Mastering AI-Driven Vendor Due Diligence for Competitive Advantage
You're under pressure. Stakeholders demand faster decisions, yet every vendor choice carries risk - compliance gaps, security flaws, cost overruns, or failed integrations that waste millions. The traditional due diligence process is too slow, too manual, and no longer viable in an era of rapid AI adoption. You know the stakes. One wrong vendor decision can delay digital transformation by months, erode margins, and expose your organisation to reputational damage. But getting it right - truly right - means more than checking boxes. It means leveraging intelligent systems to predict risk, validate performance, and align vendor capabilities with strategic outcomes. Mastering AI-Driven Vendor Due Diligence for Competitive Advantage is not another theoretical framework. It's a precision-built methodology that transforms how procurement, technology governance, and vendor risk teams operate. This course delivers a repeatable, AI-embedded due diligence system that turns vendor evaluation from a cost centre into a strategic lever. Imagine walking into your next board meeting with a fully validated, AI-assisted due diligence dossier - complete with predictive risk scoring, compliance alignment maps, and ROI forecasts - all generated in under 48 hours. That’s the standard this course sets. One senior vendor risk manager at a global fintech used this framework to reduce evaluation cycles from 14 days to 3.5 days, uncovering a critical data sovereignty gap in a shortlisted AI provider before contract signing. The result? A $2.1M procurement reversal that avoided regulatory penalties and protected customer trust. This isn’t about automation for efficiency’s sake. It’s about building a defensible, scalable due diligence engine that gives your organisation a measurable edge. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced. Always Accessible. Built for Real-World Demands.
You need clarity, not complexity. That’s why this course is designed to fit seamlessly into your schedule, delivering elite knowledge without disruption. - Self-paced learning with immediate online access upon enrollment - start today, progress at your speed.
- On-demand structure means no rigid timetables, no live sessions, and no deadlines. Learn when and where it works for you.
- Most professionals complete the core modules in 12–18 hours and begin applying frameworks within 5–7 days.
- Lifetime access includes all future content updates at no additional cost, ensuring your certification remains relevant as AI regulation and tools evolve.
- Access is 24/7, globally available, and fully mobile-friendly, whether you're reviewing a risk matrix on your phone during travel or completing a due diligence checklist from your tablet at a site visit.
Expert Guidance Without the Hype
This isn’t isolated learning. You’re supported throughout by clear, actionable content reviewed by professionals with proven track records in AI governance and enterprise procurement. - Every module includes instructor-curated decision trees, real-world templates, and embedded validation rules used by top-tier organisations.
- Ongoing written support via structured Q&A pathways ensures you can clarify complex concepts with precision, without being funneled into webinars or video calls.
- You’ll build your own AI-Driven Vendor Risk Profile step-by-step, using the same logic gates and scoring models deployed in Fortune 500 vendor onboarding pipelines.
Certification with Global Recognition
Upon completion, you’ll earn a professionally formatted Certificate of Completion issued by The Art of Service. This credential is widely recognised across procurement, IT governance, risk, and compliance roles in regulated industries including finance, healthcare, and critical infrastructure. The Art of Service has issued over 250,000 certifications globally, with alumni in organisations including Deloitte, Siemens, UBS, and NHS Digital. This certification is not a participation trophy - it’s a signal of technical rigour and operational readiness. No Risk. No Hidden Costs. Full Transparency.
We eliminate every barrier to starting. - Pricing is straightforward, with no hidden fees, subscription traps, or upsells.
- We accept Visa, Mastercard, PayPal - payment processing is secure, encrypted, and compliant with global standards.
- If, after completing the material, you don’t find it applicable to your role, you’re covered by our 30-day satisfied-or-refunded guarantee. No questions, no delays.
- After enrollment, you’ll receive a confirmation email. Your access details will be sent separately once your course materials have been finalised - a standard process that ensures data integrity and secure provisioning.
This Works - Even If You’re Not an AI Expert
“I handle vendor contracts, not machine learning.” That’s exactly why this course is designed for you. The curriculum assumes no prior AI expertise. All models, algorithms, and data workflows are explained through decision logic, risk thresholds, and business outcomes - not code or data science theory. One procurement lead in the energy sector used this course to overhaul her team’s AI vendor assessments. Within six weeks, she led the successful deployment of an AI-driven compliance monitoring system - avoiding a potential $4.3M in non-compliance fines. She had never run a model before. She didn’t need to. If you can map a process, assess risk, or draft a contract, this course will amplify your impact. The framework works - even if you’re time-constrained, working across departments, or operating in a highly regulated environment. You’re not buying content. You're gaining access to a proven system for reducing risk, accelerating decisions, and earning recognition as a strategic enabler.
