Academy / AI GRCS

AI GRCS Training.

Build AI-literate teams that understand how to use, govern, assess and secure AI systems responsibly.

Business context

What this capability solves

Role-based AI Governance, Risk, Compliance & Security training covering responsible AI, governance, security, red teaming and scenario-based practice.

EBP Integra delivery principle

Advice, controls, technology, governance, evidence and operating procedures are designed together so the capability can be sustained after implementation.

Deep-dive capabilities

Capability model

The service can be scoped as a focused engagement or combined into a broader enterprise programme.

AI Awareness & Ethics

Responsible use, acceptable-use boundaries, accountability and human oversight.

Executive & Staff Tracks

Role-specific modules for leaders, business users, risk, legal, technology and security.

AI Governance & Risk

Inventory, classification, impact/risk assessment, controls and monitoring.

AI Security

Prompt injection, data leakage, model/system threats and secure use.

Red Team & Scenarios

Hands-on adversarial testing and governance exercises.

Curriculum / Certification Design

Build structured learning pathways and assessment for enterprise AI roles.

Reference operating model

How the capability fits together

Final scope, control ownership and delivery model are confirmed during discovery.

Governance & Scope

Objectives, applicability, decision rights, owners, policies, risk appetite and acceptance criteria.

Assessment & Design

Evidence collection, gap/risk analysis, target controls, architecture, procedures and prioritized roadmap.

Implementation

Controls, documentation, workflows, integrations, configuration, training and remediation.

Assurance & Operations

Testing, evidence, management reporting, escalation, review cadence and continuous improvement.

Controls & governance

Role-based learning objectives
Current policy/regulatory alignment
Pre/post or practical assessment
Attendance/completion evidence
Facilitator and material quality review
Feedback and improvement cycle

Priority use cases

Enterprise AI adoption
AI policy rollout
ISO 42001 programme
AI security uplift
Agentic AI deployment
Executive capability

Key deliverables

Role-based curriculum
Training materials
Labs/scenarios
Assessment results
Action recommendations
Completion report

Integration considerations

LMS/learning records
HR/role taxonomy
Policy/controls
Awareness communications
Simulation/lab platforms
Certification partner workflow

Implementation

Phased delivery

Each phase produces decision-ready evidence and clear ownership for the next stage.

1. DiscoverConfirm scope, stakeholders, obligations, evidence, dependencies and risk drivers.

2. Assess & DesignAnalyze current state, define target controls and agree the implementation roadmap.

3. ImplementDeploy controls, documents, workflows, training and required technology/integration.

4. Assure & OperateValidate effectiveness, close residual gaps, hand over and establish recurring governance.

Outcome and KPI framework

Knowledge gainRole coverageScenario performancePolicy compliance understandingAI risk awareness