Data Integrity Assessment
Trusted data, on purpose.
Not by accident.
SOC 2 (AICPA)
Data integrity is not a database problem. It is a control problem. Anchored in SOC 2, we assess whether the data driving your AI models, business decisions, and regulatory disclosures is reliable, controlled, and defensible at the moment it matters most.
Data Integrity Assessment
Evaluate whether business-critical data is reliable, controlled, and defensible.
SOC 2
The Problem
Your AI is only as reliable as the data it runs on.
Most organisations have some data quality processes. Far fewer have the controls, lineage documentation, access governance, and monitoring infrastructure that make data integrity defensible to auditors, regulators, customers, and their own leadership.
When data integrity fails, the consequences are not isolated to the data team. They surface in AI model outputs that cannot be trusted, regulatory disclosures that cannot be verified, and business decisions built on foundations that do not hold.
Our Data Integrity Assessment evaluates six critical control areas using the SOC 2 framework as its anchor giving findings the rigour and external credibility that internal reviews cannot.
“Data integrity is not a database problem. It is a control problem.”
The distinction that shapes how we assess and what we find.
Anchor framework
SOC 2 (AICPA)
The AICPA’s Service Organisation Control 2 framework is the gold standard for data systems assurance. It provides principled criteria for assessing the quality, accuracy, and control of data systems beyond what internal process documentation alone can achieve.
What We Assess
Six critical control areas.
Each area addresses a distinct dimension of data integrity together, they cover the full picture of whether your data is reliable, controlled, and defensible.
Data Quality & Accuracy
What decisions does this system make or inform? Who is affected and how significantly? Can decisions be challenged, reviewed, or reversed? We assess the scope and consequence of algorithmic decisions and whether adequate correction mechanisms exist.
Processing Controls
Are the processes that transform, aggregate, and route data operating with appropriate authorisation and oversight? We assess processing integrity whether data is handled completely, accurately, and in a timely manner throughout its operational lifecycle.
Data Lineage & Auditability
Can you trace any piece of data from its origin through every transformation to its current state? Lineage and auditability are essential for regulatory disclosure, AI model validation, and incident investigation and most organisations do not have them.
Access & Change Controls
Who has access to data systems and with what level of privilege? Are changes to datasets, models, and pipelines authorised, logged, and reversible? We assess whether access governance and change management are sufficient to prevent integrity failures.
Monitoring & Anomaly Detection
Are data quality issues and integrity anomalies detected in time to prevent downstream harm? We evaluate monitoring infrastructure, alert thresholds, response procedures, and the gap between when an integrity failure occurs and when it is discovered.
Third-Party & Pipeline Integrity
Does the data entering from external vendors, APIs, and data pipelines meet the same integrity standards as internally generated data? Third-party data sources are often the weakest link, we assess whether controls extend meaningfully beyond the organisation’s boundary.
Anchor Framework
SOC 2 (AICPA)
The AICPA’s Service Organisation Control 2 standard provides a principled framework for assessing the security, availability, processing integrity, confidentiality, and privacy of data systems.
Using SOC 2 as the anchor means our findings are not based on internal criteria. They are referenced against a recognised, auditable standard that carries credibility with leadership, clients, and regulators alike.
Privacy – Personal information is collected, used, retained, and disclosed in conformity with privacy commitments.
What You Gain
Data integrity, on purpose.
The outcome is not a data quality report. It is a structured understanding of your data control environment with findings that are actionable, prioritised, and grounded in a recognised framework.
Reliability
Confidence that the data powering your AI and decisions is accurate when it matters most.
Traceability
Clear data lineage and audit trails that support regulatory disclosure and model validation.
Control
Governance structures and access controls that prevent integrity failures before they propagate.
Defensibility
Findings anchored in SOC 2 externally credible and verifiable beyond internal self-assessment.
AI Foundation
A data control environment that supports trustworthy AI models that perform because the data they depend on is sound.
Complete the Picture
Explore our other governance services.
Data integrity is the foundation. AI Act readiness, algorithmic accountability, and production AI engineering complete the lifecycle.
Ready to Begin
Let's assess your data control environment.
Every engagement begins with a scoping consultation. We map your data environment, identify the highest-priority control areas, and design an assessment that produces findings you can act on.