Module 1: Foundations of AI-Driven Vendor Due Diligence - Why traditional vendor due diligence fails in the age of AI
- Understanding the core risks in AI vendor ecosystems
- Key differences between AI and non-AI vendor assessments
- The four pillars of intelligent due diligence: accuracy, transparency, scalability, and foresight
- Regulatory drivers shaping AI vendor governance (GDPR, AI Act, NIST AI RMF)
- Defining critical success factors for AI vendor integration
- Mapping organisational risk appetite to vendor selection criteria
- Establishing decision ownership and escalation pathways
- Integrating due diligence into broader procurement and innovation strategies
- Measuring the cost of poor vendor decisions in financial and operational terms
Module 2: Strategic Frameworks for AI Vendor Evaluation - The AI Vendor Maturity Model: assessing capability depth
- Vendor innovation vs. stability: finding the right balance
- Designing a dynamic risk weighting matrix
- Creating role-based assessment profiles (procurement, legal, technical, compliance)
- Integrating ESG criteria into AI vendor scoring
- Building a vendor risk taxonomy specific to AI services
- Using control frameworks like ISO 27001, SOC 2, and CSA CCM in AI contexts
- Assessing algorithmic bias potential in vendor offerings
- Designing scenario-based testing for vendor resilience
- Developing a pre-engagement threat model for high-risk vendors
Module 3: AI-Powered Risk Identification & Analysis - Automated risk signal detection in vendor documentation
- NLP techniques for parsing vendor contracts and SLAs
- Extracting and validating claims from AI vendor marketing materials
- Using LLMs to generate risk hypotheses from unstructured data
- Identifying red flags in training data provenance and data governance
- Detecting hallucination patterns in vendor-provided case studies
- Analysing change management history for operational instability
- Mapping vendor dependency risks in third-party AI models
- Using sentiment analysis to assess customer and employee reviews
- Flagging inconsistencies in uptime, performance, and security incident reporting
Module 4: Data Integrity & Security in AI Vendor Systems - Evaluating data lineage and provenance tracking capabilities
- Assessing real-time data drift detection mechanisms
- Validating model retraining triggers and frequency
- Testing for data poisoning vulnerability in vendor pipelines
- Reviewing encryption standards for data in transit and at rest
- Confirming access controls and least privilege implementation
- Auditing multi-tenancy architecture for isolation risks
- Evaluating GDPR, CCPA, and cross-border data flow compliance
- Testing model inversion and membership inference attack resilience
- Requiring transparent model card and data card disclosures
Module 5: AI Model Transparency & Explainability Verification - Demanding model documentation as a contractual obligation
- Validating model architecture transparency: what’s proprietary vs. explainable
- Assessing the vendor’s ability to provide local explanations
- Evaluating SHAP, LIME, or counterfactual explanations in practice
- Testing for consistency in model behaviour across subpopulations
- Requiring adversarial robustness testing reports
- Determining fallback mechanisms during model failure
- Verifying interpretability for high-stakes decisions (HR, lending, healthcare)
- Using surrogate models to audit black-box vendor systems
- Establishing monitoring protocols for model degradation
Module 6: Performance Benchmarking & Validation - Designing domain-specific accuracy test sets
- Requiring out-of-sample performance metrics from vendors
- Validating latency, throughput, and scalability claims
- Testing real-world inference performance under load
- Assessing model drift monitoring and alerting capabilities
- Setting up shadow mode testing before production deployment
- Running A/B comparisons with alternative models or legacy systems
- Using synthetic data to stress-test edge cases
- Measuring fairness metrics across protected attributes
- Demanding independent third-party audit results when available
Module 7: Contractual Safeguards & Service Level Enforcement - Benchmarking performance SLAs beyond uptime and availability
- Defining measurable AI-specific service levels: accuracy, drift, latency
- Building penalty clauses for model degradation or bias incidents
- Requiring audit rights and model access for validation
- Setting data ownership and model ownership terms
- Negotiating intellectual property rights for fine-tuned models
- Ensuring exit rights and model transferability
- Requiring model version rollback capabilities
- Defining incident response timelines for AI failures
- Including kill-switch provisions for uncontrollable model behaviour
Module 8: Governance, Monitoring & Continuous Assessment - Building ongoing vendor health dashboards
- Automating signal ingestion from security bulletins and CVEs
- Integrating financial health monitoring for vendor continuity
- Tracking regulatory compliance status in real time
- Setting thresholds for automatic reassessment triggers
- Using AI to summarise and prioritise incident reports
- Conducting quarterly risk recalibration sessions
- Implementing automated alerting for compliance drift
- Updating risk profiles based on new use cases or integration depth
- Establishing board-level reporting templates for vendor risk
Module 9: AI Integration Risk Assessment - Mapping data flow dependencies between vendor and internal systems
- Assessing API security and rate limiting in production
- Validating error handling and retry logic in integrations
- Testing integration resilience during vendor outages
- Scoping potential cascading failures across systems
- Ensuring logging and tracing across integration boundaries
- Monitoring for unauthorised data exfiltration patterns
- Validating input sanitisation to prevent prompt injection
- Checking for model chain-of-thought leaks in API responses
- Testing integration with identity and access management systems
Module 10: Financial & Operational Due Diligence - Validating ROI claims with realistic cost-benefit models
- Uncovering hidden costs in API usage, scaling, and support
- Assessing vendor financial stability using public signals
- Reviewing customer retention and churn data
- Evaluating R&D investment as a proxy for longevity
- Analysing leadership team expertise and turnover
- Mapping vendor roadmap alignment with your strategic goals
- Assessing support response times and resolution quality
- Benchmarking vendor pricing against open-source alternatives
- Modelling total cost of ownership over 3- and 5-year horizons
Module 11: Ethical AI & Bias Mitigation Due Diligence - Requiring bias impact assessments from vendors
- Validating demographic representation in training data
- Testing model outcomes across subgroup fairness metrics
- Demanding transparency in bias mitigation techniques used
- Checking for ongoing fairness monitoring in production
- Assessing recourse mechanisms for affected individuals
- Evaluating human-in-the-loop requirements for high-risk use cases
- Reviewing ethical use policies and prohibited applications
- Confirming independent ethics board or advisory oversight
