🎮 The Next Input — Issue #208

In partnership with

⚡ The Briefing — 60 sec

• South Australia’s AI Royal Commission is already drawing scrutiny One of those exercises where, if done right? Groundbreaking. If done wrong? An absolute farce. There’s a real opportunity here to produce something practical, durable and globally useful. Please, for the love of God, don’t turn it into 900 pages nobody reads.

• OpenAI reportedly completed a $7 billion employee tender offer Biggest takeaway from all of this? I should’ve listened more in school. Somewhere there is an OpenAI engineer looking at their tender paperwork wondering whether the yacht needs its own yacht.

• Zuckerberg makes another huge AI move amid race with OpenAI, Anthropic and China Whenever Zuck starts posting martial arts videos, you know a corporate flex is incoming. New open-source model, billions of users in the distribution funnel and suddenly he’s acting like the next Bruce Lee. Never change, Mark 🤣

🛠️ The Playbook — AI Governance Test Bench

Mission
Turn AI governance from policy theatre into a repeatable system that tests whether real AI workflows are safe, useful and accountable before they scale.

Difficulty
Advanced

Build time
4–6 hours

ROI
Speeds up responsible AI deployment by replacing vague governance debates with evidence, testing and clear go/no-go decisions.

0) Why This Matters

AI governance has a credibility problem.

Too often it looks like:

• committees

• principles

• consultations

• frameworks

• reports

• another framework explaining the previous framework

Meanwhile, somebody in operations has already connected an agent to SharePoint and given it write permissions.

The useful version of governance is operational.

Can this system access the right data?

Can we explain what it did?

Can somebody stop it?

What happens when it is wrong?

Who owns the decision?

A good governance framework should answer those questions in software and process—not merely prose.

1) Architecture

2) Workflow

1. Register every material AI workflow with a named business owner and intended outcome.

2. Classify the workflow according to data sensitivity, autonomy and potential consequence.

3. Generate required tests based on the risk level rather than applying identical controls everywhere.

4. Run functional, adversarial, privacy and failure-mode evaluations before production access.

5. Require accountable human approval for unresolved high-risk findings.

6. Monitor live performance and automatically trigger reassessment after major model, workflow or data changes.

3) Example Prompts

Governance Classification

You are an enterprise AI governance analyst.

Assess the following AI use case:

[DESCRIPTION]

Evaluate:
- data sensitivity
- decision consequence
- level of autonomy
- external-user impact
- regulatory exposure
- reversibility
- human oversight
- security permissions

Classify the use case as:
LOW / MEDIUM / HIGH / CRITICAL

Then return:
1. justification
2. mandatory controls
3. required testing
4. approval owners
5. monitoring requirements

Governance Red Team

You are red-teaming an AI workflow before production deployment.

Workflow:
[DESCRIPTION]

Attempt to identify:
- prompt injection paths
- excessive permissions
- sensitive-data leakage
- hallucination risks
- unsafe autonomous actions
- inaccessible or discriminatory behaviour
- incorrect escalation
- audit-trail gaps
- situations where the system fails silently

Return each scenario with:
1. attack or failure method
2. expected safe behaviour
3. observed risk
4. severity
5. recommended mitigation

Executive Go/No-Go

Prepare a concise AI deployment decision brief.

Inputs:
- intended business outcome
- risk classification
- evaluation results
- unresolved findings
- mitigation controls
- accountable owners

Return:
1. business value
2. major risks
3. controls in place
4. unresolved issues
5. accepted risks
6. monitoring plan
7. GO / CONDITIONAL GO / NO-GO recommendation

Do not hide uncertainty.

4) Guardrails

• Do not govern all AI systems as though they carry identical risk.

• Require named business ownership for every production workflow.

• Test complete workflows, not just model outputs.

• Keep production permissions separate from experimentation permissions.

• Record why risks were accepted, not merely that they were accepted.

• Reassess workflows after major model or data changes.

• Maintain emergency disable and rollback procedures.

• Measure whether governance improves outcomes instead of simply adding approvals.

5) Pilot Rollout — 3 hours

1. Select one live or near-production AI workflow.

2. Register its owner, purpose, models, data sources and permissions.

3. Assign a risk classification using a standard scoring rubric.

4. Generate and run ten tests covering quality, privacy, security and autonomy.

5. Route unresolved findings through a recorded approval decision.

6. Publish the final controls, monitoring metrics and reassessment triggers.

6) Metrics

• Percentage of production AI workflows registered

• Percentage with named accountable owners

• Pre-release critical issues discovered

• Time from governance submission to decision

• High-risk workflows with active monitoring

• Permission violations detected

• Post-launch incident rate

• Accepted risks without mitigation plans

• Time to disable a problematic workflow

• Governance controls retired because they produced no value

Pro Tip: If your AI governance cannot tell a team whether something is safe enough to ship, it is probably documentation—not governance.

🎯 The Arsenal — Tools & Platforms

• Microsoft Purview · tracks sensitive information, compliance and audit activity · Link

• Microsoft Entra ID · controls user and agent permissions across enterprise systems · Link

• Azure AI Evaluation · runs repeatable quality and safety evaluations across AI workflows · Link

• Airtable · maintains practical AI use-case, risk and approval registers · Link

• Microsoft Teams Approvals · records accountable governance decisions inside existing workflows · Link

Copy-paste prompt block:

You are designing an operational AI governance system for my organisation.

Organisation:
[DESCRIPTION]

Current AI systems:
[LIST]

Sensitive data:
[LIST]

Regulatory requirements:
[LIST]

Existing identity and security stack:
[LIST]

Business owners:
[LIST]

The governance framework must:
- register all material AI use cases
- classify risk proportionately
- generate mandatory tests based on risk
- evaluate quality, security, privacy and autonomy
- enforce least-privilege access
- record accountable human approvals
- maintain a complete audit trail
- support emergency shutdown
- automatically trigger reassessment after major changes
- avoid unnecessary bureaucracy for low-risk use cases

Return:
1. governance operating model
2. risk-classification rubric
3. architecture
4. evaluation framework
5. approval matrix
6. monitoring model
7. incident process
8. pilot rollout
9. operational metrics

💡 Free Office Hours

AI governance is rapidly becoming unavoidable. The opportunity is to build it as infrastructure that enables deployment—not bureaucracy that merely proves somebody held a meeting about it.

Book here: https://calendly.com

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🕹️ Game Over

Governments are commissioning AI inquiries.

Frontier-lab employees are becoming multimillionaires.

And Zuck is throwing roundhouse kicks while open-sourcing models.

Perfectly normal industry.

— Aaron Automating the boring. Amplifying the brilliant.

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