Insights
Why Context Graphs Will Define AI Success in Regulated Industries
Highly-regulated industries require data relationships, provenance and context, provided through context graphs that supplement RAG, vector databases and MCP.
How to build AI governance that enables delivery instead of blocking it
Build collaborative and flexible AI governance frameworks that enable rapid innovation, production and delivery, and ensure your AI plans are on-time, on-budget and compliant in 2026.
Practical Security Guardrails for Large Language Models
Actionable techniques to ensure secure LLM deployments that balance innovation with function, from using prompt injection protection to ethical use and access controls.
The Critical Role of Data Governance in Responsible AI Implementation
Strong data governance is foundational for trustworthy AI, ensuring data quality, privacy and compliance within AI systems. Read on to learn more.
The Dimensions of Enterprise AI Governance: A Focus on Model Lifecycle Management
Explore how structured model lifecycle management turns governance principles into an operational reality, helping to guide AI development from design through retirement with control, transparency and trust.
RAG, Agents and Graph: Your AI Compliance Dream Team
The dream team of AI compliance - read on to discover how Retrieval Augmented Generation (RAG), AI agent frameworks and knowledge graph techniques combine to support regulatory compliant AI systems.
The Paris AI Action Summit Day 2: When Politics Met Technology
Our day 2 of the Paris AI Summit tackled the intersection of policy, ethics, and innovation and highlighted the collaboration between leaders and tech.
The Paris AI Action Summit: Day 1 Summary
Our day 1 recap of the Paris AI Action Summit shares global insights on responsible AI, innovation policy and enterprise transformation.
The Human Element in AI Governance
Successful AI depends not just on tech, but on humans - particularly responsible development, deployment and use.
Unlocking AI's Potential: The C-Suite Blueprint for Responsible Innovation
A C‑level framework to adopting AI responsibly, balancing innovation with risk, oversight and scalability to achieve fast and ethically scale solutions.
Setting an Acceptable Use Policy for Generative AI in Your Business
Why and how enterprises need to build and maintain an Acceptable Use policy, which should create guardrails, rules and oversight for how generative models are used internally.
AI Agents and the Three Lines of Defence: A Banking Inspired Approach
This blog provides an AI governance framework for highly-regulated industries, using the banking industry as inspiration.
5 Essential Best Practices for LLM Governance: A Framework for Success
Key practices for organising, monitoring and securing large language model systems in enterprise settings.
Building a Pragmatic AI Governance Framework: Lessons from the Trenches
Discover practical steps to building effective AI governance, including balancing innovation with risk, compliance and accountability.
AWS DataZone: Empowering Data Monetisation with AI-driven Governance
Discover how AWS DataZone is transforming data monetisation with AI-driven governance. This blog by Ben Saunders explores how AWS DataZone's innovative features—like the Business Data Catalogue, collaborative projects, and governed data sharing—empower organisations to unlock new revenue streams. Learn how AI and machine learning capabilities streamline data management, enhance data quality, and accelerate the journey from data to dollars.
