accepting that audit findings represent their management failures, not their auditors' discoveries What AI Cannot Do And if you thought AI can help you with this, you will be left wanting. Here's the thing: AI cannot take ownership of your obligations. AI might be able to analyze some of your compliance gaps, generate your procedures, monitor your controls They'll add AI tools to the stack, automate the busywork, and still fail to keep their promises because

Yet AI operates further up the DIKIW model, where knowledge and intelligence live, and our architecture Much of the risk in AI adoption comes from that gap.

AI Safety Labels There is an idea floating around the internet and within some regulatory bodies that we should apply safety labels to AI systems, akin to pharmaceutical prescriptions. While well intended this is misguided for a variety of reasons, namely AI’s adaptive nature.   In contrast, AI systems represent a new paradigm of adaptive intelligence. An AI Regulatory Framework: Intelligent Compliance Laws of AI Regulation for Compliance Cybernetics pioneer

Most current thinking places AI at the monitoring or response layer: detecting anomalies, flagging incidents develops this as a formal proposal: **Promise Agents** — security equipment with embedded, fine-tuned AI finished design, but a direction worth examining for security architects, compliance practitioners, AI He serves on ISO's ESG working group and OSPE's AI in Engineering committee, and chairs the AI Committee

When it comes to AI, many don’t want to hear about the risk. Some will go as far as claiming that AI creates no threat to humans or the world we live in. They say, it’s only people that use AI, who create the risk. AI technologies and its interactions with systems. AI can provide great benefits, however, it also brings with it significant risk.

. ❤️ C1 — AI safety and Responsible AI in practice. It creates what's not there: safety, security, sustainability, quality, responsible AI, legal adherence

Unlike the commoditized world of cloud computing and agile development, AI systems need real engineering engineering as the discipline that adapts engineering rigour to whatever digital paradigm emerges: AI We stand at the threshold of the AI era. As a Professional Digital/AI Engineering Advocate, Raimund champions proper licensure across the entire He actively contributes to the profession through his leadership roles, serving as AI Committee Chair

continual mechanization of human work, now accelerated by the integration of Artificial Intelligence (AI ) and Agentic AI. AI systems are being deployed to handle everything from customer service inquiries to complex data analysis We now have AI systems to make decisions and perform work with far-reaching consequences without the Approach AI and digital agents as tools to augment human wisdom, not replace it.

How should the use of AI be governed when used in safety devices or as part of a safety component? Here are some of the ways AI have improved DSS: Automated Data Analysis : AI algorithms can automatically Machine Learning : AI-enabled DSS can use machine learning algorithms to improve the accuracy of its Advantages of Autonomous Decision-Making using AI: Speed and efficiency: AI can analyze data at a much Disadvantages of Autonomous Decision Making using AI: Lack of human oversight: AI systems can make decisions

AI Exposes the Latency For many compliance programs, this misplaced ownership is a latent risk. In a growing number of domains, AI among them, an unowned obligation no longer waits to become a problem obligations — privacy, safety, quality, financial integrity, legal and ethical commitments — through AI An AI cannot own any of them. It cannot accept responsibility when an obligation is unmet. Deploying AI into an organization that already put ownership in the wrong place does not close the gap

When it comes to AI we must: Keep Humans In The Loop When there is a chance of harm, the decision to AI should not make ethical decisions for you. What steps can you take starting today to ensure your organization is responsible with its use of AI?

Today's AI is inference over data, used as a proxy for both knowledge and intelligence, with the layers An ontology of AI comes first, built from what these systems are and do. He writes about compliance, engineering, and AI at leancompliance.ca.