Mastering AI-Driven SOX Compliance for Future-Proof Audits
You’re under pressure. Regulatory deadlines loom. Audit trails are growing more complex. Manual processes are failing to keep up with AI systems that evolve faster than compliance frameworks can adapt. You need more than a checklist - you need a strategy that’s intelligent, repeatable, and defensible. The cost of getting this wrong is high: regulatory fines, board scrutiny, career risk. But the opportunity is greater. Organisations that master AI-driven compliance are not just avoiding penalties - they’re earning trust, accelerating audits, and positioning themselves as innovation leaders. Mastering AI-Driven SOX Compliance for Future-Proof Audits is not another theory-heavy lecture. It’s a battle-tested, step-by-step system that transforms how you design, implement, and validate SOX controls in AI-powered environments. This course bridges the gap between outdated compliance models and tomorrow’s AI-augmented audit landscape. By the end, you’ll have built a fully documented, board-ready framework to demonstrate AI accountability, with clear audit trails that withstand even the most rigorous external scrutiny - all in under 30 days. Take Sarah Kim, Senior Internal Auditor at a Fortune 500 fintech. After completing this course, she redesigned her company’s AI-driven transaction monitoring controls, reducing manual review time by 72% and receiving formal recognition from the CFO for “elevating audit maturity to enterprise-ready levels.” You’re not just learning compliance - you’re future-proofing your role, your reputation, and your organisation’s governance. Here’s how this course is structured to help you get there.Course Format & Delivery Details Learn On Your Terms - No Constraints
This course is self-paced, with on-demand access from any device, anywhere in the world. Enrol once, and gain lifetime access to all materials, including every future update at no extra cost. You control when, where, and how fast you learn. Most professionals complete the core implementation in 4 to 6 weeks, dedicating 4–5 hours per week. Many apply the first control framework within the first 10 days - delivering immediate ROI to their teams. 24/7 Access, Mobile-Optimised, Ready for Your Workflow
Access all materials anytime, on any device. Whether you’re preparing for an audit on your tablet during travel or refining control logic on your phone between meetings, the course adapts to your schedule - not the other way around. Expert Guidance with Real-World Clarity
You are not learning in isolation. This course includes structured instructor insights, curated FAQs, and proven implementation templates developed by compliance leaders with decades of combined SOX and AI governance experience. Every concept is validated against real regulatory outcomes and audit findings. Certificate of Completion: A Globally Recognised Credential
Upon finishing, you’ll earn a Certificate of Completion issued by The Art of Service - a name trusted by over 120,000 professionals across 140 countries. This credential signals not just completion, but mastery of next-generation compliance standards and is increasingly referenced in career advancement paths, promotions, and consulting engagements. No Hidden Fees, No Surprises
The price you see is the price you pay. There are no upsells, no subscription traps, and no hidden charges. One upfront investment covers full access, all updates, and your permanent certificate. - Secure payment accepted via Visa
- Mastercard
- PayPal
Zero-Risk Enrollment: Satisfied or Refunded
We stand behind this course with a strong satisfaction guarantee. If you complete the core framework and find it doesn’t deliver actionable value, you’re covered by our refund promise. No risk. No questions. No regrets. Instant Confirmation, Seamless Onboarding
After enrolment, you’ll receive a confirmation email. Your access details will be delivered separately once your course materials are prepared - ensuring every resource is up to date and ready for impact. This Works Even If…
…you’ve never worked with AI systems before. The curriculum starts with foundational clarity, not assumed knowledge. You’ll learn to speak the language of machine learning accountability, translate AI risks into control objectives, and apply pre-built templates that work in both legacy and modern environments. …your organisation moves slowly on compliance innovation. This course gives you the tools to lead from any level. Past learners include staff auditors who became SOX team leads, compliance analysts who drove AI governance policy, and consultants who doubled their client retention by offering AI-embedded control assessments. You don’t need permission to be ready. You just need the right framework. This is it.
