The Standards and Practices Review

Opening Scene

Every broadcast network has a standards and practices review — a formal process checking that content meets legal, regulatory, and organizational requirements before it airs. It’s not a creative afterthought; it’s a genuine, necessary gate that shapes decisions well upstream of the actual broadcast. Production LLM systems increasingly need this same formal governance layer, addressed deliberately rather than discovered as a problem after deployment.

In Plain English

Governance and compliance for LLM systems covers a genuinely broad set of concerns: data handling and privacy for what gets sent to and logged from a model, auditability for how and why the system produced a given output, and adherence to whatever regulatory requirements apply to a specific industry or jurisdiction. This connects directly to this content library’s dedicated series on AI governance and responsible AI, applied here as a concrete, operational requirement rather than an abstract principle.

The Old Way

Before governance was treated as a standard, upstream requirement for production LLM systems, it was often addressed too late:

Compliance and governance requirements were sometimes considered only after a system was already built, rather than shaping design decisions from the start.
Logging and data handling practices, covered in Article 14, weren’t always designed with privacy and regulatory requirements explicitly in mind.
There wasn’t yet a well-established practice of building auditability — a clear record of why a system produced a specific output — into a system’s design from the outset.

Treating governance as an upstream, design-shaping requirement, rather than a late add-on, reflects a maturing recognition of its genuine, sometimes significant consequences when addressed too late.

What’s Changing (and Why AI Is the Reason)

Governance and compliance requirements increasingly shape LLM system design from the start, connecting directly to this content library’s dedicated AI governance and responsible AI series.
Logging practices, covered in Article 14, increasingly get designed explicitly with privacy and regulatory requirements in mind, rather than added retroactively.
Auditability — a clear, reconstructable record of why a system produced a given output — has become an increasingly standard design requirement, not an afterthought.

The Metaphor, Fully Extended

The Broadcast	Governance and Compliance Concept
A standards and practices review before content airs	A governance and compliance review before an LLM system deploys
A necessary gate, not a creative afterthought	A necessary requirement, not a late-stage add-on
Shaping decisions well upstream of the actual broadcast	Shaping system design well upstream of actual deployment
A formal, documented review process	A formal, documented compliance and auditability process

For Beginners: What to Actually Do

Practice identifying the specific data handling and privacy requirements relevant to a project before building its logging and data flows.
Learn the basic idea of auditability: being able to reconstruct why a system produced a specific output after the fact.
Get comfortable exploring this content library’s dedicated AI governance and responsible AI series for the broader principles this practice draws on.

For Practitioners and Leaders: The Deeper Layer

Build governance and compliance requirements into a project’s design from the start, not as a late-stage review before launch.
Design logging practices, covered in Article 14, explicitly with privacy and regulatory requirements in mind from the outset.
Connect governance practice directly to this content library’s dedicated AI governance and responsible AI series for the fuller organizational context.

Quick Recap

Governance and compliance for LLM systems covers data handling, privacy, auditability, and regulatory adherence.
These requirements should shape system design from the start, not be addressed as a late-stage add-on.
Logging practices need to be designed explicitly with privacy and regulatory requirements in mind.
This connects directly to the broader principles covered in this content library’s AI governance and responsible AI series.

Where This Fits in the Series

Article 15 covered governance and compliance as an upstream design requirement. Article 16 turns to rehearsing the blackout: practicing incident response before a real one happens.