// 01
What is an AI governance framework?
An AI governance framework is the set of policies, roles, review gates, evaluation methods, and audit records used to decide how AI workflows are scoped, tested, approved, and monitored.
Responsible AI in practice.
AIRIG treats responsible AI as a working discipline: define the use case, review the data, test the workflow, document evidence, and keep people accountable for outcomes.
AIRIG governance run
LIVE
// 01DEFINEScopeREADY
// 02INSPECTReview dataREADY
// 03TESTStress testREADY
// 04MONITOROperateREADY
Controls
24
Review gates
8
Open risks
0
// 03A - Governance evidence
Responsible AI becomes an operating system for review.
AIRIG connects governance language to implementation evidence: policies, evaluation runs, owners, review queues, and audit logs.
Policy controls
Evaluation runs
Owner assignment
Change monitoring
Governance control plane
VERIFIED
Inputs
Scoped
Review
Human
Record
Trace
// 01 - Foundational standards
Controls that turn responsible AI from statement to workflow.
AIRIG validates each AI workflow through practical safety, privacy, governance, transparency, and human oversight controls.
// 01
Safety review
Map likely misuse, failure modes, escalation paths, and approval gates before a workflow moves into pilot.
> safety.review() :: 4 controls required
// 02
Transparency
Keep data sources, prompts, configuration choices, model changes, and reviewer responsibilities visible.
> trace.visible :: sources + reviewers
// 03
Governance
Define ownership, review cadence, release rules, incident paths, and change controls for AI-assisted work.
> governance.owner :: assigned
// 04
Privacy-aware design
Minimise sensitive data exposure, set retention rules, and apply controls that match the use case.
> privacy.scan :: 0 critical leaks
// 02 - Validation process
A governance workflow for deciding what is ready.
Use this flow to decide whether an AI use case is ready to test, pilot, deploy, or postpone.
// 01
Scope
DEFINE
Clarify purpose, users, affected parties, risk level, and decisions AI must not make.
// 02
Review data
INSPECT
Check source quality, permissions, sensitivity, representativeness, and known gaps.
// 03
Stress test
TEST
Run edge cases, ambiguous prompts, misuse scenarios, source gaps, and review handoffs.
// 04
Operate
MONITOR
Track feedback, incidents, quality drift, and workflow changes after deployment.
// 03 - Practice principles
Three principles that keep AI work accountable.
Responsible AI has to show up in product surfaces, review queues, operating rules, and audit evidence.
// 01
Human accountability
AI outputs remain working material until a responsible person reviews, accepts, revises, or rejects them.
// 02
Evidence over confidence
The interface should expose supporting material and uncertainty instead of hiding behind polished answers.
// 03
Narrow before broad
Start with scoped workflows, measurable review criteria, and clear stop conditions before expanding adoption.
// 04 - Research notes
Practical briefings for responsible AI adoption.
Field notes on governance, evaluation, product decisions, and human oversight in AI-assisted work.
// 05 - Responsible AI FAQ
Questions about governance, evaluation, and guardrails.
Concise definitions for teams comparing AI governance frameworks and practical evaluation methods.
// 02
What is an evaluation harness?
An evaluation harness is a repeatable testing setup that checks AI outputs against expected behavior, source quality, edge cases, reviewer decisions, and operational risk criteria.
// 03
How does AIRIG make responsible AI practical?
AIRIG turns responsible AI into operating steps: scope the use case, review data, test the workflow, document evidence, assign owners, and monitor changes after deployment.
// 06 - Responsible AI support
Build AI that can be reviewed.
AIRIG can help scope responsible AI reviews, policy questions, evaluation plans, and product governance needs.