Module 1: Foundations of AI in Regulatory Compliance - Understanding AI systems in financial governance
- Key differences between traditional and AI-driven SOX controls
- The role of machine learning in transaction processing and reporting
- Regulatory expectations for AI transparency and auditability
- Defining 'black box' risk in compliance contexts
- How AI alters the control environment under SOX 404
- The impact of AI-generated data on audit trails
- Common misconceptions about AI and compliance
- Historical failures in AI governance and lessons learned
- The evolution of audit standards in response to automation
Module 2: Regulatory Frameworks and AI Accountability - Sarbanes-Oxley Section 302 and 404 in the age of AI
- Mapping AI activities to SOX compliance requirements
- How regulators evaluate AI transparency in filings
- The role of internal controls over financial reporting (ICFR)
- Aligning AI models with COSO and COBIT frameworks
- Regulatory precedents for AI misclassification and bias
- Integrating AI oversight into board-level reporting
- Documentation requirements for AI-enabled processes
- Chain of custody for model training, input, and output
- Global perspectives on AI and financial regulation
Module 3: AI Risk Assessment for SOX 404 - Identifying AI-impacted financial reporting areas
- Performing a SOX-relevant AI risk inventory
- Scoring AI risk using quantitative and qualitative factors
- Determining materiality thresholds for AI processes
- Mapping AI functions to financial statement line items
- Analysing data provenance and model dependency
- Assessing model drift and its audit implications
- Defining critical AI controls using RACI matrices
- Integrating AI risk into entity-level controls
- Creating a dynamic AI risk register with update protocols
Module 4: Designing AI-Aware Control Objectives - Translating SOX requirements into AI-specific controls
- Control design for automated decision-making systems
- Ensuring completeness and accuracy in AI outputs
- Designing controls for real-time anomaly detection
- Segregation of duties in AI development and deployment
- Version control for machine learning models
- Input validation and data sanitisation protocols
- Output reconciliation and exception handling
- Control objectives for model retraining cycles
- Designing fallback procedures for AI failure
Module 5: Implementing Automated Monitoring Controls - Selecting monitoring tools for AI model performance
- Designing dashboards for continuous SOX monitoring
- Setting thresholds for automated alerts and escalation
- Integrating AI monitoring with GRC platforms
- Logging model behaviour and data flow changes
- Automating control effectiveness testing
- Using AI to monitor other AI systems (self-auditing)
- Configuring alert fatigue prevention mechanisms
- Creating audit trails for model decisions
- Enabling real-time exception reporting to control owners
Module 6: Documentation Standards for AI-Powered Processes - Documentation requirements under SOX 404
- Creating AI control narratives and process flows
- Standardising documentation across model lifecycles
- Developing model cards for transparency reporting
- Storing documentation for audit readiness
- Versioning control documentation with model updates
- Creating clear control ownership records
- Documenting assumptions in AI model design
- Detailing model limitations and edge cases
- Using templates for consistent AI control documentation
Module 7: Testing AI Control Effectiveness - Designing test plans for AI-integrated controls
- Selecting appropriate sample sizes and testing frequency
- Testing model accuracy and consistency over time
- Validating control design through walkthroughs
- Performing re-performance of AI outputs
- Assessing model performance against benchmarks
- Detecting bias and fairness issues in decision outputs
- Testing fallback and override mechanisms
- Analysing false positives and false negatives
- Documenting test results and exceptions
Module 8: Managing Model Drift and Data Decay - Understanding model drift in financial applications
- Monitoring for concept and data drift
- Setting thresholds for model re-evaluation
- Designing automated drift detection alerts
- Retesting controls after significant data shifts
- Updating model training data without compromising controls
- Documenting drift response procedures
- Ensuring model stability during market volatility
- Linking drift detection to change management
- Creating audit evidence of drift mitigation
Module 9: AI Governance and Organisational Controls - Establishing an AI governance committee
- Defining roles: data scientists, auditors, compliance officers
- Creating AI model development standards
- Implementing model review and approval workflows
- Conducting pre-deployment risk assessments
- Setting model retirement criteria
- Integrating AI governance into SOX committees
- Training control owners on AI responsibilities
- Communicating AI risks to executive leadership
- Aligning AI governance with enterprise risk management
Module 10: Change Management in AI Systems - Tracking changes to AI models, data, and environment
- Implementing version control for machine learning
- Using Git for model and code traceability
- Documenting parameter tuning and hyperparameter changes
- Reviewing model updates for SOX impact
- Testing controls after model deployment
- Managing emergency model fixes and patches
- Ensuring rollback capabilities are in place
- Communicating changes to audit teams
- Archiving historical model versions for inspection
Module 11: Audit Preparedness and Evidence Collection - Preparing for external audit of AI systems
- Compiling evidence packs for AI controls
- Selecting samples for audit testing
- Responding to auditor inquiries about AI logic
- Demonstrating model interpretability
- Providing data lineage reports
