AI-Driven GRC Automation and Monitoring Training Course – Amsterdam, 5–9 Apr 2027

AI-Driven GRC Automation and Monitoring is a 1-week training course for GRC managers, internal auditors and compliance officers who build a Continuous Control Monitoring Rulebook for their function. Control owners face growing volumes of evidence, faster system change and closer scrutiny of how machine learning models are themselves controlled. Jupiter Prime Training offers this course on AI-Driven GRC Automation and Monitoring for practitioners who test controls every quarter.

Why AI-Driven GRC Automation and Monitoring Matters Now

Three forces push assurance teams towards automation: cloud and ERP platforms that change weekly, boards asking for near real-time assurance rather than annual sampling, and audit committees questioning how AI models are tested before they are trusted. Done well, automation surfaces exceptions early; done badly, it creates noisy alerts and evidence nobody can reproduce. The course aligns its methods with ISO/IEC 42001, the AI management system standard, and the NIST AI Risk Management Framework (NIST AI RMF).

Who Gains Most from AI-Driven GRC Automation and Monitoring

• Control testing leads who design test scripts, sample logic and exception thresholds
• Internal audit specialists who plan continuous auditing and data analytics coverage
• Compliance monitoring officers who track obligations, breaches and remediation status
• Risk analysts who maintain key risk indicators and loss event data
• Technology risk officers who oversee access, change and model controls on core platforms

A second and third line pair, such as a compliance unit and its internal audit counterpart, gains most when they attend together and agree shared rules for automated evidence.

AI-Driven GRC Automation and Monitoring Capabilities

Participants replace manual sampling with machine-run tests whose logic and thresholds can be traced.

• Map a control library to machine-testable attributes and data sources — control owners
• Design automated test scripts with clear pass, fail and exception logic — audit committee
• Calibrate anomaly detection thresholds to reduce false positives — monitoring analysts
• Build predictive key risk indicators from incident and loss history — chief risk officer
• Specify evidence retention and reproducibility rules for automated tests — external assurance providers
• Assess model controls against ISO/IEC 42001 and NIST AI RMF — AI oversight committee

Key AI-Driven GRC Automation and Monitoring Terms

• Continuous control monitoring — automated, frequent testing of control operation using system data rather than periodic manual samples.
• Control library — a structured catalogue of controls with owners, objectives and the risks each mitigates.
• Automated evidence collection — scheduled extraction of logs, configurations and approvals that prove a control operated, stored with timestamps.
• Exception triage — the process of ranking, investigating and closing flagged control failures by severity and likely root cause.
• Predictive key risk indicator — a metric fed by a model that signals rising risk before a loss or breach occurs.
• Full-population testing — examining every transaction or record in scope instead of a sample, made practical by automation.

AI-Driven GRC Automation and Monitoring Day by Day

Day 1: Assurance Pressures and the Automation Case

• Three Lines Model Roles — Who Owns Automated Control Tests
• Control Library Rationalisation — Which Controls Are Machine-Testable First
• Assurance Maturity Assessment — Where Manual Sampling Still Fits
• Data Source Inventory — Which ERP and Cloud Logs Feed Tests
• Automation Benefit Scoring — Ranking Candidate Controls by Effort and Risk

Day 2: Building Automated Control Tests

• Test Script Logic Design — Defining Pass, Fail and Exception Rules
• Full-Population Testing Techniques — Replacing Samples for Payments and Access
• Segregation of Duties Conflict Detection — Prioritising Toxic Access Combinations
• Automated Evidence Collection Pipelines — Timestamped Proof for Reviewers
• Robotic Process Automation Bots — When Scripts Beat Platform Connectors

Day 3: Machine Learning for Monitoring and Prediction

• Anomaly Detection on Journal Entries — Flagging Unusual Postings for Review
• Natural Language Processing of Policies — Linking Obligations to Controls
• Predictive Key Risk Indicators — Signalling Breaches Before Losses Occur
• Threshold Calibration and False Positive Rates — Setting Alert Volumes
• Exception Triage Queues — Routing Failures by Severity and Owner

Day 4: Controlling the Automation Itself

• ISO/IEC 42001 Clauses for Monitoring Models — Documented AI Controls
• NIST AI RMF Map, Measure, Manage Functions — Model Risk Treatment
• Model Drift and Explainability Checks — Defending Scores to Auditors
• Reproducibility and Evidence Retention Rules — Satisfying External Assurance
• Monitoring Dashboards for Committees — Reporting Exceptions and Trends

Day 5: Practising the Continuous Control Monitoring Rulebook

• Banking Payments Case — Applying Full-Population and Duties Tests
• Manufacturing Procurement Case — Applying Anomaly Detection to Vendor Spend
• Calibrated Alert Scenario — Applying Thresholds and Triage Routing
• Peer Challenge Panel — Testing Rules Against Second Line Objections
• Continuous Control Monitoring Rulebook — Final Tests, Owners and Thresholds

Applying AI-Driven GRC Automation and Monitoring After the Course

• Run a full-population test on one high-risk payment or access control and compare its exceptions with the last manual sample.
• Recalibrate the alert threshold of one existing monitoring rule using a month of false positive data and document the change.

AI-Driven GRC Automation and Monitoring Questions (FAQ)

What is AI-Driven GRC Automation and Monitoring and why does it matter now?

It is the use of scripts and machine learning to test controls continuously and predict risk. It matters now because systems change faster than annual sampling can follow, and boards expect earlier warning of control failures.

Which roles gain most from AI-Driven GRC Automation and Monitoring?

Control testing leads, internal audit specialists, compliance monitoring officers, risk analysts and technology risk officers gain most.

How does AI-Driven GRC Automation and Monitoring differ from an AI governance course?

It is hands-on control testing rather than policy design. A governance course sets principles and oversight structures for AI, while this subject builds the test scripts, thresholds and evidence pipelines that monitor everyday controls.

What do you bring back from AI-Driven GRC Automation and Monitoring?

You bring back a rulebook of automated control tests with owners, data sources and calibrated thresholds.

AI-Driven GRC Automation and Monitoring: The Way Forward

The Continuous Control Monitoring Rulebook first informs which priority controls move from manual sampling to automated testing. Control owners, internal auditors and compliance monitoring officers use it to agree thresholds, evidence rules and triage routes. After its first quarter in use, revisit the false positive rates and model drift results.