A Practical Guide to Google’s AI Regulation Principles

TLDR: Google recently outlined seven principles for writing sensible AI regulation. They focus on outputs, practical safeguards, closing gaps in existing laws, and giving regulators the technical support they need. For organisations trying to understand how policy should be shaped, or how to shape their own internal rules: the value lies in the simplicity. Regulation works when it focuses on measurable behaviour, not technical theory, and when agencies are equipped to apply the rules consistently.

Google published an article describing how governments could regulate AI without slowing productive use. They argued for rules that focus on outputs, rely on existing laws where possible, empower agencies with technical capability and keep regulation tied to clear evidence of harm.

They also emphasised the need for alignment across jurisdictions so organisations don’t end up navigating a patchwork of conflicting requirements.The article highlighted a point that often gets buried: regulation is most effective when it is built around what AI does, not how AI models are built.

This is important for non-technical leaders because it keeps the discussion grounded in behaviour, responsibility and traceability: things organisations can actually manage.

Why the focus on outputs matters

In simple terms, Google suggests that regulation should look at the quality and safety of AI outputs. If a system creates harmful, misleading or unsafe outcomes, regulators can intervene directly because the evidence is visible and measurable.
This avoids regulating model internals or algorithms that change too fast for legislation to keep up.

For organisations, this mirrors how internal governance should work.
You don’t need to audit the architecture of a model.
You need to check what it produces, who used it, what controls were applied and what information it accessed.
That’s manageable.

It’s also the foundation of ISO 42001 and 42011 thinking: traceability, explainability and clear accountability pathways.

Where existing laws already cover the risks

Google’s piece also reinforces something often forgotten in public debates: many risks associated with AI are already illegal under existing laws.

Fraud remains fraud.
Discrimination remains discrimination.
Consumer protection still applies.

The gap isn’t the law itself.

It’s whether those laws are updated to recognise AI as a tool used within the existing behaviours. This is why “new AI-specific regulation” can sometimes cause duplication rather than clarity. The smarter path is identifying which laws already apply, which need updates and which areas currently lack coverage.

For organisations setting internal policy, this approach keeps governance practical. So, start by mapping AI use to existing policies. Most of the required controls are already in place, they just need AI-specific language or workflows.

One of the clearer points in Google’s article is the need for regulators to have access to real technical capability.

A central “hub” of expertise supporting many organisations (the “hub-and-spoke” model) ensures consistency across sectors.

• Banking risks differ from healthcare risks.
  
• Transport differs from education.
  
• But each domain can rely on a shared technical baseline.

This aligns with what many organisations already face internally. Different departments use AI differently, but the standards for documentation, access, review and oversight should be common. That central capability, even if small — prevents fragmentation.

What responsible innovation looks like

Google frames innovation and safeguards as complementary.
This is often misunderstood as a political line, but the operational reality is straightforward. Organisations adopt AI more confidently when expectations are clear, documentation is simple and oversight doesn’t create unnecessary delays.

Responsible AI innovation is not a slogan.

It is:

• clear access rules
  
• controlled data sources
  
• explainable outputs
  
• predictable review steps
  
• measurable compliance

When they exist, AI can be used more broadly because people know where the boundaries sit.

Why alignment matters for organisations

Google’s article points out that hundreds of AI bills exist across U.S. states alone.
A fragmented regulatory environment is expensive, slow and confusing.
Alignment lowers the operational burden.

For organisations, the same principle applies internally.
If each department invents AI rules independently, confusion grows.
If the finance team has one set of expectations, operations has another and customer service has none, the governance burden becomes unsustainable.

The clearest value in Google’s piece is this:

AI regulation works when it focuses on behaviour, evidence and accountability instead of technical analysis.

This is exactly how internal AI policy should function:

1. focus on what the system produces
   
2. check for identifiable risks
   
3. define who is responsible
   
4. record how the output was generated
   
5. use clear documents as the baseline
   
6. treat AI as a tool inside existing legal frameworks
   

This keeps governance practical for teams who don’t work in technical roles.

It also keeps organisations aligned with emerging international approaches, including ISO 42011 AIMS.

FAQs

Q: Do we need our own regulatory framework to use AI safely?
A: No. Start by mapping your existing policies to AI use. Most organisations already have the foundations; they just need updates.

Q: How do we know if an AI output is “high-quality”?
A: Define criteria tied to your context: accuracy, clarity, relevance, source traceability and compliance with internal standards.

Q: What if our organisation has no technical capability?
A: You don’t need it to start. You need clear documentation, simple access rules and a controlled environment. Technical depth comes later.

Q: How do we prevent inconsistent AI adoption across departments?
A: Set a single baseline policy. Departments can extend it, but not replace it.

Q: Do small organisations need to think about regulation?
A: Yes, but only in proportion to your use cases. The principles still apply: clarity, accountability, controlled data and documented decisions.

Q: What’s the first practical step?
A: Identify where AI is already being used, even informally. Create transparency before creating new rules.

If your organisation needs a structured, ISO-aligned way to develop clear AI policies, our AI Policy Authoring Program (aligned to ISO 42011 AIMS) gives you the tools and templates to do it properly.