- Creating model performance scorecards
- Organising documentation by control objective
- Conducting internal mock audits
- Defending AI control effectiveness under scrutiny
Module 12: Explainability and Interpretability in SOX Audits - Why explainability matters in financial controls
- Using SHAP, LIME, and other interpretability tools
- Generating human-readable explanations for decisions
- Balancing model complexity with auditability
- Documenting model decision logic for auditors
- Creating decision trees from black-box models
- Presenting trade-offs between accuracy and transparency
- Using surrogate models for explanation
- Training auditors on AI interpretability outputs
- Storing explanation logs for future review
Module 13: AI in Fraud Detection and Anomaly Monitoring - Using AI for real-time fraud detection
- Designing controls around AI-driven anomaly alerts
- Validating the performance of fraud models
- Setting thresholds to minimise false positives
- Investigating flagged transactions
- Integrating AI alerts with case management systems
- Ensuring ethical use of behavioural AI
- Documenting model fairness in fraud detection
- Testing alert escalation and response workflows
- Reporting fraud detection outcomes to audit committees
Module 14: Data Integrity in AI-Driven SOX Processes - Validating data inputs to AI models
- Preventing data tampering and manipulation
- Ensuring referential integrity across datasets
- Using hashing and digital signatures for data tracking
- Monitoring data pipeline health
- Detecting duplicate or missing data entries
- Validating data transformations before model input
- Controlling access to training and operational data
- Documenting data mapping and lineage
- Using blockchain-inspired ledgers for critical data
Module 15: Third-Party and Vendor AI Solutions - Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Understanding AI systems in financial governance
- Key differences between traditional and AI-driven SOX controls
- The role of machine learning in transaction processing and reporting
- Regulatory expectations for AI transparency and auditability
- Defining 'black box' risk in compliance contexts
- How AI alters the control environment under SOX 404
- The impact of AI-generated data on audit trails
- Common misconceptions about AI and compliance
- Historical failures in AI governance and lessons learned
- The evolution of audit standards in response to automation
Module 2: Regulatory Frameworks and AI Accountability - Sarbanes-Oxley Section 302 and 404 in the age of AI
- Mapping AI activities to SOX compliance requirements
- How regulators evaluate AI transparency in filings
- The role of internal controls over financial reporting (ICFR)
- Aligning AI models with COSO and COBIT frameworks
- Regulatory precedents for AI misclassification and bias
- Integrating AI oversight into board-level reporting
- Documentation requirements for AI-enabled processes
- Chain of custody for model training, input, and output
- Global perspectives on AI and financial regulation
Module 3: AI Risk Assessment for SOX 404 - Identifying AI-impacted financial reporting areas
- Performing a SOX-relevant AI risk inventory
- Scoring AI risk using quantitative and qualitative factors
- Determining materiality thresholds for AI processes
- Mapping AI functions to financial statement line items
- Analysing data provenance and model dependency
- Assessing model drift and its audit implications
- Defining critical AI controls using RACI matrices
- Integrating AI risk into entity-level controls
- Creating a dynamic AI risk register with update protocols
Module 4: Designing AI-Aware Control Objectives - Translating SOX requirements into AI-specific controls
- Control design for automated decision-making systems
- Ensuring completeness and accuracy in AI outputs
- Designing controls for real-time anomaly detection
- Segregation of duties in AI development and deployment
- Version control for machine learning models
- Input validation and data sanitisation protocols
- Output reconciliation and exception handling
- Control objectives for model retraining cycles
- Designing fallback procedures for AI failure
Module 5: Implementing Automated Monitoring Controls - Selecting monitoring tools for AI model performance
- Designing dashboards for continuous SOX monitoring
- Setting thresholds for automated alerts and escalation
- Integrating AI monitoring with GRC platforms
- Logging model behaviour and data flow changes
- Automating control effectiveness testing
- Using AI to monitor other AI systems (self-auditing)
- Configuring alert fatigue prevention mechanisms
- Creating audit trails for model decisions
- Enabling real-time exception reporting to control owners
Module 6: Documentation Standards for AI-Powered Processes - Documentation requirements under SOX 404
- Creating AI control narratives and process flows
- Standardising documentation across model lifecycles
- Developing model cards for transparency reporting
- Storing documentation for audit readiness
- Versioning control documentation with model updates
- Creating clear control ownership records
- Documenting assumptions in AI model design
- Detailing model limitations and edge cases
- Using templates for consistent AI control documentation
Module 7: Testing AI Control Effectiveness - Designing test plans for AI-integrated controls
- Selecting appropriate sample sizes and testing frequency
- Testing model accuracy and consistency over time
- Validating control design through walkthroughs
- Performing re-performance of AI outputs
- Assessing model performance against benchmarks
- Detecting bias and fairness issues in decision outputs
- Testing fallback and override mechanisms
- Analysing false positives and false negatives
- Documenting test results and exceptions
Module 8: Managing Model Drift and Data Decay - Understanding model drift in financial applications
