Governance, Risk and Compliance with AI Training Course: Controls, Alerts and Early Warning

Programme Summary

Governance, risk and compliance with AI is the subject of this two-week training course for compliance, risk, governance and internal control managers at managerial level, ending in a monitored controls and early warning plan for one business area. Many organisations test controls once a year, so problems surface late. The programme moves from governance and risk principles to diagnosis of control data, then to automated tests, alerts and early warning models, and finally to a plan a sponsor can approve. Mars Training Centre UK delivers this programme on governance, risk and compliance with AI to mixed-organisation cohorts.

The Business Case

Periodic reviews give a picture that is out of date by the time it is reported. Machine-assisted monitoring can test whole populations of transactions and records and raise alerts early, but it also creates noise, hides weak data and adds models that need their own oversight. Leaders who set clear alert thresholds, evidence standards and ownership gain earlier warning without overloading their teams.

• Control Failure Detection Time: Falls as controls are tested continuously and alerts reach named owners.
• Alert Workload and False Alarms: Reduce as thresholds are calibrated against team capacity and review results.
• Compliance Breach Exposure: Declines as obligations, changes and early indicators are tracked in one view.

Principles and Evidence Base

• ISO 37000 Governance of Organizations: Frames the direction, oversight and accountability principles applied in Sessions 1 and 9 to the monitoring design.
• ISO 31000 Risk Management Guidelines: Supply the risk process and criteria used in Sessions 2 and 7 to rate alerts and indicators.
• ISO 37301 Compliance Management Systems: Set expectations for obligations, controls and review, applied in Sessions 3 to 5.
• ISO/IEC 42001 AI Management System: Sets controls for AI tools, applied in Session 8 to the models that watch the controls.

Intended Participants

• Compliance managers who own obligations registers and monitoring programmes.
• Risk managers who set criteria, indicators and appetite for the organisation.
• Internal control and assurance managers who test whether controls operate.
• Governance leads who report risk and compliance positions to boards and committees.

Typical settings include banking, insurance, energy, healthcare and public bodies.

Learning Outcomes

1. Participants will be able to evaluate which governance, risk and compliance tasks suit AI support.
2. Participants will be able to diagnose weaknesses in control design and control data.
3. Participants will be able to construct automated control tests with sampling and evidence rules.
4. Participants will be able to design alert thresholds that match team capacity.
5. Participants will be able to prioritise early warning indicators by risk and reliability.
6. Participants will be able to justify a monitoring plan and its model safeguards to a sponsor.

Programme Structure

Day 1: Governance, Risk and Compliance with AI and the Control Landscape

Which governance, risk and compliance tasks suit AI, and which stay with people?

• GRC Process Inventory
• Obligation and Control Library
• AI Use Case Long-List
• ISO 37000 Governance Principles Review
• Human Judgement Boundary Sheet

Day 2: Risk Criteria and Appetite

How do appetite and criteria decide what an alert is worth?

• ISO 31000 Risk Process Map
• Risk Appetite Statement Draft
• Risk Criteria Scale
• Key Risk Indicator Long-List
• Risk Owner Assignment Sheet

Day 3: Obligations and Regulatory Change

How are obligations found, mapped and kept current?

• ISO 37301 Obligation Register
• Regulatory Change Intake Log
• Obligation to Control Mapping
• Text Classification Claim Check
• Change Impact Triage Grid

Day 4: Diagnosing Control Weaknesses and Data

Which data can show whether a control really works?

• Control Design Gap Review
• Source System Data Map
• Data Quality Scorecard
• Exception Pattern Analysis
• Control Owner Interview Guide

Day 5: Designing Automated Control Tests

How is a manual check turned into a test a machine can run?

• Test Logic Specification
• Population and Sampling Rule
• Evidence Capture Template
• False Result Review Log
• Test Frequency Matrix

Day 6: Alert Design and Triage

How many alerts can a team act on, and in what order?

• Alert Threshold Calibration
• Alert Volume and Capacity Model
• Alert Triage Playbook
• Escalation Ladder
• Alert Closure Evidence Record

Day 7: Early Warning and Predictive Models

What can a model say about risk before a control fails?

• Leading Indicator Selection
• Early Warning Score Design
• Model Backtest Reading
• Trigger Threshold Table
• Scenario Stress Sheet

Day 8: Governing the Models and Tools

How are the tools that watch the controls themselves controlled?

• ISO/IEC 42001 Control Mapping
• Model Inventory and Owner Register
• Bias and Drift Monitoring Log
• Vendor Claim Assessment
• Model Change Approval Rules

Day 9: Reporting, Assurance and Culture

How is monitored risk reported so that a board can act?

• Risk and Compliance Dashboard Layout
• Board Reporting Pack Outline
• Second Line Challenge Protocol
• Issue Remediation Tracker
• Speak-Up Signal Review

Day 10: Monitored Controls and Early Warning Plan

Do the earlier tools combine into one plan that a sponsor can approve?

• Applying the Session 1 Process Inventory
• Testing the Session 4 Data Quality Scorecard
• Checking the Session 6 Capacity Model
• Rehearsing the Session 8 Approval Rules
• Completing the Monitored Controls Plan

Methodology

The programme uses a structured exercise built on prepared files for two business areas, one in a bank and one in a utility, each with obligations lists, control descriptions, sample transaction extracts and alert logs. Participants work in small groups, build tests and alert rules from the files, and defend their choices to peers acting as sponsors and auditors.

• Suggested activity: Turning a manual approval check into a written automated test.
• Suggested activity: Setting an alert threshold and judging its workload against a supplied team size.
• Suggested activity: Reading a model backtest and deciding which indicators to keep.

Capstone Output

Each participant completes a monitored controls and early warning plan for one business area. It holds an obligation and control map, a set of automated tests, alert rules with capacity limits, an indicator list, a model safeguard note and a board reporting outline. The plan is used at work to brief a sponsor and to agree duties with control owners.

Frequently Asked Questions

Is a course on governance, risk and compliance with AI suitable for managers new to data tools?

Yes. Governance, risk and compliance with AI is taught through prepared files, templates and worked examples. It assumes experience in compliance, risk, control or governance work, but no coding and no earlier analytics training.

Which standards does governance, risk and compliance with AI rest on?

Governance, risk and compliance with AI rests on ISO 37000 for governance, ISO 31000 for risk, ISO 37301 for compliance management systems and ISO/IEC 42001 for the control of AI tools.

How is the monitoring plan from governance, risk and compliance with AI used at work?

You use the monitored controls plan to brief your sponsor, agree alert duties with control owners and set the rules for escalation and model change.

How does governance, risk and compliance with AI differ from an internal audit course?

Governance, risk and compliance with AI concentrates on continuous monitoring, alerts and early warning across the organisation, while an internal audit course concentrates on independent engagements and audit reporting.