AI safety is closely related to the broader field of responsible AI, which aims to ensure that AI systems AI assurance and AI safety are both important concepts in the field of artificial intelligence (AI), Impact on Compliance AI governance, AI assurance, and AI safety are critical components to support current AI Assurance : AI assurance refers to the process of testing and validating AI systems to ensure that AI Safety: AI safety refers specifically to ensuring that AI systems are safe and do not cause harm

Imagine your organization deploys an AI agent to process vendor invoices. This is the governance question that agentic AI is forcing organizations to confront. Much of the current AI governance discourse has inverted this. Agentic AI changes that calculation. Governing AI agents isn't something separate from what compliance professionals already do.

AI is pushing humans out of the loop. Train AI agents to participate in the governance loops that already exist. Most organizations are responding by drafting AI-specific policies, standing up AI ethics committees, This will fail — because it treats AI governance as separate from organizational governance. The governance architectures needed to govern AI agents are not new. They already exist.

However, AI lacks the knowledge (and most likely always will) that comes from experience along with the

Here are some of the things he said: Three facts about AI: AI has happened ( the genie is out of the What is AI (I have paraphrased this)? Before AI we told the computer how to do what we want - we trained the dog With generative AI we tell on the open internet Don’t teach AI to write code Don’t let AI prompt another AI What is the problem Sure, AI can dumb it done or AI-splain it to us so we feel better.

AI Systems: Learning Machines with Unpredictable Behaviour AI systems—especially those based on machine Why Engineering Matters for AI Because of these differences, AI systems need a new layer of discipline—AI Here are some key concepts behind engineering AI systems: 1.  Life-cycle Management AI development doesn’t end at deployment. It’s not enough to build AI systems that work. We need to build AI systems we can trust.

As artificial intelligence continues to advance, AI engineers face a practical challenge – how to build Current State of AI Uncertainty Current AI technologies, particularly advanced systems that use large What aspects of AI should receive attention: the technology itself, the models, the companies developing Rather than relying solely on static evidence, successful AI engineering requires ongoing observation How does your organization approach uncertainty in AI systems?

That is the part the current AI conversation keeps skipping. There are two ways to bring AI into a business, and they are not the same thing. The economics of AI only get worse from there. Any resistance is read as an obstacle, a sign of not being committed to AI. Most of the AI conversation is about adoption. Very little of it is about adaptation.

They called it AI. They said it would help save lives. This was not responsible AI. It was irresponsible adoption. This is what makes AI adoption an ethical choice. Choose Wisely. He is a long-standing advocate for professional digital engineering and the accountable adoption of AI

There are those who think that AI is only software. AI will save us, perhaps, even from ourselves. AI Middle Earth What character are we playing in the AI story and where are we on the map of AI Middle The real world is not in our AI models either. Engineers who are willing to fight the AI Dragon of Uncertainty.

AI adoption is delivering efficiency, not innovation — and we have begun to mistake the one for the other Almost everything the AI economy sells today is the first kind — summarize the document, draft the email I have come to believe AI is having its ERP moment, now, at civilizational scale and three orders of What AI actually diminishes is not the human. It is the necessity of that fusion. And for most enterprises, what AI adoption actually delivers is narrower than the promise — the same

AI Risk Containment Architecture Industrial leaders in safety-critical, highly regulated sectors like Direct integration of AI into operational or enterprise systems introduces unacceptable risks, as even This paper proposes a similar architecture for AI: one that separates Artificial Intelligence Technology (AIT) into bounded domains with controlled interfaces to Operational Technology (OT) and Information