- Monitoring for concept and data drift
- Setting thresholds for model re-evaluation
- Designing automated drift detection alerts
- Retesting controls after significant data shifts
- Updating model training data without compromising controls
- Documenting drift response procedures
- Ensuring model stability during market volatility
- Linking drift detection to change management
- Creating audit evidence of drift mitigation
Module 9: AI Governance and Organisational Controls - Establishing an AI governance committee
- Defining roles: data scientists, auditors, compliance officers
- Creating AI model development standards
- Implementing model review and approval workflows
- Conducting pre-deployment risk assessments
- Setting model retirement criteria
- Integrating AI governance into SOX committees
- Training control owners on AI responsibilities
- Communicating AI risks to executive leadership
- Aligning AI governance with enterprise risk management
Module 10: Change Management in AI Systems - Tracking changes to AI models, data, and environment
- Implementing version control for machine learning
- Using Git for model and code traceability
- Documenting parameter tuning and hyperparameter changes
- Reviewing model updates for SOX impact
- Testing controls after model deployment
- Managing emergency model fixes and patches
- Ensuring rollback capabilities are in place
- Communicating changes to audit teams
- Archiving historical model versions for inspection
Module 11: Audit Preparedness and Evidence Collection - Preparing for external audit of AI systems
- Compiling evidence packs for AI controls
- Selecting samples for audit testing
- Responding to auditor inquiries about AI logic
- Demonstrating model interpretability
- Providing data lineage reports
- Creating model performance scorecards
- Organising documentation by control objective
- Conducting internal mock audits
- Defending AI control effectiveness under scrutiny
Module 12: Explainability and Interpretability in SOX Audits - Why explainability matters in financial controls
- Using SHAP, LIME, and other interpretability tools
- Generating human-readable explanations for decisions
- Balancing model complexity with auditability
- Documenting model decision logic for auditors
- Creating decision trees from black-box models
- Presenting trade-offs between accuracy and transparency
- Using surrogate models for explanation
- Training auditors on AI interpretability outputs
- Storing explanation logs for future review
Module 13: AI in Fraud Detection and Anomaly Monitoring - Using AI for real-time fraud detection
- Designing controls around AI-driven anomaly alerts
- Validating the performance of fraud models
- Setting thresholds to minimise false positives
- Investigating flagged transactions
- Integrating AI alerts with case management systems
- Ensuring ethical use of behavioural AI
- Documenting model fairness in fraud detection
- Testing alert escalation and response workflows
- Reporting fraud detection outcomes to audit committees
Module 14: Data Integrity in AI-Driven SOX Processes - Validating data inputs to AI models
- Preventing data tampering and manipulation
- Ensuring referential integrity across datasets
- Using hashing and digital signatures for data tracking
- Monitoring data pipeline health
- Detecting duplicate or missing data entries
- Validating data transformations before model input
- Controlling access to training and operational data
- Documenting data mapping and lineage
- Using blockchain-inspired ledgers for critical data
Module 15: Third-Party and Vendor AI Solutions - Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Identifying AI-impacted financial reporting areas
- Performing a SOX-relevant AI risk inventory
- Scoring AI risk using quantitative and qualitative factors
- Determining materiality thresholds for AI processes
- Mapping AI functions to financial statement line items
- Analysing data provenance and model dependency
- Assessing model drift and its audit implications
- Defining critical AI controls using RACI matrices
- Integrating AI risk into entity-level controls
- Creating a dynamic AI risk register with update protocols
Module 4: Designing AI-Aware Control Objectives - Translating SOX requirements into AI-specific controls
- Control design for automated decision-making systems
- Ensuring completeness and accuracy in AI outputs
- Designing controls for real-time anomaly detection
- Segregation of duties in AI development and deployment
- Version control for machine learning models
- Input validation and data sanitisation protocols
- Output reconciliation and exception handling
- Control objectives for model retraining cycles
- Designing fallback procedures for AI failure
Module 5: Implementing Automated Monitoring Controls - Selecting monitoring tools for AI model performance
- Designing dashboards for continuous SOX monitoring
- Setting thresholds for automated alerts and escalation
- Integrating AI monitoring with GRC platforms
- Logging model behaviour and data flow changes
- Automating control effectiveness testing
- Using AI to monitor other AI systems (self-auditing)
- Configuring alert fatigue prevention mechanisms
- Creating audit trails for model decisions
- Enabling real-time exception reporting to control owners
Module 6: Documentation Standards for AI-Powered Processes - Documentation requirements under SOX 404
- Creating AI control narratives and process flows
- Standardising documentation across model lifecycles
- Developing model cards for transparency reporting
- Storing documentation for audit readiness
- Versioning control documentation with model updates
- Creating clear control ownership records
- Documenting assumptions in AI model design
- Detailing model limitations and edge cases
- Using templates for consistent AI control documentation
