Written by Thirty posts ago, we started with a question: can a simple lifecycle model, DASUD, really stretch to cover modern AI systems like GenAI, RAG, and agents? You’ve now seen that it can. More than that, you’ve used it to build a coherent, practical approach to advanced AI governance.
Let’s briefly recap what you’ve built and where you can take it from here.
A lifecycle that still works
You’ve seen that:
- Design Can handle everything from ML use‑cases to GenAI assistants, RAG systems, and multi‑agent architectures—if you define purpose, scope, risk, and oversight clearly.
- Acquire Now covers not just training data, but prompts, fine‑tuning corpora, RAG sources, tools, APIs, and feedback.
- Store Includes models, logs, embeddings, vector stores, and agent memories, with segmentation and retention aligned to risk.
- Use Captures how outputs and actions are used in real workflows, with explicit oversight modes and monitoring.
- Delete Isn’t just about dropping tables; it’s about retiring models, updating/removing knowledge, resetting memories, and using kill switches and rollback when needed.
That’s a powerful message: we didn’t throw away governance; we extended it.
A library of applied patterns
Across the series, you’ve learned:
- Design artefacts Use‑case canvases, GenAI design checklists, RAG design sheets, agent charters.
- Input governance patterns Rules for training data, RAG content, prompts, tools, and feedback.
- Storage and memory rules Logging and retention matrices, memory scopes, and segmentation strategies.
- Oversight models Clear definitions of HITL and HOTL, tied to risk tiers and mapped into workflows.
- Incident and lifecycle practices AI‑specific incident types, playbooks, kill‑switch patterns, and decommissioning checklists.
This is more than theory; it’s a toolbox you can reach for when the next AI project shows up.
A playbook and a program
You’ve also:
- Assembled a playbook Turning scattered ideas into a structured, DASUD‑aligned manual with templates and examples.
- Sketched an operating model Showing how to plug advanced AI governance into existing councils, risk processes, and change management.
- Defined metrics and dashboards So you can answer executives when they ask, “Is this working?”
That’s the difference between a learning sprint and a governance capability.
What comes next
No framework stays perfect forever. Advanced AI will keep evolving: new model types, new patterns (like tool‑calling or multi‑step reasoning), new regulations. DASUD’s strength is that it gives you questions to ask at each stage, even as the answers change.
Going forward:
- Treat DASUD as your backbone For any new AI pattern, ask: what’s new at Design, Acquire, Store, Use, and Delete?
- Keep templates and playbooks living Update them as you learn from real projects and incidents.
- Share and refine in community Internally, build a small network of colleagues applying these ideas. Externally, share selected insights so you learn from others too.
Your role in this story
Finally, recognise what you’ve learned:
- You didn’t wait for a “finished” regulatory picture.
- You didn’t abandon your governance foundations.
- You built a bridge from data governance to advanced AI governance, step by step.
That positions you not just as someone who “understands AI risk,” but as someone who can design and run the systems that keep advanced AI aligned with organisational values and obligations.
If you keep iterating, listening, and refining, this 30 post series will be the starting chapter of a much longer story—one where you are a central author of how your organisation does AI, not just a reviewer at the end.
If you’d like assistance or advice with your Data Governance implementation, or any other topic (Privacy, Cybersecurity, Ethics, AI and Product Management) please feel free to drop me an email here and I will endeavour to get back to you as soon as possible. Alternatively, you can reach out to me on LinkedIn and I will get back to you within the same day!
Nigel D'Souza 