- Mapping vendor practices to OECD AI Principles and AI HLEG guidelines
Module 12: Regulatory Compliance & Audit Readiness - Aligning vendor due diligence with internal audit requirements
- Generating automated compliance evidence packs
- Preparing for NIST AI RMF conformance assessments
- Building GDPR-compliant data processing addendums
- Documenting lawful basis for AI-driven decision-making
- Preparing for EU AI Act high-risk classification checks
- Creating defensible records of due diligence decisions
- Responding to regulatory inquiries with pre-validated evidence
- Ensuring right-to-explanation compliance for affected parties
- Integrating vendor assessments into broader GRC platforms
Module 13: Cross-Functional Alignment & Stakeholder Engagement - Creating shared vendor risk language across departments
- Designing role-specific due diligence checklists
- Running cross-functional vendor evaluation workshops
- Communicating risk in business terms to non-technical executives
- Building consensus on high-risk vendor decisions
- Training procurement teams on AI-specific red flags
- Equipping legal teams with AI clause libraries
- Up-skilling compliance officers on AI risk indicators
- Creating escalation protocols for fast-tracked vendors
- Generating executive summaries from technical assessments
Module 14: AI-Specific Due Diligence Toolkits - Building a custom RFP generator for AI vendors
- Designing AI-focused vendor questionnaire templates
- Creating dynamic risk scoring spreadsheets with automated logic
- Using checklists to enforce consistency across assessments
- Developing due diligence playbooks for common AI use cases
- Generating model disclosure request forms
- Building contract clause libraries for AI-specific terms
- Creating incident response playbooks for AI failures
- Designing management dashboards for active vendors
- Automating watchlist alerts using RSS and public feeds
Module 15: Practical Application & Real-World Simulations - Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package
Module 16: Certification, Next Steps & Career Advancement - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from each module
- Completing the final AI Vendor Due Diligence Project
- Submitting work for certification validation
- Receiving official Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Communicating value to managers and stakeholders
- Leveraging certification in performance reviews and promotions
- Accessing advanced resources and alumni networks
- Planning your next strategic initiative using the AI due diligence framework
- Why traditional vendor due diligence fails in the age of AI
- Understanding the core risks in AI vendor ecosystems
- Key differences between AI and non-AI vendor assessments
- The four pillars of intelligent due diligence: accuracy, transparency, scalability, and foresight
- Regulatory drivers shaping AI vendor governance (GDPR, AI Act, NIST AI RMF)
- Defining critical success factors for AI vendor integration
- Mapping organisational risk appetite to vendor selection criteria
- Establishing decision ownership and escalation pathways
- Integrating due diligence into broader procurement and innovation strategies
- Measuring the cost of poor vendor decisions in financial and operational terms
Module 2: Strategic Frameworks for AI Vendor Evaluation - The AI Vendor Maturity Model: assessing capability depth
- Vendor innovation vs. stability: finding the right balance
- Designing a dynamic risk weighting matrix
- Creating role-based assessment profiles (procurement, legal, technical, compliance)
- Integrating ESG criteria into AI vendor scoring
- Building a vendor risk taxonomy specific to AI services
- Using control frameworks like ISO 27001, SOC 2, and CSA CCM in AI contexts
- Assessing algorithmic bias potential in vendor offerings
- Designing scenario-based testing for vendor resilience
- Developing a pre-engagement threat model for high-risk vendors
Module 3: AI-Powered Risk Identification & Analysis - Automated risk signal detection in vendor documentation
- NLP techniques for parsing vendor contracts and SLAs
- Extracting and validating claims from AI vendor marketing materials
- Using LLMs to generate risk hypotheses from unstructured data
- Identifying red flags in training data provenance and data governance
- Detecting hallucination patterns in vendor-provided case studies
- Analysing change management history for operational instability
- Mapping vendor dependency risks in third-party AI models
- Using sentiment analysis to assess customer and employee reviews
- Flagging inconsistencies in uptime, performance, and security incident reporting
Module 4: Data Integrity & Security in AI Vendor Systems - Evaluating data lineage and provenance tracking capabilities
- Assessing real-time data drift detection mechanisms
- Validating model retraining triggers and frequency
- Testing for data poisoning vulnerability in vendor pipelines
- Reviewing encryption standards for data in transit and at rest
- Confirming access controls and least privilege implementation
- Auditing multi-tenancy architecture for isolation risks
- Evaluating GDPR, CCPA, and cross-border data flow compliance
- Testing model inversion and membership inference attack resilience
- Requiring transparent model card and data card disclosures
Module 5: AI Model Transparency & Explainability Verification - Demanding model documentation as a contractual obligation
- Validating model architecture transparency: what’s proprietary vs. explainable
- Assessing the vendor’s ability to provide local explanations
- Evaluating SHAP, LIME, or counterfactual explanations in practice
- Testing for consistency in model behaviour across subpopulations
- Requiring adversarial robustness testing reports
- Determining fallback mechanisms during model failure
- Verifying interpretability for high-stakes decisions (HR, lending, healthcare)
- Using surrogate models to audit black-box vendor systems
- Establishing monitoring protocols for model degradation
Module 6: Performance Benchmarking & Validation - Designing domain-specific accuracy test sets
- Requiring out-of-sample performance metrics from vendors
- Validating latency, throughput, and scalability claims
- Testing real-world inference performance under load
- Assessing model drift monitoring and alerting capabilities
- Setting up shadow mode testing before production deployment
- Running A/B comparisons with alternative models or legacy systems
- Using synthetic data to stress-test edge cases
- Measuring fairness metrics across protected attributes
- Demanding independent third-party audit results when available
Module 7: Contractual Safeguards & Service Level Enforcement - Benchmarking performance SLAs beyond uptime and availability
- Defining measurable AI-specific service levels: accuracy, drift, latency
- Building penalty clauses for model degradation or bias incidents
- Requiring audit rights and model access for validation
- Setting data ownership and model ownership terms
- Negotiating intellectual property rights for fine-tuned models
- Ensuring exit rights and model transferability
- Requiring model version rollback capabilities