Module 7: Testing AI Control Effectiveness - Designing test plans for AI-integrated controls
- Selecting appropriate sample sizes and testing frequency
- Testing model accuracy and consistency over time
- Validating control design through walkthroughs
- Performing re-performance of AI outputs
- Assessing model performance against benchmarks
- Detecting bias and fairness issues in decision outputs
- Testing fallback and override mechanisms
- Analysing false positives and false negatives
- Documenting test results and exceptions
Module 8: Managing Model Drift and Data Decay - Understanding model drift in financial applications
- Monitoring for concept and data drift
- Setting thresholds for model re-evaluation
- Designing automated drift detection alerts
- Retesting controls after significant data shifts
- Updating model training data without compromising controls
- Documenting drift response procedures
- Ensuring model stability during market volatility
- Linking drift detection to change management
- Creating audit evidence of drift mitigation
Module 9: AI Governance and Organisational Controls - Establishing an AI governance committee
- Defining roles: data scientists, auditors, compliance officers
- Creating AI model development standards
- Implementing model review and approval workflows
- Conducting pre-deployment risk assessments
- Setting model retirement criteria
- Integrating AI governance into SOX committees
- Training control owners on AI responsibilities
- Communicating AI risks to executive leadership
- Aligning AI governance with enterprise risk management
Module 10: Change Management in AI Systems - Tracking changes to AI models, data, and environment
- Implementing version control for machine learning
- Using Git for model and code traceability
- Documenting parameter tuning and hyperparameter changes
- Reviewing model updates for SOX impact
- Testing controls after model deployment
- Managing emergency model fixes and patches
- Ensuring rollback capabilities are in place
- Communicating changes to audit teams
- Archiving historical model versions for inspection
Module 11: Audit Preparedness and Evidence Collection - Preparing for external audit of AI systems
- Compiling evidence packs for AI controls
- Selecting samples for audit testing
- Responding to auditor inquiries about AI logic
- Demonstrating model interpretability
- Providing data lineage reports
- Creating model performance scorecards
- Organising documentation by control objective
- Conducting internal mock audits
- Defending AI control effectiveness under scrutiny
Module 12: Explainability and Interpretability in SOX Audits - Why explainability matters in financial controls
- Using SHAP, LIME, and other interpretability tools
- Generating human-readable explanations for decisions
- Balancing model complexity with auditability
- Documenting model decision logic for auditors
- Creating decision trees from black-box models
- Presenting trade-offs between accuracy and transparency
- Using surrogate models for explanation
- Training auditors on AI interpretability outputs
- Storing explanation logs for future review
Module 13: AI in Fraud Detection and Anomaly Monitoring - Using AI for real-time fraud detection
- Designing controls around AI-driven anomaly alerts
- Validating the performance of fraud models
- Setting thresholds to minimise false positives
- Investigating flagged transactions
- Integrating AI alerts with case management systems
- Ensuring ethical use of behavioural AI
- Documenting model fairness in fraud detection
- Testing alert escalation and response workflows
- Reporting fraud detection outcomes to audit committees
Module 14: Data Integrity in AI-Driven SOX Processes - Validating data inputs to AI models
- Preventing data tampering and manipulation
- Ensuring referential integrity across datasets
- Using hashing and digital signatures for data tracking
- Monitoring data pipeline health
- Detecting duplicate or missing data entries
- Validating data transformations before model input
- Controlling access to training and operational data
- Documenting data mapping and lineage
- Using blockchain-inspired ledgers for critical data
Module 15: Third-Party and Vendor AI Solutions - Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Selecting monitoring tools for AI model performance
- Designing dashboards for continuous SOX monitoring
- Setting thresholds for automated alerts and escalation
- Integrating AI monitoring with GRC platforms
- Logging model behaviour and data flow changes
- Automating control effectiveness testing
- Using AI to monitor other AI systems (self-auditing)
- Configuring alert fatigue prevention mechanisms
- Creating audit trails for model decisions
- Enabling real-time exception reporting to control owners
Module 6: Documentation Standards for AI-Powered Processes - Documentation requirements under SOX 404
- Creating AI control narratives and process flows
- Standardising documentation across model lifecycles
- Developing model cards for transparency reporting
- Storing documentation for audit readiness
- Versioning control documentation with model updates
- Creating clear control ownership records
- Documenting assumptions in AI model design
- Detailing model limitations and edge cases
- Using templates for consistent AI control documentation
Module 7: Testing AI Control Effectiveness - Designing test plans for AI-integrated controls
- Selecting appropriate sample sizes and testing frequency
- Testing model accuracy and consistency over time
- Validating control design through walkthroughs
- Performing re-performance of AI outputs
- Assessing model performance against benchmarks
- Detecting bias and fairness issues in decision outputs
- Testing fallback and override mechanisms
- Analysing false positives and false negatives
- Documenting test results and exceptions
