Mastering Third Party Risk Management in the Age of AI and Automation
You’re not just managing vendors anymore. You’re navigating a high-stakes digital ecosystem where one overlooked third party can trigger a cybersecurity breach, violate AI ethics frameworks, or disrupt your entire supply chain overnight. The pressure is real. Stakeholders demand resilience. Regulators demand proof. And you’re expected to deliver certainty in a world of complexity and constant change. Meanwhile, AI and automation are accelerating third-party integration faster than risk controls can keep up. Legacy processes fail. Manual checks miss algorithmic exposure. Contracts don’t cover machine learning dependencies. And suddenly, you're exposed - not by malice, but by outdated thinking. Mastering Third Party Risk Management in the Age of AI and Automation is your strategic playbook to turn chaos into control, ambiguity into authority, and risk into competitive advantage. This is not theory. It’s the step-by-step methodology professionals use to go from reactive compliance to proactive governance - and deliver a board-ready, AI-aware third-party risk program in 30 days or less. One senior risk officer used this exact framework to audit 217 vendors in six weeks, identifying three critical AI model dependencies that were previously untracked. The result? A 40% reduction in exposure and a promotion to Head of Digital Resilience. You don’t need more tools. You need a next-generation strategy. Here’s how this course is structured to help you get there.Course Format & Delivery Details This course is designed for leaders who need results - not hype. Everything is built for high-impact learning that fits your schedule and commands respect at the executive level. Self-Paced. Always Available. Fully Accessible.
Enroll once and gain immediate online access. This is an on-demand program with no fixed dates or time commitments. You progress at your own pace, on your own schedule, with full control over your learning path. The average learner completes the core curriculum in 28 hours and applies key risk strategies to real projects in under 10 days. - Lifetime access to all course materials, including every future update at no additional cost - we continuously evolve content as AI regulations, automation frameworks, and global standards change.
- Access from any device, anywhere in the world, 24/7. The platform is mobile-optimized, so you can review risk templates on your commute or refine audit strategies during a lunch break.
- Clear, step-by-step instruction using interactive diagnostics, real-world case studies, and decision frameworks - all text-based, no video distractions, built for deep comprehension and immediate implementation.
Direct Support. Trusted Certification.
You’re not learning in isolation. This course includes dedicated guidance from experienced third-party risk architects who specialise in AI governance and digital supply chain assurance. Ask questions, refine your approach, and validate your risk logic through structured review pathways built into the curriculum. Upon completion, you earn a Certificate of Completion issued by The Art of Service - a credential recognised by risk professionals across 62 countries. This certification signals mastery of modern risk frameworks, enhances your professional credibility, and strengthens your standing in internal audits, vendor negotiations, and board-level reporting. No Risk. Full Confidence. 100% Guarantee.
We eliminate buyer friction with a powerful promise: If this course doesn’t deliver measurable clarity, confidence, and career-moving value, you get a full refund - no questions asked. - Pricing is transparent with no hidden fees. What you see is exactly what you pay.
- We accept all major payment methods: Visa, Mastercard, PayPal - secure and globally accessible.
- After enrollment, you’ll receive a confirmation email. Your access credentials and course entry details will be sent separately once the learning environment is fully provisioned.
- You retain full control over your data and privacy at every stage.
This Works - Even If You Are:
- New to AI-driven risk models but expected to govern them confidently.
- Managing legacy systems while integrating automated third-party platforms.
- Overwhelmed by vendor sprawl, API dependencies, or algorithmic supply chain links.
- Required to report to compliance, audit, or executive teams with limited risk infrastructure.
This system works - even if your organisation hasn’t yet defined its AI ethics policy. Even if you’re auditing vendors who use black-box models. Even if you’re the only person in your team thinking ahead. Why? Because this course doesn’t teach generic compliance. It gives you the operational precision to map, measure, and mitigate risk in real time. The curriculum is battle-tested, industry-agnostic, and built on audit-proven methodologies trusted by financial services, healthcare, and cloud infrastructure leaders. Your confidence is our priority. Your results are non-negotiable.
