We assess your AI systems against regulatory requirements and responsible-AI principles, documenting every gap. A model risk register and mechanisms to monitor bias and drift over time are put in place. The service keeps your systems aligned with evolving regulation while reducing legal and reputational exposure.
What's included
- Assess existing AI systems against responsible-AI principles and Saudi regulatory requirements, documenting every gap.
- Fairness and bias analysis across sensitive attributes, measuring performance and outcome disparity between groups.
- Strengthen transparency and explainability through model cards and documented decision logic.
- Build a model risk register linking each risk to a mitigating control, an owner, and an indicator.
- Design continuous monitoring for bias, drift, and performance degradation over time with alerting thresholds.
- Align personal-data processing inside models with PDPL and SDAIA principles.
Methodology & standards
Inventory and classification: catalogue AI systems, use cases, data, and their level of impact on individuals.
Compliance assessment: measure each system against SDAIA principles, PDPL, and NIST AI RMF, evidencing gaps.
Fairness and explainability analysis: bias tests, model cards, and documented explainability for high-impact cases.
Remediation and governance: a prioritised remediation plan, a risk register, and human-oversight controls.
Continuous monitoring: bias and drift dashboards, alerting thresholds, and periodic review procedures.
Deliverables
- A responsible-AI compliance assessment report with a gap matrix and evidence.
- A fairness and bias analysis report with quantitative metrics for each high-impact model.
- A model risk register with mitigating controls, owners, and indicators.
- Documented model cards for the systems in scope.
- A prioritised remediation plan and a continuous-monitoring roadmap.
- An executive summary linking risks to legal and reputational impact.
Regulatory controls it satisfies
Typical timeline
The initial assessment is typically delivered in three to six weeks, with monitoring continuing on an agreed cycle thereafter.
Common questions
How do you measure bias in a model we treat as a black box?
We measure bias at the input and output level without needing access to model weights, by comparing outcomes and error rates across groups on representative samples. That is enough to detect and document disparity and propose mitigating controls, even for external models.
Is compliance a one-time event or an ongoing process?
It is ongoing. Models drift, their data changes, and regulation evolves, so we deliver a baseline assessment and then monitoring mechanisms, alerting thresholds, and periodic reviews that keep you aligned over time.
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