Microsoft’s third edition of the Responsible AI Transparency Report emphasizes that responsible AI is an ongoing discipline rather than a final destination. The framework adapts to rapidly changing autonomous capabilities.
Every organization building AI applications eventually faces the same question: how do we create the environment where that building can happen safely and at scale?
A Copilot license can help people work faster, but a wider enterprise AI strategy should also help the organization build, govern, and operate better AI-enabled capabilities.
If AI agents are to work the way you want them to, having guardrails and a proper foundation in place make them more reliable so you can let them act autonomously while you sleep.
Agentic AI systems can be built for many purposes. This article is about how Fortytwo’s COO Remi Vandemir built an agent system that manages all the side-projects that he doesn’t have time to manage himself.
Governance requires the ability to stop, restrict or postpone technology that the organization is not yet ready to manage responsibly. That can be an uncomfortable truth if you want your organization to move quickly with AI.
As Fortytwo expands into Denmark, we have entered a new strategic partnership with Emilie Lundblad, who will lead the initiative. We spoke with Emilie about what she is seeing in the Danish market when it comes to AI adoption, and where companies are getting stuck.
In a world where agentic AI is gaining agency, insider risk is growing at a speed you cannot control.
Fortytwo and Telenor Cyberdefence are joining forces at a time when many organisations are moving from AI experimentation to real operational use.
This article describes how MCP works and what you need to know about it.
This is the fourth article in a series of five, describing what MCP is, why you need it, and how it helps AI agents talk to your system.
This is the third article in a series of five, describing what MCP is, why you need it, and how it helps AI agents talk to your system.
Large language models can understand requests, reason about context, and suggest useful next steps.
But business work does not happen inside a language model.
Why is the model context protocol, MCP, needed? AI can reason about work, but it cannot safely participate in real systems without a controlled interface.