Module 1: Foundations of Third Party Risk in the Digital Age - Defining third party risk in modern enterprise ecosystems
- Evolution from traditional vendor management to AI-integrated risk governance
- The convergence of cyber risk, supply chain integrity, and algorithmic exposure
- Understanding data sovereignty and jurisdictional risk in global outsourcing
- Key regulatory drivers: GDPR, CCPA, NIS2, DORA, and AI Act implications
- Types of third parties: cloud providers, SaaS platforms, AI model vendors, managed service partners
- High-risk vendor categorisation frameworks
- Vendor lifecycle management from onboarding to decommissioning
- The role of procurement, legal, and risk teams in coordinated oversight
- Common failure points in legacy third party risk programs
- Recognising blind spots in automated vendor integration
- Linking third party exposure to enterprise resilience metrics
Module 2: AI, Automation, and Emerging Technological Risk Vectors - How AI introduces new third party dependencies through pre-trained models
- Understanding API-based AI services and their risk surface
- Machine learning pipelines as shared responsibility models
- Risks associated with generative AI in content, customer service, and document processing
- Algorithmic bias and ethical exposure in third party AI tools
- Model drift and version control in external AI systems
- Automated decision-making and accountability gaps
- AI model interpretability and auditability challenges
- Vendor lock-in through AI-based workflows and automation scripts
- Data leakage risks in AI training processes using client data
- Automated bot networks and digital labour platforms as third parties
- Robotic Process Automation (RPA) tools and orchestration risks
- Monitoring AI-generated outputs for compliance and brand integrity
- Dependency on external data sources and real-time analytics feeds
- AI model certification and vendor attestation limitations
Module 3: Building a Future-Ready Third Party Risk Framework - Designing a scalable, AI-aware risk assessment methodology
- Integrating risk into digital transformation and innovation roadmaps
- Establishing a Third Party Risk Management (TPRM) steering committee
- Developing risk appetite statements for AI and automation adoption
- Creating a centralised vendor inventory with dynamic risk tagging
- Automated risk scoring and continuous monitoring integration
- Defining escalation protocols for high-risk AI vendor incidents
- Aligning TPRM with Enterprise Risk Management (ERM) objectives
- Drafting AI-specific risk thresholds and tolerance levels
- Using digital risk dashboards for real-time oversight
- Incorporating cyber resilience metrics into vendor evaluation
- Developing exit strategies and contingency plans for critical vendors
- Stress-testing vendor dependencies under disruption scenarios
- Implementing adaptive control frameworks (e.g. NIST, ISO 27001, CSA CCM)
- Mapping vendor relationships to business continuity plans
Module 4: Risk Assessment and Due Diligence Methodologies - Designing custom due diligence questionnaires for AI vendors
- Conducting deep technical assessments of algorithmic transparency
- Reviewing AI model documentation, training data sources, and validation reports
- Assessing vendor cybersecurity posture using standard benchmarks
- Evaluating AI fairness, accountability, and transparency (FAT) practices
- Analysing vendor incident response and breach notification capabilities
- Verifying SOC 2, ISO, and other compliance certifications for validity and scope
- Third party penetration testing and red teaming coordination
- Assessing physical and personnel security controls at vendor locations
- Reviewing subcontractor management and downstream risk exposure
- Digital forensics readiness and data recovery assurances
- Testing failover mechanisms and resilience under load
- Data encryption standards in transit and at rest
- Access control policies and principle of least privilege enforcement
- Conducting multi-layered risk walkthroughs with vendor teams
- Using risk heat maps to visualise exposure across vendor portfolios
Module 5: Contracting and Legal Safeguards for AI Vendors - Drafting AI-specific contractual clauses for model performance guarantees
- Defining ownership and usage rights for AI-generated content
- Liability allocation for algorithmic errors and biased outputs
- Data processing agreements aligned with global privacy standards
- Right-to-audit clauses with technical access to AI systems
- IP protection in machine learning models trained on client data
- Warranty provisions for model accuracy, reliability, and drift monitoring
- Penalty structures for service level agreement (SLA) violations
- Exit assistance and data portability commitments
- Subprocessor transparency and approval workflows
- Force majeure and business continuity obligations
- Insurance requirements for AI liability and cyber events
- Geographic restrictions on data processing and model hosting
