AI Governance

What is AI Governance?

AI governance is how an organization, in practice, decides who gets to build AI systems, what those systems are allowed to do, and how someone checks the work afterward.

It's less a single policy document and more a working system: assigned roles, review steps, technical guardrails, and logs that let you prove what happened when something goes wrong.

Governance is what connects the two day to day, the actual mechanics that make sure good intentions and legal requirements show up in how a system behaves.

Done well, governance stops being just downside protection. It becomes something you can point to when a customer or regulator asks how you're using AI responsibly, and that increasingly shows up as a factor in the deal itself.

Why AI Governance Matters

Regulation is no longer optional

Every composable component is pre-validated. Security, access controls, and data handling are embedded in the building blocks — not added as an afterthought.

Agents change the risk

Because components are purpose-built for enterprise environments, applications handle integration complexity, load, and edge cases that prototype-grade code cannot.

Trust is the real bottleneck

Trust is the real bottleneck. A report by the IBM Institute for Business Value found roughly 80% of business leaders point to concerns about explainability, bias, ethics, or trust, not a shortage of use cases, as the biggest thing holding back generative AI adoption.

A separate global study of over 48,000 people across 47 countries found the same gap from the other direction: 66% of people already use AI regularly, but only 46% say they actually trust it.

Governance lets you move fast not what slows you down.

Teams that skip it don't actually ship quicker; they just find out about the risk later, usually after something's already live.

Clear boundaries set upfront let people build with confidence instead of second-guessing every decision, and that's what actually speeds delivery up.

Sources:

What is AI governance?
Global study reveals trust of AI remains a critical challenge
Why responsible AI governance is important

AI Governance in Joget

Joget is built on the idea that enterprises should be able to build and govern apps at the speed of business, without giving up control. That principle runs through every AI capability on the platform, across three layers that together cover an AI system's entire lifecycle.

Design-time

What's being built, by whom, at what cost

Runtime

What an agent does while it's running, and whether it's safe

Usage & cost

What it's costing you, platform-wide

Runtime governance

Guards and Execution Audit Trails

Applies to Agent Builder and AI Composer

Every message that touches an LLM inside Joget runs through a content safety pipeline first, both before it reaches the model and after the model responds.

When something trips a check, Joget can block the task, sanitize the content, or redirect execution to a safer path.

THE THREE GUARD TYPES:

LLM-Based Guard

Hands the safety decision to a second model, such as Llama Guard, running against a safety prompt you configure

Rule-Based Guard

Checks content against regex patterns and keyword blocklists. Redacts matches with [REDACTED] by default.

Webhook Guard

Posts content to your own external endpoint, which returns a straight safe or unsafe decision.

What the Execution Audit Trail logs

Every agent run is captured in full: who ran it, what ran, how it ran, and when. Click into any record and a node-by-node graph shows the full execution trace, with each node's request, response, and status.

If a run is still in progress, an Abort control sits right in the detail view, with a confirmation step before it stops the execution.

Design-time governance

The AI Designer Governance Console

Applies to AI Designer, visible to Admins and System Managers

Governance doesn't wait for an agent to go live. Every AI Designer session, meaning every time someone uses AI to build or modify an app, is tracked from the moment it starts, down to which app, which user, and every individual LLM call made along the way, at what token cost.

Usage & cost governance

The AI Usage Dashboard

Platform-wide, visible to Admins and System Managers

Zoom out from a single session and you get the whole platform. Every LLM call made through Agent Builder, aggregated into trends you can actually act on, token usage over time, broken down by model and by plugin, so cost isn't a surprise you find out about at the end of the month.

What each system governs

Not every layer covers the same ground. Here's how they compare:

Concern	Agent Builder	AI Composer	AI Designer
Content safety	Guards (LLM / Rule / Webhook)	Guards (LLM / Rule / Webhook)	Not applicable
Audit & traceability	Execution Audit Trail	Execution Audit Trail	Governance Console
Token / cost tracking	Input / output token counts	Input / output token counts	Full breakdown incl. cache tokens
Access control	System Manager (audit) App Creator (execution)	System Manager (audit) App Creator (execution)	System Manager only
Abort running execution	Available	Available	Not applicable
Session tracking	Run ID-based	Run ID-based	Session ID-based

Put together: design-time governance catches issues before an agent ever runs, runtime governance controls what it does while it's running, and usage governance keeps the cost of all of it visible. That's coverage across an AI system's entire lifecycle.

Implementing a Guardrail Layer in AI Agents

Learn how to actively control and sanitize AI agent outputs

This quick walkthrough shows you how to detect policy violations and trigger automated safety actions such as Blocking, Sanitizing, or Redirecting responses using custom LLM checks, specific word patterns, and external APIs.

See it running on your own use case

Bring one real AI workflow and we'll show you exactly what gets logged, guarded, and governed.

Frequently Asked Questions

What's the difference between AI governance and AI ethics?

AI ethics defines the values, like fairness and transparency.
AI governance is the operational layer that turns those values into policies, approval workflows, and monitoring that actually get enforced.

Do AI agents need different governance than regular AI models?

Yes. Agents take actions, not just generate outputs, so governance has to cover execution permissions, autonomy levels, and real-time monitoring, not just content review.

Can a running AI agent in Joget be stopped mid-execution?

Yes. The Execution Audit Trail includes an Abort control with a confirmation step, right in the run detail view.

Who can see the AI Designer governance console?

Admins and System Managers. Every AI Designer session, and every LLM call inside it, is tracked from the moment it starts.