Responsible AI Governance

Turn AI principles into controls people can actually operate.

Galang AI translates responsible-AI policy into practical inventories, risk tiers, evaluation requirements, approval gates, audit evidence, and recurring operational review.

Explore capabilities
02 / Control systemHUMAN AUTHORITY

Human authority

Consequential actions

Policy
Evaluate
Log
Override
Policy, evaluation, logging, and override mechanisms orbit one center of gravity: authorized human judgment.
  • 01NIST AI RMF-aligned planning without implying certification
  • 02Human authority defined for consequential actions
  • 03Controls tied to evidence, ownership, and operating cadence
  • 04Technical and organizational governance designed together

What we implement

Governance that reaches the system and the workflow.

01

AI inventory and risk tiering

Create a usable record of AI use cases, owners, data, models, affected users, intended outcomes, and the level of review each system requires.

  • Use-case intake and accountable ownership
  • Risk and impact classification
  • Decision records and approved-use boundaries
02

Evaluation and evidence

Define what good performance means for the actual workflow and retain evidence that the system was tested against relevant failure modes.

  • Quality, groundedness, and safety criteria
  • Representative test sets and red-team scenarios
  • Release evidence and change documentation
03

Human control and incident paths

Make approval, override, escalation, and shutdown responsibilities explicit before the system affects consequential work.

  • Authority matrix for draft, recommendation, and action
  • Override and exception handling
  • Incident intake, triage, and corrective action
04

Operational monitoring

Keep governance alive after launch through telemetry, review schedules, change controls, and periodic reassessment.

  • Model and workflow monitoring
  • Vendor and model change review
  • Control testing and governance reporting

Operator-led delivery

Govern, map, measure, and manage in the real operating environment.

  1. 01

    Establish the decision context

    Inventory use cases, policies, stakeholders, obligations, risk appetite, and existing technology controls.

  2. 02

    Design the control system

    Define tiers, evidence requirements, human authorities, technical guardrails, and the workflow teams will follow.

  3. 03

    Operationalize and test

    Implement intake, evaluation, logs, reviews, escalation, and reporting; then test whether the controls work in practice.

Questions buyers ask

Straight answers before you engage.

What is responsible AI governance?

Responsible AI governance is the operating system around how an organization selects, builds, evaluates, approves, monitors, and retires AI. It connects policy to named owners, risk tiers, required evidence, human authority, technical controls, and recurring review.

Do you only write AI policies?

No. Policy is useful only when teams can apply it. Galang AI helps implement inventories, intake workflows, control gates, evaluation plans, documentation, logs, escalation paths, and operating cadences alongside the written standard.

Can you map governance to the NIST AI Risk Management Framework?

Yes. We can use the NIST AI RMF and its Govern, Map, Measure, and Manage functions as a practical organizing framework. A mapping exercise does not itself constitute government certification, authorization, or a guarantee that every applicable obligation has been satisfied.

How do you decide where human approval is required?

We examine impact, reversibility, data sensitivity, affected people, legal and policy obligations, model uncertainty, and the authority delegated to the system. The resulting control matrix states what AI may draft, recommend, or execute and who can approve or override it.

Can governance work with multiple AI vendors and internal models?

Yes. The governance model should describe the use case, data, decision, control, and evidence requirements rather than assume one vendor. Vendor-specific technical controls can then be attached to the same enterprise operating standard.

Related Galang AI resources

Knowledge & RAG SystemsSource-grounded systems with access controls, citations, and audit trails.
Government AI ConsultingResponsible implementation for public-sector and regulated operating environments.
Managed AI OperationsMonitoring, governance, and continuous improvement after deployment.

Govern the decision, not just the model.

Start with one real AI use case. We will identify the evidence, authority, controls, and operating routine needed to deploy it responsibly.