Module 8: Managing Model Drift and Data Decay - Understanding model drift in financial applications
- Monitoring for concept and data drift
- Setting thresholds for model re-evaluation
- Designing automated drift detection alerts
- Retesting controls after significant data shifts
- Updating model training data without compromising controls
- Documenting drift response procedures
- Ensuring model stability during market volatility
- Linking drift detection to change management
- Creating audit evidence of drift mitigation
Module 9: AI Governance and Organisational Controls - Establishing an AI governance committee
- Defining roles: data scientists, auditors, compliance officers
- Creating AI model development standards
- Implementing model review and approval workflows
- Conducting pre-deployment risk assessments
- Setting model retirement criteria
- Integrating AI governance into SOX committees
- Training control owners on AI responsibilities
- Communicating AI risks to executive leadership
- Aligning AI governance with enterprise risk management
Module 10: Change Management in AI Systems - Tracking changes to AI models, data, and environment
- Implementing version control for machine learning
- Using Git for model and code traceability
- Documenting parameter tuning and hyperparameter changes
- Reviewing model updates for SOX impact
- Testing controls after model deployment
- Managing emergency model fixes and patches
- Ensuring rollback capabilities are in place
- Communicating changes to audit teams
- Archiving historical model versions for inspection
Module 11: Audit Preparedness and Evidence Collection - Preparing for external audit of AI systems
- Compiling evidence packs for AI controls
- Selecting samples for audit testing
- Responding to auditor inquiries about AI logic
- Demonstrating model interpretability
- Providing data lineage reports
- Creating model performance scorecards
- Organising documentation by control objective
- Conducting internal mock audits
- Defending AI control effectiveness under scrutiny
Module 12: Explainability and Interpretability in SOX Audits - Why explainability matters in financial controls
- Using SHAP, LIME, and other interpretability tools
- Generating human-readable explanations for decisions
- Balancing model complexity with auditability
- Documenting model decision logic for auditors
- Creating decision trees from black-box models
- Presenting trade-offs between accuracy and transparency
- Using surrogate models for explanation
- Training auditors on AI interpretability outputs
- Storing explanation logs for future review
Module 13: AI in Fraud Detection and Anomaly Monitoring - Using AI for real-time fraud detection
- Designing controls around AI-driven anomaly alerts
- Validating the performance of fraud models
- Setting thresholds to minimise false positives
- Investigating flagged transactions
- Integrating AI alerts with case management systems
- Ensuring ethical use of behavioural AI
- Documenting model fairness in fraud detection
- Testing alert escalation and response workflows
- Reporting fraud detection outcomes to audit committees
Module 14: Data Integrity in AI-Driven SOX Processes - Validating data inputs to AI models
- Preventing data tampering and manipulation
- Ensuring referential integrity across datasets
- Using hashing and digital signatures for data tracking
- Monitoring data pipeline health
- Detecting duplicate or missing data entries
- Validating data transformations before model input
- Controlling access to training and operational data
- Documenting data mapping and lineage
- Using blockchain-inspired ledgers for critical data
Module 15: Third-Party and Vendor AI Solutions - Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Designing test plans for AI-integrated controls
- Selecting appropriate sample sizes and testing frequency
- Testing model accuracy and consistency over time
- Validating control design through walkthroughs
- Performing re-performance of AI outputs
- Assessing model performance against benchmarks
- Detecting bias and fairness issues in decision outputs
- Testing fallback and override mechanisms
- Analysing false positives and false negatives
- Documenting test results and exceptions
Module 8: Managing Model Drift and Data Decay - Understanding model drift in financial applications
- Monitoring for concept and data drift
- Setting thresholds for model re-evaluation
- Designing automated drift detection alerts
- Retesting controls after significant data shifts
- Updating model training data without compromising controls
- Documenting drift response procedures
- Ensuring model stability during market volatility
- Linking drift detection to change management
- Creating audit evidence of drift mitigation
Module 9: AI Governance and Organisational Controls - Establishing an AI governance committee
- Defining roles: data scientists, auditors, compliance officers
- Creating AI model development standards
- Implementing model review and approval workflows
- Conducting pre-deployment risk assessments
- Setting model retirement criteria
- Integrating AI governance into SOX committees
- Training control owners on AI responsibilities
- Communicating AI risks to executive leadership
- Aligning AI governance with enterprise risk management
Module 10: Change Management in AI Systems - Tracking changes to AI models, data, and environment
- Implementing version control for machine learning
- Using Git for model and code traceability
- Documenting parameter tuning and hyperparameter changes
- Reviewing model updates for SOX impact
- Testing controls after model deployment
- Managing emergency model fixes and patches
- Ensuring rollback capabilities are in place
- Communicating changes to audit teams
- Archiving historical model versions for inspection
Module 11: Audit Preparedness and Evidence Collection - Preparing for external audit of AI systems