- Defining incident response timelines for AI failures
- Including kill-switch provisions for uncontrollable model behaviour
Module 8: Governance, Monitoring & Continuous Assessment - Building ongoing vendor health dashboards
- Automating signal ingestion from security bulletins and CVEs
- Integrating financial health monitoring for vendor continuity
- Tracking regulatory compliance status in real time
- Setting thresholds for automatic reassessment triggers
- Using AI to summarise and prioritise incident reports
- Conducting quarterly risk recalibration sessions
- Implementing automated alerting for compliance drift
- Updating risk profiles based on new use cases or integration depth
- Establishing board-level reporting templates for vendor risk
Module 9: AI Integration Risk Assessment - Mapping data flow dependencies between vendor and internal systems
- Assessing API security and rate limiting in production
- Validating error handling and retry logic in integrations
- Testing integration resilience during vendor outages
- Scoping potential cascading failures across systems
- Ensuring logging and tracing across integration boundaries
- Monitoring for unauthorised data exfiltration patterns
- Validating input sanitisation to prevent prompt injection
- Checking for model chain-of-thought leaks in API responses
- Testing integration with identity and access management systems
Module 10: Financial & Operational Due Diligence - Validating ROI claims with realistic cost-benefit models
- Uncovering hidden costs in API usage, scaling, and support
- Assessing vendor financial stability using public signals
- Reviewing customer retention and churn data
- Evaluating R&D investment as a proxy for longevity
- Analysing leadership team expertise and turnover
- Mapping vendor roadmap alignment with your strategic goals
- Assessing support response times and resolution quality
- Benchmarking vendor pricing against open-source alternatives
- Modelling total cost of ownership over 3- and 5-year horizons
Module 11: Ethical AI & Bias Mitigation Due Diligence - Requiring bias impact assessments from vendors
- Validating demographic representation in training data
- Testing model outcomes across subgroup fairness metrics
- Demanding transparency in bias mitigation techniques used
- Checking for ongoing fairness monitoring in production
- Assessing recourse mechanisms for affected individuals
- Evaluating human-in-the-loop requirements for high-risk use cases
- Reviewing ethical use policies and prohibited applications
- Confirming independent ethics board or advisory oversight
- Mapping vendor practices to OECD AI Principles and AI HLEG guidelines
Module 12: Regulatory Compliance & Audit Readiness - Aligning vendor due diligence with internal audit requirements
- Generating automated compliance evidence packs
- Preparing for NIST AI RMF conformance assessments
- Building GDPR-compliant data processing addendums
- Documenting lawful basis for AI-driven decision-making
- Preparing for EU AI Act high-risk classification checks
- Creating defensible records of due diligence decisions
- Responding to regulatory inquiries with pre-validated evidence
- Ensuring right-to-explanation compliance for affected parties
- Integrating vendor assessments into broader GRC platforms
Module 13: Cross-Functional Alignment & Stakeholder Engagement - Creating shared vendor risk language across departments
- Designing role-specific due diligence checklists
- Running cross-functional vendor evaluation workshops
- Communicating risk in business terms to non-technical executives
- Building consensus on high-risk vendor decisions
- Training procurement teams on AI-specific red flags
- Equipping legal teams with AI clause libraries
- Up-skilling compliance officers on AI risk indicators
- Creating escalation protocols for fast-tracked vendors
- Generating executive summaries from technical assessments
Module 14: AI-Specific Due Diligence Toolkits - Building a custom RFP generator for AI vendors
- Designing AI-focused vendor questionnaire templates
- Creating dynamic risk scoring spreadsheets with automated logic
- Using checklists to enforce consistency across assessments
- Developing due diligence playbooks for common AI use cases
- Generating model disclosure request forms
- Building contract clause libraries for AI-specific terms
- Creating incident response playbooks for AI failures
- Designing management dashboards for active vendors
- Automating watchlist alerts using RSS and public feeds
Module 15: Practical Application & Real-World Simulations - Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package
Module 16: Certification, Next Steps & Career Advancement - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from each module
- Completing the final AI Vendor Due Diligence Project
- Submitting work for certification validation
- Receiving official Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Communicating value to managers and stakeholders
- Leveraging certification in performance reviews and promotions
- Accessing advanced resources and alumni networks
- Planning your next strategic initiative using the AI due diligence framework
- Automated risk signal detection in vendor documentation
- NLP techniques for parsing vendor contracts and SLAs
- Extracting and validating claims from AI vendor marketing materials
- Using LLMs to generate risk hypotheses from unstructured data
- Identifying red flags in training data provenance and data governance
- Detecting hallucination patterns in vendor-provided case studies
- Analysing change management history for operational instability
- Mapping vendor dependency risks in third-party AI models
- Using sentiment analysis to assess customer and employee reviews
- Flagging inconsistencies in uptime, performance, and security incident reporting
Module 4: Data Integrity & Security in AI Vendor Systems - Evaluating data lineage and provenance tracking capabilities
- Assessing real-time data drift detection mechanisms
- Validating model retraining triggers and frequency
- Testing for data poisoning vulnerability in vendor pipelines
- Reviewing encryption standards for data in transit and at rest
- Confirming access controls and least privilege implementation
- Auditing multi-tenancy architecture for isolation risks
- Evaluating GDPR, CCPA, and cross-border data flow compliance
- Testing model inversion and membership inference attack resilience
- Requiring transparent model card and data card disclosures
Module 5: AI Model Transparency & Explainability Verification - Demanding model documentation as a contractual obligation
- Validating model architecture transparency: what’s proprietary vs. explainable
- Assessing the vendor’s ability to provide local explanations
- Evaluating SHAP, LIME, or counterfactual explanations in practice
- Testing for consistency in model behaviour across subpopulations
- Requiring adversarial robustness testing reports
- Determining fallback mechanisms during model failure
- Verifying interpretability for high-stakes decisions (HR, lending, healthcare)
- Using surrogate models to audit black-box vendor systems
- Establishing monitoring protocols for model degradation