- Conflict resolution mechanisms and jurisdiction clauses
- Ensuring enforceability across multinational vendor relationships
Module 6: Continuous Monitoring and Real-Time Risk Intelligence - Implementing automated monitoring for AI vendor performance and anomalies
- Integrating dark web and breach intelligence feeds into vendor oversight
- Using digital risk protection platforms for brand and data exposure
- Monitoring social media and public sentiment for vendor reputation risk
- Tracking software bill of materials (SBOMs) in third party AI tools
- Setting up keyword alerts for executive changes or financial instability
- Using AI to analyse vendor communication patterns for risk indicators
- Integrating security telemetry from vendor APIs into SIEM systems
- Automated compliance validation via policy-as-code frameworks
- Monitoring environmental, social, and governance (ESG) risks in supply chains
- Linking vendor ESG performance to AI ethics commitments
- Alert fatigue reduction through intelligent prioritisation rules
- Creating custom risk scorecards with dynamic weightings
- Scheduling periodic reassessment cycles based on risk tier
- Using anomaly detection to identify emerging automation risks
Module 7: AI Risk Governance and Board-Level Reporting - Translating technical risk insights into executive summaries
- Developing a board-level Third Party Risk dashboard
- Measuring and reporting on AI risk exposure KPIs
- Creating risk appetite alignment documents for audit committees
- Presenting risk treatment progress and mitigation milestones
- Communicating residual risk and escalation decisions
- Linking AI vendor risk to strategic business objectives
- Preparing concise, evidence-based incident disclosure narratives
- Using visual risk storytelling techniques for stakeholder engagement
- Demonstrating compliance with AI regulatory expectations
- Justifying investment in risk mitigation and control enhancements
- Documenting governance decisions for audit trails
- Integrating third party risk into enterprise risk registers
- Reporting on vendor concentration and single points of failure
- Mapping risk exposure to financial impact and insurance coverage
Module 8: Practical Application and Hands-On Risk Projects - Conducting a full risk assessment on a simulated AI vendor
- Building a vendor risk scoring model from scratch
- Drafting a board-ready third party risk briefing document
- Creating an audit playbook for AI model vendors
- Designing a vendor offboarding checklist with data sanitisation steps
- Running a tabletop exercise for AI failure scenario
- Developing a crisis communication plan for third party incidents
- Mapping critical vendor dependencies across business units
- Creating a risk-adjusted vendor prioritisation inventory
- Writing an AI ethics addendum for vendor contracts
- Developing a continuous monitoring implementation plan
- Generating a vendor risk maturity assessment report
- Designing a training program for procurement teams on AI risks
- Building a risk-aware procurement playbook
- Simulating a regulatory examination of third party controls
- Conducting a gap analysis against industry best practices
Module 9: Advanced Integration with Cybersecurity and Compliance - Integrating TPRM with Zero Trust Architecture principles
- Extending identity and access management to third party systems
- Securing API gateways and microservices in vendor ecosystems
- Implementing mutual TLS and certificate-based authentication
- Using multi-factor authentication (MFA) for vendor access portals
- Enforcing device compliance for vendor staff accessing corporate systems
- Monitoring for lateral movement risks from compromised vendors
- Securing AI model endpoints against adversarial attacks
- Validating model inputs to prevent prompt injection and data poisoning
- Linking vendor risk to cyber insurance underwriting requirements
- Aligning vendor controls with MITRE ATT&CK framework
- Integrating vendor risk into incident response playbooks
- Testing vendor containment procedures during breach simulations
- Ensuring log retention and forensic readiness across vendor platforms
- Conducting joint disaster recovery drills with critical vendors
Module 10: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: criteria and structure
- Submitting your completed third party risk program portfolio
- Receiving feedback from expert reviewers
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn, resume, and professional profiles
- Accessing exclusive alumni resources and industry updates
- Joining a global network of certified Third Party Risk professionals
- Identifying career advancement opportunities in AI governance
- Pursuing specialisation paths: AI ethics officer, digital supply chain auditor, TPRM lead
- Leveraging certification in salary negotiations and promotions
- Using your risk program as a live case study in job interviews
- Accessing continued learning modules on emerging AI regulations
- Participating in peer review forums and benchmarking exercises
