regulation, governance and emerging capabilities are changing organisations across different industries.

Follow important developments in AI regulation, standards and regulatory expectations affecting organisations and their use of artificial intelligence.

Follow significant developments across artificial intelligence, technology, regulation, governance and enterprise adoption.

Access practical templates designed to support AI governance, risk assessment, accountability, documentation and implementation.

Use practical checklists to support AI governance reviews, implementation planning, risk management and organisational readiness.

Explore the core policies organisations should consider when establishing clear rules, responsibilities and controls for AI use.

Access practical AI guides, briefing packs, roadmaps and resources covering governance, leadership, risk, assurance and implementation.

Explore clear definitions of important artificial intelligence, governance, risk, regulation, machine learning and generative AI terminology.

A governance-led comparison across nine AI platforms, organised around data sensitivity, existing technology stack and regulatory exposure.

A managed gateway to Claude, Llama and other models inside AWS infrastructure your organisation may already trust.

OpenAI's assistant, often already in informal use across an organisation before any governance exists around it.

Anthropic's assistant, available through Claude.ai, Team and Enterprise accounts, and the Claude API.

Google's AI assistant embedded across Workspace, with governance implications tied to existing Google infrastructure.

An enterprise AI and data platform built with regulated industries in mind, including built-in governance tooling.

Open-weight models an organisation can self-host, trading vendor convenience for control over data and infrastructure.

AI embedded across Microsoft 365, inheriting much of its governance posture from existing tenant configuration.

The infrastructure layer beneath many private and open-weight AI deployments, relevant to organisations building their own stack.

An AI research and answer engine increasingly used for business research, with its own governance considerations.