Module 6: Performance Benchmarking & Validation - Designing domain-specific accuracy test sets
- Requiring out-of-sample performance metrics from vendors
- Validating latency, throughput, and scalability claims
- Testing real-world inference performance under load
- Assessing model drift monitoring and alerting capabilities
- Setting up shadow mode testing before production deployment
- Running A/B comparisons with alternative models or legacy systems
- Using synthetic data to stress-test edge cases
- Measuring fairness metrics across protected attributes
- Demanding independent third-party audit results when available
Module 7: Contractual Safeguards & Service Level Enforcement - Benchmarking performance SLAs beyond uptime and availability
- Defining measurable AI-specific service levels: accuracy, drift, latency
- Building penalty clauses for model degradation or bias incidents
- Requiring audit rights and model access for validation
- Setting data ownership and model ownership terms
- Negotiating intellectual property rights for fine-tuned models
- Ensuring exit rights and model transferability
- Requiring model version rollback capabilities
- Defining incident response timelines for AI failures
- Including kill-switch provisions for uncontrollable model behaviour
Module 8: Governance, Monitoring & Continuous Assessment - Building ongoing vendor health dashboards
- Automating signal ingestion from security bulletins and CVEs
- Integrating financial health monitoring for vendor continuity
- Tracking regulatory compliance status in real time
- Setting thresholds for automatic reassessment triggers
- Using AI to summarise and prioritise incident reports
- Conducting quarterly risk recalibration sessions
- Implementing automated alerting for compliance drift
- Updating risk profiles based on new use cases or integration depth
- Establishing board-level reporting templates for vendor risk
Module 9: AI Integration Risk Assessment - Mapping data flow dependencies between vendor and internal systems
- Assessing API security and rate limiting in production
- Validating error handling and retry logic in integrations
- Testing integration resilience during vendor outages
- Scoping potential cascading failures across systems
- Ensuring logging and tracing across integration boundaries
- Monitoring for unauthorised data exfiltration patterns
- Validating input sanitisation to prevent prompt injection
- Checking for model chain-of-thought leaks in API responses
- Testing integration with identity and access management systems
Module 10: Financial & Operational Due Diligence - Validating ROI claims with realistic cost-benefit models
- Uncovering hidden costs in API usage, scaling, and support
- Assessing vendor financial stability using public signals
- Reviewing customer retention and churn data
- Evaluating R&D investment as a proxy for longevity
- Analysing leadership team expertise and turnover
- Mapping vendor roadmap alignment with your strategic goals
- Assessing support response times and resolution quality
- Benchmarking vendor pricing against open-source alternatives
- Modelling total cost of ownership over 3- and 5-year horizons
Module 11: Ethical AI & Bias Mitigation Due Diligence - Requiring bias impact assessments from vendors
- Validating demographic representation in training data
- Testing model outcomes across subgroup fairness metrics
- Demanding transparency in bias mitigation techniques used
- Checking for ongoing fairness monitoring in production
- Assessing recourse mechanisms for affected individuals
- Evaluating human-in-the-loop requirements for high-risk use cases
- Reviewing ethical use policies and prohibited applications
- Confirming independent ethics board or advisory oversight
- Mapping vendor practices to OECD AI Principles and AI HLEG guidelines
Module 12: Regulatory Compliance & Audit Readiness - Aligning vendor due diligence with internal audit requirements
- Generating automated compliance evidence packs
- Preparing for NIST AI RMF conformance assessments
- Building GDPR-compliant data processing addendums
- Documenting lawful basis for AI-driven decision-making
- Preparing for EU AI Act high-risk classification checks
- Creating defensible records of due diligence decisions
- Responding to regulatory inquiries with pre-validated evidence
- Ensuring right-to-explanation compliance for affected parties
- Integrating vendor assessments into broader GRC platforms
Module 13: Cross-Functional Alignment & Stakeholder Engagement - Creating shared vendor risk language across departments
- Designing role-specific due diligence checklists
- Running cross-functional vendor evaluation workshops
- Communicating risk in business terms to non-technical executives
- Building consensus on high-risk vendor decisions
- Training procurement teams on AI-specific red flags
- Equipping legal teams with AI clause libraries
- Up-skilling compliance officers on AI risk indicators
- Creating escalation protocols for fast-tracked vendors
- Generating executive summaries from technical assessments
Module 14: AI-Specific Due Diligence Toolkits - Building a custom RFP generator for AI vendors
- Designing AI-focused vendor questionnaire templates
- Creating dynamic risk scoring spreadsheets with automated logic
- Using checklists to enforce consistency across assessments
- Developing due diligence playbooks for common AI use cases
- Generating model disclosure request forms
- Building contract clause libraries for AI-specific terms
- Creating incident response playbooks for AI failures
- Designing management dashboards for active vendors
- Automating watchlist alerts using RSS and public feeds
Module 15: Practical Application & Real-World Simulations - Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package
Module 16: Certification, Next Steps & Career Advancement - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from each module
- Completing the final AI Vendor Due Diligence Project
- Submitting work for certification validation
- Receiving official Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Communicating value to managers and stakeholders
- Leveraging certification in performance reviews and promotions
- Accessing advanced resources and alumni networks
- Planning your next strategic initiative using the AI due diligence framework
- Demanding model documentation as a contractual obligation
- Validating model architecture transparency: what’s proprietary vs. explainable
- Assessing the vendor’s ability to provide local explanations
- Evaluating SHAP, LIME, or counterfactual explanations in practice
- Testing for consistency in model behaviour across subpopulations
- Requiring adversarial robustness testing reports
- Determining fallback mechanisms during model failure
- Verifying interpretability for high-stakes decisions (HR, lending, healthcare)
- Using surrogate models to audit black-box vendor systems
- Establishing monitoring protocols for model degradation
Module 6: Performance Benchmarking & Validation - Designing domain-specific accuracy test sets
- Requiring out-of-sample performance metrics from vendors
- Validating latency, throughput, and scalability claims
- Testing real-world inference performance under load