- Contributing to open-source risk frameworks and templates
- Staying ahead of automation risk trends with curated intelligence briefings
- Building a personal brand as a future-ready risk leader
- Guiding organisational transformation with confidence and credibility
- Establishing yourself as the go-to expert in AI-era risk management
- Defining third party risk in modern enterprise ecosystems
- Evolution from traditional vendor management to AI-integrated risk governance
- The convergence of cyber risk, supply chain integrity, and algorithmic exposure
- Understanding data sovereignty and jurisdictional risk in global outsourcing
- Key regulatory drivers: GDPR, CCPA, NIS2, DORA, and AI Act implications
- Types of third parties: cloud providers, SaaS platforms, AI model vendors, managed service partners
- High-risk vendor categorisation frameworks
- Vendor lifecycle management from onboarding to decommissioning
- The role of procurement, legal, and risk teams in coordinated oversight
- Common failure points in legacy third party risk programs
- Recognising blind spots in automated vendor integration
- Linking third party exposure to enterprise resilience metrics
Module 2: AI, Automation, and Emerging Technological Risk Vectors - How AI introduces new third party dependencies through pre-trained models
- Understanding API-based AI services and their risk surface
- Machine learning pipelines as shared responsibility models
- Risks associated with generative AI in content, customer service, and document processing
- Algorithmic bias and ethical exposure in third party AI tools
- Model drift and version control in external AI systems
- Automated decision-making and accountability gaps
- AI model interpretability and auditability challenges
- Vendor lock-in through AI-based workflows and automation scripts
- Data leakage risks in AI training processes using client data
- Automated bot networks and digital labour platforms as third parties
- Robotic Process Automation (RPA) tools and orchestration risks
- Monitoring AI-generated outputs for compliance and brand integrity
- Dependency on external data sources and real-time analytics feeds
- AI model certification and vendor attestation limitations
Module 3: Building a Future-Ready Third Party Risk Framework - Designing a scalable, AI-aware risk assessment methodology
- Integrating risk into digital transformation and innovation roadmaps
- Establishing a Third Party Risk Management (TPRM) steering committee
- Developing risk appetite statements for AI and automation adoption
- Creating a centralised vendor inventory with dynamic risk tagging
- Automated risk scoring and continuous monitoring integration
- Defining escalation protocols for high-risk AI vendor incidents
- Aligning TPRM with Enterprise Risk Management (ERM) objectives
- Drafting AI-specific risk thresholds and tolerance levels
- Using digital risk dashboards for real-time oversight
- Incorporating cyber resilience metrics into vendor evaluation
- Developing exit strategies and contingency plans for critical vendors
- Stress-testing vendor dependencies under disruption scenarios
- Implementing adaptive control frameworks (e.g. NIST, ISO 27001, CSA CCM)
- Mapping vendor relationships to business continuity plans
Module 4: Risk Assessment and Due Diligence Methodologies - Designing custom due diligence questionnaires for AI vendors
- Conducting deep technical assessments of algorithmic transparency
- Reviewing AI model documentation, training data sources, and validation reports
- Assessing vendor cybersecurity posture using standard benchmarks
- Evaluating AI fairness, accountability, and transparency (FAT) practices
- Analysing vendor incident response and breach notification capabilities
- Verifying SOC 2, ISO, and other compliance certifications for validity and scope
- Third party penetration testing and red teaming coordination
- Assessing physical and personnel security controls at vendor locations
- Reviewing subcontractor management and downstream risk exposure
- Digital forensics readiness and data recovery assurances
- Testing failover mechanisms and resilience under load
- Data encryption standards in transit and at rest
- Access control policies and principle of least privilege enforcement
- Conducting multi-layered risk walkthroughs with vendor teams
- Using risk heat maps to visualise exposure across vendor portfolios
Module 5: Contracting and Legal Safeguards for AI Vendors - Drafting AI-specific contractual clauses for model performance guarantees
- Defining ownership and usage rights for AI-generated content
- Liability allocation for algorithmic errors and biased outputs
- Data processing agreements aligned with global privacy standards
- Right-to-audit clauses with technical access to AI systems
- IP protection in machine learning models trained on client data
- Warranty provisions for model accuracy, reliability, and drift monitoring
- Penalty structures for service level agreement (SLA) violations
- Exit assistance and data portability commitments