- Compiling evidence packs for AI controls
- Selecting samples for audit testing
- Responding to auditor inquiries about AI logic
- Demonstrating model interpretability
- Providing data lineage reports
- Creating model performance scorecards
- Organising documentation by control objective
- Conducting internal mock audits
- Defending AI control effectiveness under scrutiny
Module 12: Explainability and Interpretability in SOX Audits - Why explainability matters in financial controls
- Using SHAP, LIME, and other interpretability tools
- Generating human-readable explanations for decisions
- Balancing model complexity with auditability
- Documenting model decision logic for auditors
- Creating decision trees from black-box models
- Presenting trade-offs between accuracy and transparency
- Using surrogate models for explanation
- Training auditors on AI interpretability outputs
- Storing explanation logs for future review
Module 13: AI in Fraud Detection and Anomaly Monitoring - Using AI for real-time fraud detection
- Designing controls around AI-driven anomaly alerts
- Validating the performance of fraud models
- Setting thresholds to minimise false positives
- Investigating flagged transactions
- Integrating AI alerts with case management systems
- Ensuring ethical use of behavioural AI
- Documenting model fairness in fraud detection
- Testing alert escalation and response workflows
- Reporting fraud detection outcomes to audit committees
Module 14: Data Integrity in AI-Driven SOX Processes - Validating data inputs to AI models
- Preventing data tampering and manipulation
- Ensuring referential integrity across datasets
- Using hashing and digital signatures for data tracking
- Monitoring data pipeline health
- Detecting duplicate or missing data entries
- Validating data transformations before model input
- Controlling access to training and operational data
- Documenting data mapping and lineage
- Using blockchain-inspired ledgers for critical data
Module 15: Third-Party and Vendor AI Solutions - Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Establishing an AI governance committee
- Defining roles: data scientists, auditors, compliance officers
- Creating AI model development standards
- Implementing model review and approval workflows
- Conducting pre-deployment risk assessments
- Setting model retirement criteria
- Integrating AI governance into SOX committees
- Training control owners on AI responsibilities
- Communicating AI risks to executive leadership
- Aligning AI governance with enterprise risk management
Module 10: Change Management in AI Systems - Tracking changes to AI models, data, and environment
- Implementing version control for machine learning
- Using Git for model and code traceability
- Documenting parameter tuning and hyperparameter changes
- Reviewing model updates for SOX impact
- Testing controls after model deployment
- Managing emergency model fixes and patches
- Ensuring rollback capabilities are in place
- Communicating changes to audit teams
- Archiving historical model versions for inspection
Module 11: Audit Preparedness and Evidence Collection - Preparing for external audit of AI systems
- Compiling evidence packs for AI controls
- Selecting samples for audit testing
- Responding to auditor inquiries about AI logic
- Demonstrating model interpretability
- Providing data lineage reports
- Creating model performance scorecards
- Organising documentation by control objective
- Conducting internal mock audits
- Defending AI control effectiveness under scrutiny
Module 12: Explainability and Interpretability in SOX Audits - Why explainability matters in financial controls
- Using SHAP, LIME, and other interpretability tools
- Generating human-readable explanations for decisions
- Balancing model complexity with auditability
- Documenting model decision logic for auditors
- Creating decision trees from black-box models
- Presenting trade-offs between accuracy and transparency
- Using surrogate models for explanation
- Training auditors on AI interpretability outputs
- Storing explanation logs for future review
Module 13: AI in Fraud Detection and Anomaly Monitoring - Using AI for real-time fraud detection
- Designing controls around AI-driven anomaly alerts
- Validating the performance of fraud models
- Setting thresholds to minimise false positives
- Investigating flagged transactions
- Integrating AI alerts with case management systems
- Ensuring ethical use of behavioural AI
- Documenting model fairness in fraud detection
- Testing alert escalation and response workflows
- Reporting fraud detection outcomes to audit committees
Module 14: Data Integrity in AI-Driven SOX Processes - Validating data inputs to AI models
- Preventing data tampering and manipulation
- Ensuring referential integrity across datasets
- Using hashing and digital signatures for data tracking
- Monitoring data pipeline health
- Detecting duplicate or missing data entries
- Validating data transformations before model input
- Controlling access to training and operational data
- Documenting data mapping and lineage
- Using blockchain-inspired ledgers for critical data
Module 15: Third-Party and Vendor AI Solutions - Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Preparing for external audit of AI systems
- Compiling evidence packs for AI controls
- Selecting samples for audit testing
- Responding to auditor inquiries about AI logic
- Demonstrating model interpretability
- Providing data lineage reports
- Creating model performance scorecards
- Organising documentation by control objective
- Conducting internal mock audits
- Defending AI control effectiveness under scrutiny
Module 12: Explainability and Interpretability in SOX Audits - Why explainability matters in financial controls
- Using SHAP, LIME, and other interpretability tools
