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AI governance

Clear rules. Secure use. In practice.

We implement AI governance using IBM watsonx.governance to help organisations keep AI under control at scale. From policies and accountability to model documentation, validation gates and monitoring, we put practical governance in place, so AI remains safe, traceable and operational over time.

Service overview

Governance only works if it is embedded into delivery and operations. With watsonx.governance, we turn governance requirements into workflows, evidence and continuous oversight.

  • Governance operating model (roles, ownership, decision rights)
  • Use case scope & risk classification
  • Model and data documentation (evidence ready)
  • Validation gates (models and agents) before go-live
  • Monitoring, drift and agent behaviour
  • What we implement

watsonx.governance in practice

We configure and embed governance capabilities so that your teams can manage AI consistently across the lifecycle, from initial assessment to production and continuous monitoring. The objective is simple: the right level of control, without blocking delivery.

How we implement AI governance

01.
Assess
Clarify use cases, stakeholders, data sensitivity and risk scenarios. Define what must be controlled and what evidence is required.
02.
Design the governance setup
Define policies, accountability, decision gates, and documentation requirements. Align with your internal controls and delivery model.
03.
Configure watsonx.governance
Set up workflows, documentation structures, approval gates and monitoring routines so governance is applied consistently.
01.
Embed into delivery & operations
Integrate governance into project rituals (testing, sign-offs, go-live readiness) and define ownership for ongoing monitoring and change management.

Bias and fairness: make it measurable

Where AI influences people or prioritises cases, bias risk must be treated explicitly. We define concrete fairness risks for your context (e.g., HR, customer cases), test representative scenarios, and implement controls and review routines so issues can be detected and corrected early.

General questions

Yes. We can assess current practices and implement governance retroactively: documentation, approval gates, monitoring routines and clear ownership.

Not if it is designed pragmatically. We set the lightest governance that provides real control, and we embed it into delivery rituals rather than adding bureaucracy.

We define risk scenarios, agree what “fair” means for the use case, test representative situations, and set safeguards and review routines to detect and correct issues.

A list of priority use cases, key stakeholders, and the data/process context. We typically start with a focused diagnostic to confirm scope, risks and the right governance setup.

You will receive a practical governance setup that your teams can run, including:
• A clear governance operating model (roles, ownership, decision gates)
• A documented risk and control framework aligned to your use cases
• An evidence-ready documentation pack (use cases, data, models, approvals)
• A defined validation and go-live readiness approach (criteria, checks, sign-off)
• Monitoring and run routines (reviews, drift/behaviour monitoring, incident handling)

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