Resources

What the next Model Context Protocol (MCP) release means for enterprise security and risk teams running AI agents in production.

ACE moves enterprise AI governance beyond static prompts to living playbooks. This research note explains the model, why prompts don't scale, and where enterprises can apply it today.

As AI Agents evolve into autonomous actors, securing them requires techniques beyond identity and access controls. This article explores how taint analysis can extend visibility into agent workflows, uncover hidden risks, and strengthen enterprise AI security.

AI adoption is accelerating, but trust and accountability are essential. Learn how explainability, observability, and behavioural oversight help security teams meet regulations, manage risk, and scale AI Agents with confidence.

Building on research from Berkeley, this article outlines five barriers to enterprise AI adoption and the AI governance best practices to overcome them with visibility and accountability.

Enterprises are experimenting with AI agents, but scaling safely requires enterprise AI governance. This guide outlines how to move AI adoption from experimentation to enterprise-wide deployment with clarity, governance, and trust.

We started Geordie to build the governance layer for AI agents. By managing posture, observing behaviour, and providing contextual controls with audit-ready evidence, we make AI agent adoption safe, transparent, and enterprise-ready.

APIs, MCPs, and A2A form the foundation of AI Agent systems, yet their roles are often fragmented. This article explains how they connect, align, and collaborate, giving leaders the clarity to evaluate, govern, and scale AI adoption with confidence.

AI adoption is accelerating, but not every investment is safe. Risk mapping gives security and business leaders visibility to evaluate AI tools, weigh business impact against exposure, and make confident, accountable decisions that balance innovation with resilience.

Endpoint controls like EDR and XDR remain critical to enterprise defense, but they weren’t built for AI Agents. As these systems operate beyond traditional endpoints, leaders need new approaches to gain visibility, reduce risk, and extend security with confidence.

AI Agents can’t be black boxes. Leaders need clear visibility into how they operate to manage risk, meet compliance, and enable safe innovation across the enterprise.

Building AI Agents promises speed and scale, but developers know the pitfalls. Explore their insights on risks, friction points, and the steps leaders can take to enable safe, resilient adoption.

AI Agents aren’t just software. They make decisions that can reshape your business. Left unchecked, that power creates new forms of risk. This guide helps executives cut through the hype and focus on visibility, governance, and accountability.