Insights
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.
Automating Data Classification with AI Agents
How to use AI agents to automate your data classification tasks (metadata, labeling, schema inference) and significantly reduce manual effort.
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 DNA of an AI Agent
A detailed look at AI agents and how they work, including components, reasoning, autonomy and how AI agents are shifting the paradigm for software design.
The Human Element in AI Governance
Successful AI depends not just on tech, but on humans - particularly responsible development, deployment and use.
Red Teaming Large Language Models: A Critical Security Imperative
“Red teaming”, a military approach to providing structured challenges to plans, policies and assumptions, has some key uses in technology: from exposing vulnerabilities in LLMs to ensuring safe, secure, and ethical deployment at scale. Learn how we use “red teaming” here at WeBuild-AI.
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.
Transforming Financial Remediation: Building Technology Capabilities for the Age of AI
How financial institutions can use AI agents, automation and strong data pipelines to modernise financial remediation programmes.
Industry Spotlight: Motor Finance & Discretionary Commission Agreements - Leveraging Data & AI for a Bold Remediation Response
Learn from industry experts on how AI and data can power an efficient, fair and transparent remediation strategy in the motor finance sector through regulatory shifts, such as the recent Discretionary Commission Arrangements (DCA) from the Financial Conduct Authority (FCA).
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.