- Assessing model drift monitoring and alerting capabilities
- Setting up shadow mode testing before production deployment
- Running A/B comparisons with alternative models or legacy systems
- Using synthetic data to stress-test edge cases
- Measuring fairness metrics across protected attributes
- Demanding independent third-party audit results when available
Module 7: Contractual Safeguards & Service Level Enforcement - Benchmarking performance SLAs beyond uptime and availability
- Defining measurable AI-specific service levels: accuracy, drift, latency
- Building penalty clauses for model degradation or bias incidents
- Requiring audit rights and model access for validation
- Setting data ownership and model ownership terms
- Negotiating intellectual property rights for fine-tuned models
- Ensuring exit rights and model transferability
- Requiring model version rollback capabilities
- Defining incident response timelines for AI failures
- Including kill-switch provisions for uncontrollable model behaviour
Module 8: Governance, Monitoring & Continuous Assessment - Building ongoing vendor health dashboards
- Automating signal ingestion from security bulletins and CVEs
- Integrating financial health monitoring for vendor continuity
- Tracking regulatory compliance status in real time
- Setting thresholds for automatic reassessment triggers
- Using AI to summarise and prioritise incident reports
- Conducting quarterly risk recalibration sessions
- Implementing automated alerting for compliance drift
- Updating risk profiles based on new use cases or integration depth
- Establishing board-level reporting templates for vendor risk
Module 9: AI Integration Risk Assessment - Mapping data flow dependencies between vendor and internal systems
- Assessing API security and rate limiting in production
- Validating error handling and retry logic in integrations
- Testing integration resilience during vendor outages
- Scoping potential cascading failures across systems
- Ensuring logging and tracing across integration boundaries
- Monitoring for unauthorised data exfiltration patterns
- Validating input sanitisation to prevent prompt injection
- Checking for model chain-of-thought leaks in API responses
- Testing integration with identity and access management systems
Module 10: Financial & Operational Due Diligence - Validating ROI claims with realistic cost-benefit models
- Uncovering hidden costs in API usage, scaling, and support
- Assessing vendor financial stability using public signals
- Reviewing customer retention and churn data
- Evaluating R&D investment as a proxy for longevity
- Analysing leadership team expertise and turnover
- Mapping vendor roadmap alignment with your strategic goals
- Assessing support response times and resolution quality
- Benchmarking vendor pricing against open-source alternatives
- Modelling total cost of ownership over 3- and 5-year horizons
Module 11: Ethical AI & Bias Mitigation Due Diligence - Requiring bias impact assessments from vendors
- Validating demographic representation in training data
- Testing model outcomes across subgroup fairness metrics
- Demanding transparency in bias mitigation techniques used
- Checking for ongoing fairness monitoring in production
- Assessing recourse mechanisms for affected individuals
- Evaluating human-in-the-loop requirements for high-risk use cases
- Reviewing ethical use policies and prohibited applications
- Confirming independent ethics board or advisory oversight
- Mapping vendor practices to OECD AI Principles and AI HLEG guidelines
Module 12: Regulatory Compliance & Audit Readiness - Aligning vendor due diligence with internal audit requirements
- Generating automated compliance evidence packs
- Preparing for NIST AI RMF conformance assessments
- Building GDPR-compliant data processing addendums
- Documenting lawful basis for AI-driven decision-making
- Preparing for EU AI Act high-risk classification checks
- Creating defensible records of due diligence decisions
- Responding to regulatory inquiries with pre-validated evidence
- Ensuring right-to-explanation compliance for affected parties
- Integrating vendor assessments into broader GRC platforms
Module 13: Cross-Functional Alignment & Stakeholder Engagement - Creating shared vendor risk language across departments
- Designing role-specific due diligence checklists
- Running cross-functional vendor evaluation workshops
- Communicating risk in business terms to non-technical executives
- Building consensus on high-risk vendor decisions
- Training procurement teams on AI-specific red flags
- Equipping legal teams with AI clause libraries
- Up-skilling compliance officers on AI risk indicators
- Creating escalation protocols for fast-tracked vendors
- Generating executive summaries from technical assessments
Module 14: AI-Specific Due Diligence Toolkits - Building a custom RFP generator for AI vendors
- Designing AI-focused vendor questionnaire templates
- Creating dynamic risk scoring spreadsheets with automated logic
- Using checklists to enforce consistency across assessments
- Developing due diligence playbooks for common AI use cases
- Generating model disclosure request forms
- Building contract clause libraries for AI-specific terms
- Creating incident response playbooks for AI failures
- Designing management dashboards for active vendors
- Automating watchlist alerts using RSS and public feeds
Module 15: Practical Application & Real-World Simulations - Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package
Module 16: Certification, Next Steps & Career Advancement - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from each module
- Completing the final AI Vendor Due Diligence Project
- Submitting work for certification validation
- Receiving official Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Communicating value to managers and stakeholders
- Leveraging certification in performance reviews and promotions
- Accessing advanced resources and alumni networks
- Planning your next strategic initiative using the AI due diligence framework
- Benchmarking performance SLAs beyond uptime and availability
- Defining measurable AI-specific service levels: accuracy, drift, latency
- Building penalty clauses for model degradation or bias incidents
- Requiring audit rights and model access for validation
- Setting data ownership and model ownership terms
- Negotiating intellectual property rights for fine-tuned models
- Ensuring exit rights and model transferability
- Requiring model version rollback capabilities
- Defining incident response timelines for AI failures
- Including kill-switch provisions for uncontrollable model behaviour
Module 8: Governance, Monitoring & Continuous Assessment - Building ongoing vendor health dashboards
- Automating signal ingestion from security bulletins and CVEs
- Integrating financial health monitoring for vendor continuity
- Tracking regulatory compliance status in real time
- Setting thresholds for automatic reassessment triggers
- Using AI to summarise and prioritise incident reports
- Conducting quarterly risk recalibration sessions
- Implementing automated alerting for compliance drift
- Updating risk profiles based on new use cases or integration depth