- Subprocessor transparency and approval workflows
- Force majeure and business continuity obligations
- Insurance requirements for AI liability and cyber events
- Geographic restrictions on data processing and model hosting
- Conflict resolution mechanisms and jurisdiction clauses
- Ensuring enforceability across multinational vendor relationships
Module 6: Continuous Monitoring and Real-Time Risk Intelligence - Implementing automated monitoring for AI vendor performance and anomalies
- Integrating dark web and breach intelligence feeds into vendor oversight
- Using digital risk protection platforms for brand and data exposure
- Monitoring social media and public sentiment for vendor reputation risk
- Tracking software bill of materials (SBOMs) in third party AI tools
- Setting up keyword alerts for executive changes or financial instability
- Using AI to analyse vendor communication patterns for risk indicators
- Integrating security telemetry from vendor APIs into SIEM systems
- Automated compliance validation via policy-as-code frameworks
- Monitoring environmental, social, and governance (ESG) risks in supply chains
- Linking vendor ESG performance to AI ethics commitments
- Alert fatigue reduction through intelligent prioritisation rules
- Creating custom risk scorecards with dynamic weightings
- Scheduling periodic reassessment cycles based on risk tier
- Using anomaly detection to identify emerging automation risks
Module 7: AI Risk Governance and Board-Level Reporting - Translating technical risk insights into executive summaries
- Developing a board-level Third Party Risk dashboard
- Measuring and reporting on AI risk exposure KPIs
- Creating risk appetite alignment documents for audit committees
- Presenting risk treatment progress and mitigation milestones
- Communicating residual risk and escalation decisions
- Linking AI vendor risk to strategic business objectives
- Preparing concise, evidence-based incident disclosure narratives
- Using visual risk storytelling techniques for stakeholder engagement
- Demonstrating compliance with AI regulatory expectations
- Justifying investment in risk mitigation and control enhancements
- Documenting governance decisions for audit trails
- Integrating third party risk into enterprise risk registers
- Reporting on vendor concentration and single points of failure
- Mapping risk exposure to financial impact and insurance coverage
Module 8: Practical Application and Hands-On Risk Projects - Conducting a full risk assessment on a simulated AI vendor
- Building a vendor risk scoring model from scratch
- Drafting a board-ready third party risk briefing document
- Creating an audit playbook for AI model vendors
- Designing a vendor offboarding checklist with data sanitisation steps
- Running a tabletop exercise for AI failure scenario
- Developing a crisis communication plan for third party incidents
- Mapping critical vendor dependencies across business units
- Creating a risk-adjusted vendor prioritisation inventory
- Writing an AI ethics addendum for vendor contracts
- Developing a continuous monitoring implementation plan
- Generating a vendor risk maturity assessment report
- Designing a training program for procurement teams on AI risks
- Building a risk-aware procurement playbook
- Simulating a regulatory examination of third party controls
- Conducting a gap analysis against industry best practices
Module 9: Advanced Integration with Cybersecurity and Compliance - Integrating TPRM with Zero Trust Architecture principles
- Extending identity and access management to third party systems
- Securing API gateways and microservices in vendor ecosystems
- Implementing mutual TLS and certificate-based authentication
- Using multi-factor authentication (MFA) for vendor access portals
- Enforcing device compliance for vendor staff accessing corporate systems
- Monitoring for lateral movement risks from compromised vendors
- Securing AI model endpoints against adversarial attacks
- Validating model inputs to prevent prompt injection and data poisoning
- Linking vendor risk to cyber insurance underwriting requirements
- Aligning vendor controls with MITRE ATT&CK framework
- Integrating vendor risk into incident response playbooks
- Testing vendor containment procedures during breach simulations
- Ensuring log retention and forensic readiness across vendor platforms
- Conducting joint disaster recovery drills with critical vendors
Module 10: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: criteria and structure
- Submitting your completed third party risk program portfolio
- Receiving feedback from expert reviewers
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn, resume, and professional profiles
- Accessing exclusive alumni resources and industry updates
- Joining a global network of certified Third Party Risk professionals
- Identifying career advancement opportunities in AI governance
- Pursuing specialisation paths: AI ethics officer, digital supply chain auditor, TPRM lead
