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.
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 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.
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.
The Future of Data Ownership and Consent Management in the AI Age
Explore how the intersection of AI, personal data, and blockchain is shaping the future of data ownership and consent management in this insightful blog. Discover the rising value of personal data, the risks of unrestricted AI use, and the potential of blockchain to empower individuals to control and monetise their data. Learn about the urgent need for legislative frameworks to protect data rights, and how AI-generated content, like deep fakes, underscores the importance of transparency, consent, and ethical use. Dive into the steps necessary to build a fair and secure data economy that benefits individuals and respects privacy.
AI Ethics & MLOps - Go Fast, Without Breaking Transparency
Explore how MLOps can ensure AI ethics and transparency in your organisation in "AI Ethics & MLOps - Go Fast, Without Breaking Transparency." This blog by Ben Saunders delves into the importance of integrating ethical considerations and governance into the machine learning lifecycle. Learn how MLOps frameworks can help build, deploy, and manage AI models that are reliable, transparent, and compliant with legal standards, fostering trust among customers and regulators while accelerating AI adoption.
Navigating the Risk Landscape of AI Systems: A Short Guide
Navigating the Risk Landscape of AI Systems: A Short Guide provides crucial insights into the complexities of managing AI-related risks. As AI technologies become increasingly integrated into various sectors, understanding potential threats such as data privacy concerns, algorithmic biases, and security vulnerabilities is essential. This guide offers practical strategies for identifying, assessing, and mitigating these risks to ensure safe and ethical AI implementation. Whether you're a business leader, IT professional, or AI enthusiast, this short guide equips you with the knowledge to navigate the evolving AI risk landscape effectively.