- Establishing board-level reporting templates for vendor risk
Module 9: AI Integration Risk Assessment - Mapping data flow dependencies between vendor and internal systems
- Assessing API security and rate limiting in production
- Validating error handling and retry logic in integrations
- Testing integration resilience during vendor outages
- Scoping potential cascading failures across systems
- Ensuring logging and tracing across integration boundaries
- Monitoring for unauthorised data exfiltration patterns
- Validating input sanitisation to prevent prompt injection
- Checking for model chain-of-thought leaks in API responses
- Testing integration with identity and access management systems
Module 10: Financial & Operational Due Diligence - Validating ROI claims with realistic cost-benefit models
- Uncovering hidden costs in API usage, scaling, and support
- Assessing vendor financial stability using public signals
- Reviewing customer retention and churn data
- Evaluating R&D investment as a proxy for longevity
- Analysing leadership team expertise and turnover
- Mapping vendor roadmap alignment with your strategic goals
- Assessing support response times and resolution quality
- Benchmarking vendor pricing against open-source alternatives
- Modelling total cost of ownership over 3- and 5-year horizons
Module 11: Ethical AI & Bias Mitigation Due Diligence - Requiring bias impact assessments from vendors
- Validating demographic representation in training data
- Testing model outcomes across subgroup fairness metrics
- Demanding transparency in bias mitigation techniques used
- Checking for ongoing fairness monitoring in production
- Assessing recourse mechanisms for affected individuals
- Evaluating human-in-the-loop requirements for high-risk use cases
- Reviewing ethical use policies and prohibited applications
- Confirming independent ethics board or advisory oversight
- Mapping vendor practices to OECD AI Principles and AI HLEG guidelines
Module 12: Regulatory Compliance & Audit Readiness - Aligning vendor due diligence with internal audit requirements
- Generating automated compliance evidence packs
- Preparing for NIST AI RMF conformance assessments
- Building GDPR-compliant data processing addendums
- Documenting lawful basis for AI-driven decision-making
- Preparing for EU AI Act high-risk classification checks
- Creating defensible records of due diligence decisions
- Responding to regulatory inquiries with pre-validated evidence
- Ensuring right-to-explanation compliance for affected parties
- Integrating vendor assessments into broader GRC platforms
Module 13: Cross-Functional Alignment & Stakeholder Engagement - Creating shared vendor risk language across departments
- Designing role-specific due diligence checklists
- Running cross-functional vendor evaluation workshops
- Communicating risk in business terms to non-technical executives
- Building consensus on high-risk vendor decisions
- Training procurement teams on AI-specific red flags
- Equipping legal teams with AI clause libraries
- Up-skilling compliance officers on AI risk indicators
- Creating escalation protocols for fast-tracked vendors
- Generating executive summaries from technical assessments
Module 14: AI-Specific Due Diligence Toolkits - Building a custom RFP generator for AI vendors
- Designing AI-focused vendor questionnaire templates
- Creating dynamic risk scoring spreadsheets with automated logic
- Using checklists to enforce consistency across assessments
- Developing due diligence playbooks for common AI use cases
- Generating model disclosure request forms
- Building contract clause libraries for AI-specific terms
- Creating incident response playbooks for AI failures
- Designing management dashboards for active vendors
- Automating watchlist alerts using RSS and public feeds
Module 15: Practical Application & Real-World Simulations - Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package
Module 16: Certification, Next Steps & Career Advancement - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from each module
- Completing the final AI Vendor Due Diligence Project
- Submitting work for certification validation
- Receiving official Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Communicating value to managers and stakeholders
- Leveraging certification in performance reviews and promotions
- Accessing advanced resources and alumni networks
- Planning your next strategic initiative using the AI due diligence framework
- Mapping data flow dependencies between vendor and internal systems
- Assessing API security and rate limiting in production
- Validating error handling and retry logic in integrations
- Testing integration resilience during vendor outages
- Scoping potential cascading failures across systems
- Ensuring logging and tracing across integration boundaries
- Monitoring for unauthorised data exfiltration patterns
- Validating input sanitisation to prevent prompt injection
- Checking for model chain-of-thought leaks in API responses
- Testing integration with identity and access management systems
Module 10: Financial & Operational Due Diligence - Validating ROI claims with realistic cost-benefit models
- Uncovering hidden costs in API usage, scaling, and support
- Assessing vendor financial stability using public signals
- Reviewing customer retention and churn data
- Evaluating R&D investment as a proxy for longevity
- Analysing leadership team expertise and turnover
- Mapping vendor roadmap alignment with your strategic goals
- Assessing support response times and resolution quality
- Benchmarking vendor pricing against open-source alternatives
- Modelling total cost of ownership over 3- and 5-year horizons
Module 11: Ethical AI & Bias Mitigation Due Diligence - Requiring bias impact assessments from vendors
- Validating demographic representation in training data
- Testing model outcomes across subgroup fairness metrics
- Demanding transparency in bias mitigation techniques used
- Checking for ongoing fairness monitoring in production
- Assessing recourse mechanisms for affected individuals
- Evaluating human-in-the-loop requirements for high-risk use cases
- Reviewing ethical use policies and prohibited applications
- Confirming independent ethics board or advisory oversight
- Mapping vendor practices to OECD AI Principles and AI HLEG guidelines
Module 12: Regulatory Compliance & Audit Readiness - Aligning vendor due diligence with internal audit requirements
- Generating automated compliance evidence packs
- Preparing for NIST AI RMF conformance assessments
- Building GDPR-compliant data processing addendums
- Documenting lawful basis for AI-driven decision-making
- Preparing for EU AI Act high-risk classification checks
- Creating defensible records of due diligence decisions
- Responding to regulatory inquiries with pre-validated evidence
- Ensuring right-to-explanation compliance for affected parties
- Integrating vendor assessments into broader GRC platforms
Module 13: Cross-Functional Alignment & Stakeholder Engagement - Creating shared vendor risk language across departments
- Designing role-specific due diligence checklists
- Running cross-functional vendor evaluation workshops
