Risk assessment walks from hazard to failure mode to control, and with these systems that walk breaks enough to notice. 🔸 𝗗𝗮𝘁𝗮 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘃𝗲𝗿𝘀𝘂𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗱 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 In the old model The model, the platform, and the tooling can be bought.

ISO 5338 defines the lifecycle processes for building an AI system: the engineering work of data and model If you fine-tune a model, add guardrails, chain components, or integrate AI into a larger system, you

An agent's range of behavior — the model, the tools it can call, the situations it can wander into, the There are filters that screen what goes into the model and what comes out. The model, the filter checking the model, and the second model watching the first are mostly the same

This is a very useful model not only to understand how companies can best prioritize their efforts but with compliance added in RED : Performance Zone – The focus of this zone is to execute the business model Transformation Zone – this is where a disruptive business model scales and is introduced to the performance

discussions I have had over the last year, IT product companies appear to be moving towards an AI first model The traditional model—where product managers coordinate with software engineers, UI designers, data analysts Companies like Vercel, Replit, and dozens of Y Combinator startups demonstrate this model in action daily AI reliability and maintenance  - AI systems need monitoring, debugging, and optimization Develop AI model customization expertise  - Fine-tuning, prompt optimization, and model selection Master AI-human collaboration

The old model of risk assessment, primarily focused on mitigating the consequences, no longer serves Model Uncertainty : Often, risks are assessed using models that may not accurately reflect reality. Model uncertainty recognizes the limitations of these models and their potential deviations from actual

Known-Unknowns (Complicated Domain) Here we have moderate visibility. Traditional models struggle to provide explanations for what occurs in this domain. State of AI Uncertainty Current AI technologies, particularly advanced systems that use large language models What aspects of AI should receive attention: the technology itself, the models, the companies developing we can't predict specific outputs with perfect accuracy We understand enough about potential failure modes

However, this defines “Procedural Compliance” which is based on a traditional and reactive model for Instead, “Operational Compliance”, which is based on a holistic and proactive model, defines a state

By extending VCA to encompass these dimensions, we create a more comprehensive model that affords management foundational principles enable practical applications including compliance streams, operational compliance models

This approach is based on Lean Startup model by Eric Ries which we have adapted to the compliance domain

For AI systems, monitoring extends to model performance metrics, prediction latency, and data drift detection , it struggles with novel or complex failure modes. In AI contexts, observability encompasses the full model lifecycle—from data ingestion through training In AI systems—where complex models often operate as black boxes—explainability techniques like SHAP,

That's the line being sold to executives right now — wrapped in maturity models, readiness assessments