AI Governance

Turn responsible AI ambition into executive control

Gatecroft helps organisations establish the policies, inventories, risk classifications, approval gates and oversight routines needed to deploy AI systems with confidence.

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Governance architecture that works beyond the policy document

Effective AI governance connects strategy, legal accountability, technical controls and operational decision-making. Gatecroft designs the structures that help teams know which AI systems exist, what risks they create, who owns them and how they are approved, monitored and retired.

Services we offer

AI Governance Frameworks

AI Risk Assessments

AI DPIAs

AI Policy Development

AI Inventory Creation

AI Vendor Due Diligence

AI Compliance Reviews

ISO 42001 Readiness

Responsible AI Programmes

AI Regulatory Readiness

Data Protection Awareness Training

Core governance modules

Each module is designed to produce board-readable accountability and team-level operating clarity.

AI Policy & Accountability

Define principles, role ownership, acceptable use, approval gates and escalation duties for AI-enabled operations.

AI Policy & Accountability

Define principles, role ownership, acceptable use, approval gates and escalation duties for AI-enabled operations.

AI Inventory & Use-Case Register

Create a defensible view of systems, data sources, vendors, model types, business owners and deployment contexts.

AI Inventory & Use-Case Register

Create a defensible view of systems, data sources, vendors, model types, business owners and deployment contexts.

Risk Classification

Classify AI use cases by rights impact, data sensitivity, autonomy, security exposure, explainability needs and regulatory risk.

Risk Classification

Classify AI use cases by rights impact, data sensitivity, autonomy, security exposure, explainability needs and regulatory risk.

Model Oversight & Reporting

Establish monitoring, incident response, audit evidence, assurance reporting and lifecycle review routines for AI systems.

Model Oversight & Reporting

Establish monitoring, incident response, audit evidence, assurance reporting and lifecycle review routines for AI systems.

A practical operating model for responsible AI

01 Align
Board and leadership clarity on risk appetite, accountabilities and decision rights.

02 Control
Policies, use-case intake, risk classification, review evidence and approval gates.

03 Assure
Monitoring, incident reporting, periodic review and governance reporting rhythms.

Ready to establish accountable AI governance?

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