- Communicating risk in business terms to non-technical executives
- Building consensus on high-risk vendor decisions
- Training procurement teams on AI-specific red flags
- Equipping legal teams with AI clause libraries
- Up-skilling compliance officers on AI risk indicators
- Creating escalation protocols for fast-tracked vendors
- Generating executive summaries from technical assessments
Module 14: AI-Specific Due Diligence Toolkits - Building a custom RFP generator for AI vendors
- Designing AI-focused vendor questionnaire templates
- Creating dynamic risk scoring spreadsheets with automated logic
- Using checklists to enforce consistency across assessments
- Developing due diligence playbooks for common AI use cases
- Generating model disclosure request forms
- Building contract clause libraries for AI-specific terms
- Creating incident response playbooks for AI failures
- Designing management dashboards for active vendors
- Automating watchlist alerts using RSS and public feeds
Module 15: Practical Application & Real-World Simulations - Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package
Module 16: Certification, Next Steps & Career Advancement - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from each module
- Completing the final AI Vendor Due Diligence Project
- Submitting work for certification validation
- Receiving official Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Communicating value to managers and stakeholders
- Leveraging certification in performance reviews and promotions
- Accessing advanced resources and alumni networks
- Planning your next strategic initiative using the AI due diligence framework
- Requiring bias impact assessments from vendors
- Validating demographic representation in training data
- Testing model outcomes across subgroup fairness metrics
- Demanding transparency in bias mitigation techniques used
- Checking for ongoing fairness monitoring in production
- Assessing recourse mechanisms for affected individuals
- Evaluating human-in-the-loop requirements for high-risk use cases
- Reviewing ethical use policies and prohibited applications
- Confirming independent ethics board or advisory oversight
- Mapping vendor practices to OECD AI Principles and AI HLEG guidelines
Module 12: Regulatory Compliance & Audit Readiness - Aligning vendor due diligence with internal audit requirements
- Generating automated compliance evidence packs
- Preparing for NIST AI RMF conformance assessments
- Building GDPR-compliant data processing addendums
- Documenting lawful basis for AI-driven decision-making
- Preparing for EU AI Act high-risk classification checks
- Creating defensible records of due diligence decisions
- Responding to regulatory inquiries with pre-validated evidence
- Ensuring right-to-explanation compliance for affected parties
- Integrating vendor assessments into broader GRC platforms
Module 13: Cross-Functional Alignment & Stakeholder Engagement - Creating shared vendor risk language across departments
- Designing role-specific due diligence checklists
- Running cross-functional vendor evaluation workshops
- Communicating risk in business terms to non-technical executives
- Building consensus on high-risk vendor decisions
- Training procurement teams on AI-specific red flags
- Equipping legal teams with AI clause libraries
- Up-skilling compliance officers on AI risk indicators
- Creating escalation protocols for fast-tracked vendors
- Generating executive summaries from technical assessments
Module 14: AI-Specific Due Diligence Toolkits - Building a custom RFP generator for AI vendors
- Designing AI-focused vendor questionnaire templates
- Creating dynamic risk scoring spreadsheets with automated logic
- Using checklists to enforce consistency across assessments
- Developing due diligence playbooks for common AI use cases
- Generating model disclosure request forms
- Building contract clause libraries for AI-specific terms
- Creating incident response playbooks for AI failures
- Designing management dashboards for active vendors
- Automating watchlist alerts using RSS and public feeds
Module 15: Practical Application & Real-World Simulations - Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package
Module 16: Certification, Next Steps & Career Advancement - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from each module
- Completing the final AI Vendor Due Diligence Project
- Submitting work for certification validation
- Receiving official Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Communicating value to managers and stakeholders
- Leveraging certification in performance reviews and promotions
- Accessing advanced resources and alumni networks
- Planning your next strategic initiative using the AI due diligence framework
- Creating shared vendor risk language across departments
- Designing role-specific due diligence checklists
- Running cross-functional vendor evaluation workshops
- Communicating risk in business terms to non-technical executives
- Building consensus on high-risk vendor decisions
- Training procurement teams on AI-specific red flags
- Equipping legal teams with AI clause libraries
- Up-skilling compliance officers on AI risk indicators
- Creating escalation protocols for fast-tracked vendors
- Generating executive summaries from technical assessments
Module 14: AI-Specific Due Diligence Toolkits - Building a custom RFP generator for AI vendors
- Designing AI-focused vendor questionnaire templates
- Creating dynamic risk scoring spreadsheets with automated logic
- Using checklists to enforce consistency across assessments
- Developing due diligence playbooks for common AI use cases
- Generating model disclosure request forms
- Building contract clause libraries for AI-specific terms
- Creating incident response playbooks for AI failures
- Designing management dashboards for active vendors
- Automating watchlist alerts using RSS and public feeds
Module 15: Practical Application & Real-World Simulations - Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package
Module 16: Certification, Next Steps & Career Advancement - Preparing for the Certificate of Completion assessment
- Reviewing key concepts from each module
- Completing the final AI Vendor Due Diligence Project
- Submitting work for certification validation
- Receiving official Certificate of Completion issued by The Art of Service
- Adding certification to LinkedIn and professional profiles
- Communicating value to managers and stakeholders
- Leveraging certification in performance reviews and promotions
- Accessing advanced resources and alumni networks
- Planning your next strategic initiative using the AI due diligence framework
- Conducting a full due diligence assessment on a sample AI vendor
- Analysing real vendor documentation for risk signals
- Scoring a vendor using the AI Due Diligence Matrix
- Drafting a board-ready due diligence summary report
- Role-playing a vendor negotiation using risk findings
- Running a contract redlining exercise with AI clauses
- Simulating a breach response for a compromised AI vendor
- Updating a risk profile after new regulatory guidance
- Presenting findings to a mock executive committee
- Finalising a vendor go-live recommendation package