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- Regulating AI with Institutional Knowledge
The answer is not a smarter model. The answer is to govern the model with your knowledge. The grounded model: answers drawn from the reference The model supplies the reasoning, but it must reason Ground answers in your knowledge, not in the model. Adapt the model for communication, not for rules. Closing that gap does not require a better model.
- You're Not Using AI. AI Is Using You.
We can provide a model with our documents to process, but this does not change the model. It lives wherever and whenever the model is applied. doing so it adds its intelligence to the model's. As an organization leans on the general model, it begins to adapt itself to the model rather than the A model that processes information about you is not a model that knows you.
- Understanding Operational Compliance: Key Questions Answered
Organizations investing in compliance often have legitimate questions about how the Operational Compliance Model compliance approaches—ISO management standards, GRC platforms, COSO frameworks, Three Lines of Defence models Rather than viewing these as competing approaches, the Operational Compliance Model serves as an integrative GRC (Governance, Risk, and Compliance) platforms are tools, not operational models. Traditional "Procedural Compliance" is based on a reactive model for compliance that sits apart and is
- Operational Compliance - Update
The following diagram is a vertical orientation of our Operational Compliance Model updated to better Operational Compliance Model (Updated) We use the Operational Compliance Model to ensure policy-driven The Operational Compliance Model includes built-in risk management, compliance, and governance right from the start in one integrative model. This model is best implemented using the Lean Startup approach to achieve Minimal Viable Compliance
- The Regulatory Tsunami
Regulatory models What might it mean to be a risk-based regulator? for regulators is "Model 1." This model produces prescriptive regulation typified by inspectors showing up with a tape measure to Finally, Model 4, is a variant of the previous model where the companies under regulation are too small Sparrow suggests that risk-based regulators will: Focus on the "Expert" rather than the "Legal" model
- Developing an Environmental Golden Thread - Part 1 (Using a DSM)
A DSM models system elements and their corresponding information exchange, interactions, and relationship Dependancy Structure Matrix An environmental program will include many aspects which can be modelled For example, the following pillars are sequential relationship in the Environmental Golden Thread model As such, the coupled relationships in the model show the critical areas of focus in the system. since it connects the main upper loop to the lower loop of the model (see dependency map below).
- Compliance: Beyond the Fish Tank
Our fish tank was a model of the real world, but not the world itself. As the British statistician George Box wrote, “All models are wrong, some are useful.” We need to use models to help us navigate the real world, not replace the real world by our models. Another way of saying this is that: we don’t live in our models, and neither do our businesses. Maintain Perspective- Use models as tools for understanding, not as blueprints for reality.
- Compliance Operability Assessment Using Total Value Chain and Compliance Criticality Analysis
It provides context for how compliance fits into the overall business model. 2. into the operational business model. Evaluate Compliance Operability The final step is to assess how well the integrated compliance model One area where capability maturity models have been successfully employed is in software development, by Carnegie Mellon University, continues to develop and publish maturity models.
- Why AI is Used to Govern AI
First, the regulator must model the system it regulates. This is the Conant-Ashby theorem. Every good regulator of a system must be a model of that system.
- Why GRC Should be GRE
While this is necessary, it is based on an old model where meeting obligations (the act of compliance you start with an integrative, holistic, and proactive approach to meeting obligations, a different model
- Why Engineering Matters to AI
Continuous Change : Models may be retrained over time, either manually or automatically, as new data Engineering for robustness means testing models under various scenarios, stress conditions, and edge Explainability tools and techniques help uncover what’s driving the model’s decisions, which is essential Engineering includes versioning models, tracking data changes, managing retraining pipelines, and ensuring models continue to meet performance and compliance requirements over time.
- Promise Agents: Autonomous Policy Fulfillment in Security Architecture
Promise Theory, the framework Mark Burgess developed to model autonomous commitment, sits at the heart His observation was that the command-and-control model of managing devices was itself producing vulnerabilities His response was to model a different design principle: devices that govern themselves from within by develops this as a formal proposal: **Promise Agents** — security equipment with embedded, fine-tuned AI models












