Responsible AI in practice
AI Governance Setup
Establish who can approve AI use, what evidence they need, which limits apply and who remains accountable. Practical governance for existing and proposed systems.
Start with what your business needs. We’ll help you work out the next step.
Discuss your projectGive AI permission to move forward—and clear limits.
A team wants to launch an AI assistant. IT asks who will support it. Security asks which systems it can access. Legal asks what information leaves the organisation. The business sponsor asks who can approve it. If each question opens a new conversation with no decision owner, the problem is not a missing policy document. It is a missing way to make decisions.
Open for AI's AI governance setup service helps organisations in Saudi Arabia establish the responsibilities, approval routes, evidence and ongoing reviews needed to use AI deliberately. We turn governance into a working process for proposed systems, purchased AI features and existing deployments—not a document library that sits apart from delivery.
The desired result is practical: people know what they may do, which decisions require review, what evidence to provide, and who is accountable after launch.
Start with the decisions your organisation cannot make today
Governance should answer questions that otherwise delay work or expose the organisation to poorly understood commitments. Can a department trial a supplier's AI feature? Can an agent use customer records? Who can expand it from drafting responses to sending them? What happens when a vendor changes the model behind a service?
We use representative decisions to design the operating model. This makes the gaps concrete: an unnamed system owner, an unclear approval threshold, a supplier review that ignores data flows, or an exception accepted indefinitely without a review date.
The service can cover one business unit, a portfolio of use cases or an enterprise-wide baseline. The perimeter is agreed first. A lightweight structure for a small portfolio and a formal model for multiple regulated workflows should not produce the same administrative burden.
A proportionate route from idea to permitted use
We design a common intake that captures the proposed purpose, affected users, information sources, supplier dependencies, intended actions and accountable sponsor. It should also identify the alternative: manual work, conventional automation or no implementation. This prevents “uses AI” from becoming the only justification for approval.
Each use case then receives a review route based on its actual consequences and operating context. A drafting tool and a system that changes customer entitlements deserve different scrutiny. We agree the criteria with your decision-makers; our internal categories are not presented as statutory risk classifications.
| Proposed use | Evidence to examine | Decision to record |
|---|---|---|
| Assist an employee with low-consequence drafting | Permitted information, review expectations and approved tool | Permitted purpose, user responsibilities and usage limits |
| Retrieve restricted enterprise information | Access design, source ownership, leakage tests and retention | Approved users, datasets and unresolved access risks |
| Recommend a consequential business decision | Evaluation evidence, affected groups, limitations and reviewer authority | Decision boundary, oversight and challenge route |
| Execute an external or irreversible action | Tool permissions, approval enforcement, recovery and incident plan | Authorised actions, limits and conditions for suspension |
The approval record states what was approved and what was not. “Approved for customer service” is too vague. An actionable record identifies the workflow, intended users, relevant data, permitted actions, conditions, expiry or review trigger, and named owner.
Make accountability visible at the point of decision
We establish a responsibility map that distinguishes proposing a system, evaluating it, authorising it and operating it. An AI committee can resolve material decisions, but it should not become an inbox for every minor configuration change.
Business and system ownership
The business owner defines intended benefit, acceptable outcomes and consequences of error. The system owner maintains the technical implementation and its approved boundaries. Both roles need a clear route to stop or restrict use when conditions are no longer met.
Data, security and privacy review
Data owners establish source authority and permitted uses. Security reviews access and technical exposure. Privacy and legal specialists determine the requirements relevant to the proposed processing and contracts. Their conclusions should become specific conditions in the system record, rather than detached attachments nobody consults during a change.
Risk acceptance and oversight
A named authority accepts residual risk within the organisation's own delegation rules. Acceptance records the rationale, compensating measures and review date. It does not erase the risk, and the person implementing a control should not silently assume authority to waive it.
We define what must reach executive oversight: material incidents, overdue actions, exceptions approaching expiry, high-consequence use cases and significant changes. Routine reporting should help leadership decide, not simply count how many AI tools have been purchased.
What we establish in the governance setup
An AI inventory with meaningful scope. The register covers agreed in-house systems, vendor applications and embedded AI features. Each record has an owner, purpose, users, dependencies, data categories, operating status and review history. Discovery has a defined perimeter; an incomplete inventory is labelled as such.
Policies that tell people what to do. We develop or adapt acceptable-use rules, procurement requirements, development and release expectations, human oversight, incident handling and retirement procedures. Each policy is connected to the process where it is applied. We reuse existing risk, change and security processes wherever practical.
Decision-ready assessment records. The forms capture intended and reasonably foreseeable use, affected people, potential harms, limitations, controls and residual risks. Evidence is linked to the claim it supports. A supplier declaration is distinguished from an observed test.
Supplier and contract questions. We establish the evidence to request about processing locations, model providers, retention, use of submitted data, security, incident notification, material changes, access and exit. Procurement and legal teams determine the contractual outcome; a questionnaire is not a substitute for that decision.
