AI Governance
AI governance is the set of policies, records and reviews that make an organisation's AI use accountable and auditable.
/ quick answer
Practical governance answers five questions on paper: which systems use AI and for what, what data they touch, who approved them, what the human oversight is, and how incidents are handled. In regulated contexts it also covers model documentation, retention and the ability to explain a decision. Governance is what turns a pile of automations into something a…
What is AI Governance?
Practical governance answers five questions on paper: which systems use AI and for what, what data they touch, who approved them, what the human oversight is, and how incidents are handled. In regulated contexts it also covers model documentation, retention and the ability to explain a decision. Governance is what turns a pile of automations into something a board and a regulator can accept.
What is an example of AI Governance?
An AI register listing 14 internal systems, each with an owner, a risk tier, a data classification and a review date.
Why does AI Governance matter for AI and automation?
AI governance is the set of policies, records and reviews that make an organisation's AI use accountable and auditable. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Human-in-the-Loop
A control pattern where humans review high-risk AI decisions before execution.
- →AI Employee
An AI employee is a persistent agent that owns a defined role — with a job description, tools, memory, KPIs and a manager — instead of running as a one-off task.
- →Agent Cost Control
Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
- →AI Evaluation
AI evaluation is the measurement layer of an AI system: a fixed set of cases, a scoring method and a tracked pass rate you can regress against.
Related workflows
Turn this into a repeatable process.
- →Harden an AI System Against Injection and Misuse
Architectural controls that survive a manipulated model.
- →Hire an AI Employee (Role, Tools, KPIs)
Treat the agent like a hire: job description, onboarding, probation, review.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
- →Audit MCP Tool Security
A checklist that catches the failure modes unique to model-driven tool calls.
Related tool stacks
The tools that run it in production.
- →AI Security Stack
Least-privilege tooling, approval gates and audit trails for agentic systems.
- →Data Residency Enforcement Stack
This stack outlines the essential tools and practices for enforcing data residency policies within an organization, particularly for cloud-based data storage and processing.
- →AI Employee Stack
Everything a role-owning agent needs: knowledge, tools, memory and reporting.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
Related prompts
Reusable prompts for this job.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
- →AI Employee Job Description Prompt
Writes the role spec, KPIs and review cadence for an agent that owns a function.
- →Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
Related use cases
How people apply it, and what came out.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
Comparisons & alternatives
Pick between the options.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.
- →GPT vs Claude for Business Workflows
Choosing the right model family for production use.