DOT’s Shadow AI Detection engagement delivers a complete inventory of all AI tools in operation within your environment within two weeks, accompanied by a risk-classified register and a governance remediation plan.
The Data Liquidity Score is derived from a structured assessment across six dimensions: system connectivity, data quality, accessibility for AI model consumption, governance maturity, security controls, and lineage traceability. The initial score is produced during the Data Liquidity Audit engagement (4–6 weeks). For clients on ongoing retainers, the score is recalculated quarterly or following significant architectural changes.
In most cases, yes. The presence of a data warehouse does not in itself indicate AI readiness. Many enterprise data warehouses are architected for reporting and business intelligence workloads, not for the real-time, high-velocity consumption patterns demanded by AI models. Our audit specifically evaluates fitness-for-AI-purpose, which is a distinct assessment criterion.
The risks associated with Shadow AI span regulatory compliance (GDPR, EU AI Act), intellectual property exposure, data security, and model accuracy. DOT’s Shadow AI Detection combines network traffic analysis, endpoint activity review, IT system audit, and structured interviews to achieve comprehensive coverage. Our engagements consistently identify tools that are unknown to both IT leadership and the CISO.
Remediation timelines are proportional to architectural complexity. Organisations with a score in the 40–60% range typically achieve AI-ready status within eight to twelve weeks of engaging DOT’s remediation programme. The majority of our clients reach a score above 80% within three months, at which point AI model deployment can proceed with confidence.
DOT’s engagement model spans strategy through to implementation. We architect and oversee data migration activities, design and build clean data pipelines, and coordinate with your technology vendors throughout the integration process. We operate in a technology-agnostic manner, working across all major cloud platforms and enterprise data systems.
DOT designs Data Governance Frameworks to be complementary to , and fully consistent with , existing regulatory compliance structures. We map data ownership and quality controls to your GDPR Records of Processing Activities (ROPA), align access policies to your ISO 27001 information asset register, and ensure all AI-specific data processing activities are documented and compliant.