Exception and change management. Temporary permission has an owner, reason, compensating measures and expiry. Changes to the model, purpose, sources, users or action permissions can reopen review. The trigger depends on the significance of the change, not merely whether the application version number changed.
Role-specific enablement. End users learn what they may submit and what to review. Approvers learn to examine evidence and limitations. Technical teams learn which changes require approval. Operating teams practise incident escalation and suspension. Completion of a training session is not, by itself, proof that the governance process works.
Saudi requirements, international frameworks and your own commitments
We separate four kinds of requirement: applicable law, relevant sector obligations, contracts and voluntarily adopted frameworks. They can overlap, but they are not interchangeable. The applicability record states the source, responsible reviewer, affected system and evidence needed.
For personal-data processing, the starting reference includes SDAIA's guidance for controllers and processors. We help organise the system and data-flow evidence that your qualified privacy and legal advisers need. The engagement does not turn a generic template into a legal opinion or assert that every Saudi AI workload has identical hosting requirements.
SDAIA's AI ethics self-assessment is another relevant reference for examining current practices. Completing a self-assessment is distinct from obtaining external certification or regulatory approval.
Internationally, NIST AI RMF 1.0 organises risk-management outcomes around Govern, Map, Measure and Manage. ISO/IEC 42001 addresses an organisational AI management system. We agree which framework and edition are relevant, then map your actual processes to it. A mapping is evidence of how a framework was used, not a certificate.
Where certification is your objective, readiness work must be scoped against the applicable licensed requirements and followed through the appropriate assessment route. ISO distinguishes standard-setting from certification. This service does not imply that Open for AI is an accredited certification body.
Test the process with a change, not just a workshop
Consider a fictional organisation that has approved an assistant to draft Arabic and English sales responses. A team later wants it to send messages automatically. The product is familiar and the users are unchanged, but the action authority is materially different.
In a working governance process, the change triggers a review of sending permissions, claim approval, recipient handling, monitoring and recovery. The record identifies who approves the expansion and what evidence is needed. If that evidence is incomplete, the organisation can keep the drafting function running while withholding the new action.
We use agreed scenarios like this to walk the process from intake to decision. Another scenario might involve an undocumented supplier change or an expired exception. The exercise establishes whether the forms, owners and escalation routes work together. It should produce decisions and corrections, not just attendance records.
What a completed setup should leave behind
The deliverable is an adopted operating model with usable records: the inventory, responsibility map, approval routes, policy set, assessment method, supplier questions, exception register, change triggers and reporting cadence. The precise set is matched to the scope rather than sold as a fixed number of documents.
We agree acceptance around operational demonstrations. A new use case can find the right review route. An approver can locate the evidence and unresolved risks. An owner can identify the conditions attached to a live system. A material change or incident reaches the responsible authority.
Measures can include time to a complete decision, overdue conditions, ageing exceptions and the proportion of in-scope systems with current owners and evidence. These measures assess the process; they are not proof that the entire portfolio is risk-free.
Scope and fees depend on the number of organisational units, existing governance processes, system categories, decision authorities, languages and desired framework mapping. We agree access, participants and decision availability before promising a delivery schedule. Your organisation retains responsibility for adopting policies and making risk decisions.
Governance that remains connected to implementation
Where your data and workflow prerequisites are uncertain, start with enterprise data and AI readiness. Once systems are running, AgentOps supplies operational evidence. AI auditing examines selected controls and claims within a defined scope.
Discuss your AI governance setup. Bring the decisions currently causing delay or uncertainty, the teams involved and the AI systems already in use. We will define the minimum workable governance structure for that reality, with room to grow.
FAQ
Your questions, answered.
Is this just an AI policy-writing engagement?
No. Policies are one part of the setup. The engagement connects them to system inventory, decision rights, review routes, evidence, exceptions, change management and operating responsibilities. Acceptance includes walking representative decisions through the process.
Do we need a new AI committee?
Not necessarily. We first examine existing risk, security, procurement and change forums. A new forum is justified only if decision ownership or escalation cannot be handled effectively through those structures. Routine decisions should have a proportionate route.
Does governance cover AI features bought from software vendors?
Yes, within the agreed perimeter. Purchased and embedded AI can still introduce questions about data, use restrictions, approvals, model changes and operational responsibility. The inventory and review process should identify those features rather than cover only internally developed systems.
Can this prepare us for ISO/IEC 42001 certification?
Certification readiness can be an expressly scoped objective, using the applicable requirements and agreed assessment route. Setup work or a framework mapping is not a certificate, and this service does not represent Open for AI as an accredited certification body.
Can our existing security and privacy policies remain?
Yes. The preferred approach is to retain useful existing controls and add the AI-specific responsibilities, evidence and decision triggers that are missing. Separate AI documents should not contradict your existing delegation, security, privacy or procurement rules.
Who makes the final decision to accept a risk?
The authority designated by your organisation. We help define the evidence, options and recorded conditions. Accountability for accepting residual risk, approving a use and adopting policy remains with your authorised decision-makers.