- Generating human-readable explanations for decisions
- Balancing model complexity with auditability
- Documenting model decision logic for auditors
- Creating decision trees from black-box models
- Presenting trade-offs between accuracy and transparency
- Using surrogate models for explanation
- Training auditors on AI interpretability outputs
- Storing explanation logs for future review
Module 13: AI in Fraud Detection and Anomaly Monitoring - Using AI for real-time fraud detection
- Designing controls around AI-driven anomaly alerts
- Validating the performance of fraud models
- Setting thresholds to minimise false positives
- Investigating flagged transactions
- Integrating AI alerts with case management systems
- Ensuring ethical use of behavioural AI
- Documenting model fairness in fraud detection
- Testing alert escalation and response workflows
- Reporting fraud detection outcomes to audit committees
Module 14: Data Integrity in AI-Driven SOX Processes - Validating data inputs to AI models
- Preventing data tampering and manipulation
- Ensuring referential integrity across datasets
- Using hashing and digital signatures for data tracking
- Monitoring data pipeline health
- Detecting duplicate or missing data entries
- Validating data transformations before model input
- Controlling access to training and operational data
- Documenting data mapping and lineage
- Using blockchain-inspired ledgers for critical data
Module 15: Third-Party and Vendor AI Solutions - Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Using AI for real-time fraud detection
- Designing controls around AI-driven anomaly alerts
- Validating the performance of fraud models
- Setting thresholds to minimise false positives
- Investigating flagged transactions
- Integrating AI alerts with case management systems
- Ensuring ethical use of behavioural AI
- Documenting model fairness in fraud detection
- Testing alert escalation and response workflows
- Reporting fraud detection outcomes to audit committees
Module 14: Data Integrity in AI-Driven SOX Processes - Validating data inputs to AI models
- Preventing data tampering and manipulation
- Ensuring referential integrity across datasets
- Using hashing and digital signatures for data tracking
- Monitoring data pipeline health
- Detecting duplicate or missing data entries
- Validating data transformations before model input
- Controlling access to training and operational data
- Documenting data mapping and lineage
- Using blockchain-inspired ledgers for critical data
Module 15: Third-Party and Vendor AI Solutions - Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Assessing SOX risk in vendor-provided AI tools
- Reviewing SAS 70, SOC 1, and SOC 2 reports for AI vendors
- Negotiating audit rights for third-party models
- Testing vendor controls independently
- Mapping vendor responsibilities in service agreements
- Ensuring data privacy and residency requirements
- Monitoring vendor model updates
- Creating contingency plans for vendor failure
- Documenting reliance on external AI systems
- Maintaining internal accountability despite outsourcing
Module 16: AI in Close Automation and Financial Reporting - Using AI for automated journal entry creation
- Validating AI-generated entries for accuracy
- Designing review workflows for auto-approved entries
- Monitoring system-generated adjustments
- Reconciling AI-assisted accounts
- Flagging material variances automatically
- Documenting logic behind auto-categorisation
- Testing period-end close workflows
- Integrating AI tools with ERP systems
- Ensuring segregation between system and human reviewers
Module 17: Advanced AI Control Patterns - Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Implementing dual-model validation for high-risk decisions
- Using ensemble methods with control safeguards
- Designing consensus mechanisms for AI outputs
- Implementing human-in-the-loop checkpoints
- Creating override workflows with audit trails
- Using threshold-based escalation for AI decisions
- Applying adversarial testing to model outputs
- Testing AI under stress conditions
- Designing controls for real-time inference systems
- Validating offline vs. online model performance
Module 18: Case Studies in AI-Driven SOX Compliance - AI in accounts payable anomaly detection
- Machine learning for revenue recognition controls
- Automated fixed asset tracking with computer vision
- AI-powered reconciliations in treasury operations
- Using NLP to validate contract terms for revenue booking
- Predictive controls in inventory valuation
- AI for lease accounting under ASC 842
- Automating SOX testing for high-volume transactions
- Controlling AI tools in consolidation processes
- Building audit-ready AI frameworks in fintech startups
Module 19: Certification Project: Build Your AI-SOX Control Framework - Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks
Module 20: Sustaining and Scaling AI Compliance - Creating a centralised AI control repository
- Scaling best practices across business units
- Training teams on AI compliance standards
- Introducing AI literacy into audit curricula
- Conducting periodic AI control maturity assessments
- Updating frameworks as AI evolves
- Measuring ROI of AI compliance investments
- Reporting AI control performance to executives
- Preparing for future regulatory changes
- Leveraging your Certificate of Completion for career growth
- Selecting a real or simulated AI-impacted process
- Conducting a SOX risk assessment for the process
- Designing control objectives and automated checks
- Creating documentation templates and narratives
- Building a monitoring dashboard prototype
- Testing control effectiveness with sample data
- Preparing audit evidence packages
- Documenting model governance and change control
- Presenting a board-ready control summary
- Reviewing your framework against industry benchmarks