Systems thinker focused on constraints, control, and automation.

regulation → system → constraint → control → automation

https://www.linkedin.com/in/david-cockson

For the last 8 years I’ve worked inside complex regulatory systems, primarily in the gambling industry.

The work was rarely just policy or compliance.

It involved:

• identifying structural weaknesses in regulatory frameworks
• investigating systemic failures across organisations and suppliers
• designing operational controls to stabilise complex systems

In practice this meant constantly asking:

Where is the constraint?
What breaks first?
What control stabilises the system?

That mindset translates naturally into software systems, infrastructure, and automation.

Exploring structured governance models for AI systems.

Sable AI Governance Framework
https://davidcockson-compliance.github.io/sable-ai-governance-framework/

Pickles GmbH AI Governance Framework
https://davidcockson-compliance.github.io/pickles-gmbh-ai-governance-framework/

These projects explore how governance frameworks can be structured, generated and maintained using AI-assisted workflows.

Monitoring and analysis tool for the UK Gambling Commission licence register.

Key capabilities:

• licence register search and analysis
• domain and infrastructure discovery
• corporate group identification
• compliance monitoring views

Repository
https://github.com/davidcockson-compliance/scarlet-helix

Live instance
https://froghunter.dpdns.org/

Structured dataset and filtering tool for the UK Gambling Commission regulatory framework.

Purpose:

• convert regulatory text into structured datasets
• enable filtering and analysis
• support compliance gap analysis workflows

Current experimentation environment used to practise:

• containerisation (Docker)
• cloud infrastructure
• deployment workflows
• monitoring and observability

Projects are built with the goal of creating repeatable operational systems rather than one-off builds.

My background is systems analysis inside regulatory environments.

I now apply the same mindset to software infrastructure and operational systems.

Progression of projects:

Regulatory systems analysis
        ↓
Structured regulation datasets
        ↓
Compliance tooling (React + data models)
        ↓
Automation and deployment (Docker / Cloudflare)
        ↓
Cloud infrastructure experimentation
        ↓
Monitoring and observability systems

Each step builds on the previous one.

The direction is moving from:

analysis → tooling → infrastructure → automation

flowchart LR
A[Observe system] --> B[Find constraint]
B --> C[Map the gap]
C --> D[Design control]
D --> E[Automate solution]
E --> F[Monitor outcome]
F --> A

This loop applies whether the system is:

• regulatory frameworks
• operational processes
• infrastructure platforms
• AI workflows

Understand the system.
Remove the constraint.
Let automation keep it stable.

I’m currently building practical experience in:

• Linux systems and tooling
• Docker and container infrastructure
• cloud deployment patterns
• monitoring and observability
• AI governance and multi-agent workflows

Most learning happens through hands-on builds and homelab experimentation.

Systems usually fail at the constraint.

When approaching a new system I typically start with:

• What is the real constraint?
• What fails first under pressure?
• What control stabilises the system?
• What can be automated once the system is stable?

The goal is not complexity.

The goal is stable systems that continue working when nobody is watching.

Tao of Pratchett.

Map the gap. Architect the control.