Service / AI System Advisory & Development

AI Regulatory Compliance & Audit

Assess AI systems and governance against applicable requirements, internal policies and recognized control frameworks, then produce evidence-led remediation.

Business context

What this capability solves

Organizations need to show not only that AI policies exist but that controls operate and evidence can support internal audit, regulators, customers and boards.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Obligation Mapping

Map legal, sector, contractual and internal requirements to AI controls and assets.

Control Assessment

Design and operating-effectiveness testing across governance, data, model, security and oversight.

Evidence Review

Architecture, logs, approvals, evaluations, impact assessments, incidents and monitoring records.

Gap Classification

Rate compliance gaps by risk, impact, urgency and control dependency.

Remediation Governance

Action ownership, target date, evidence requirements and exception handling.

Audit Pack

Management summary, control matrix, evidence index and technical annex.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Business & Governance

Business objectives, accountable owners, risk appetite, policy, use-case portfolio and investment priorities.

AI Engineering Lifecycle

Data, model, prompt/RAG, agent, evaluation, release, monitoring and retirement controls.

Trust & Assurance

Risk/impact assessment, security, privacy, explainability, human oversight, testing and evidence.

Enterprise Operations

Integration, observability, incident handling, change governance, model/agent lifecycle and continuous improvement.

Controls & governance

Named business and technical owner
Use-case risk classification and approval gates
Data provenance, minimization and access control
Human accountability for high-impact outcomes
Security and privacy-by-design controls
Versioned model/prompt/agent configuration
Pre-release evaluation and red-team gates
Continuous monitoring, incident and change control
Audit-ready evidence and management reporting

Priority use cases

Internal AI audit
Regulatory readiness
Customer assurance
ISO/IEC 42001 programme
High-risk AI launch
Post-incident compliance review

Key deliverables

Obligation-control matrix
AI audit plan
Test procedures
Findings register
Evidence index
Remediation plan
Executive/audit report

Integration considerations

Enterprise IAM and workload identity
Data lake/warehouse and vector/RAG platforms
Model/API providers and private models
Application/API integration layer
MLOps/LLMOps/AgentOps and observability
SIEM/SOAR and security tooling
GRC, privacy and evidence repositories
ITSM/BPM and business workflow systems

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. AssessInventory use cases, systems, stakeholders, data, risks, maturity and constraints.

2. DesignDefine target architecture, governance, controls, delivery backlog and acceptance criteria.

3. BuildDevelop or configure AI capabilities, integrations, controls, evaluation and operating procedures.

4. AssureTest quality, safety, security, privacy, explainability and business acceptance before release.

5. OperateMonitor outcomes, drift, incidents, changes, cost, risk and control effectiveness through BAU governance.

Outcome and KPI framework

Control pass rateEvidence completenessHigh-risk gap agingRepeat audit finding rateRemediation closureAssessment coverage