- Leveraging certification in salary negotiations and promotions
- Using your risk program as a live case study in job interviews
- Accessing continued learning modules on emerging AI regulations
- Participating in peer review forums and benchmarking exercises
- Contributing to open-source risk frameworks and templates
- Staying ahead of automation risk trends with curated intelligence briefings
- Building a personal brand as a future-ready risk leader
- Guiding organisational transformation with confidence and credibility
- Establishing yourself as the go-to expert in AI-era risk management
- Designing a scalable, AI-aware risk assessment methodology
- Integrating risk into digital transformation and innovation roadmaps
- Establishing a Third Party Risk Management (TPRM) steering committee
- Developing risk appetite statements for AI and automation adoption
- Creating a centralised vendor inventory with dynamic risk tagging
- Automated risk scoring and continuous monitoring integration
- Defining escalation protocols for high-risk AI vendor incidents
- Aligning TPRM with Enterprise Risk Management (ERM) objectives
- Drafting AI-specific risk thresholds and tolerance levels
- Using digital risk dashboards for real-time oversight
- Incorporating cyber resilience metrics into vendor evaluation
- Developing exit strategies and contingency plans for critical vendors
- Stress-testing vendor dependencies under disruption scenarios
- Implementing adaptive control frameworks (e.g. NIST, ISO 27001, CSA CCM)
- Mapping vendor relationships to business continuity plans
Module 4: Risk Assessment and Due Diligence Methodologies - Designing custom due diligence questionnaires for AI vendors
- Conducting deep technical assessments of algorithmic transparency
- Reviewing AI model documentation, training data sources, and validation reports
- Assessing vendor cybersecurity posture using standard benchmarks
- Evaluating AI fairness, accountability, and transparency (FAT) practices
- Analysing vendor incident response and breach notification capabilities
- Verifying SOC 2, ISO, and other compliance certifications for validity and scope
- Third party penetration testing and red teaming coordination
- Assessing physical and personnel security controls at vendor locations
- Reviewing subcontractor management and downstream risk exposure
- Digital forensics readiness and data recovery assurances
- Testing failover mechanisms and resilience under load
- Data encryption standards in transit and at rest
- Access control policies and principle of least privilege enforcement
- Conducting multi-layered risk walkthroughs with vendor teams
- Using risk heat maps to visualise exposure across vendor portfolios
Module 5: Contracting and Legal Safeguards for AI Vendors - Drafting AI-specific contractual clauses for model performance guarantees
- Defining ownership and usage rights for AI-generated content
- Liability allocation for algorithmic errors and biased outputs
- Data processing agreements aligned with global privacy standards
- Right-to-audit clauses with technical access to AI systems
- IP protection in machine learning models trained on client data
- Warranty provisions for model accuracy, reliability, and drift monitoring
- Penalty structures for service level agreement (SLA) violations
- Exit assistance and data portability commitments
- Subprocessor transparency and approval workflows
- Force majeure and business continuity obligations
- Insurance requirements for AI liability and cyber events
- Geographic restrictions on data processing and model hosting
- Conflict resolution mechanisms and jurisdiction clauses
- Ensuring enforceability across multinational vendor relationships
Module 6: Continuous Monitoring and Real-Time Risk Intelligence - Implementing automated monitoring for AI vendor performance and anomalies
- Integrating dark web and breach intelligence feeds into vendor oversight
- Using digital risk protection platforms for brand and data exposure
- Monitoring social media and public sentiment for vendor reputation risk
- Tracking software bill of materials (SBOMs) in third party AI tools
- Setting up keyword alerts for executive changes or financial instability
- Using AI to analyse vendor communication patterns for risk indicators
- Integrating security telemetry from vendor APIs into SIEM systems
- Automated compliance validation via policy-as-code frameworks
- Monitoring environmental, social, and governance (ESG) risks in supply chains
- Linking vendor ESG performance to AI ethics commitments
- Alert fatigue reduction through intelligent prioritisation rules
- Creating custom risk scorecards with dynamic weightings
- Scheduling periodic reassessment cycles based on risk tier
- Using anomaly detection to identify emerging automation risks
Module 7: AI Risk Governance and Board-Level Reporting - Translating technical risk insights into executive summaries
- Developing a board-level Third Party Risk dashboard
- Measuring and reporting on AI risk exposure KPIs
- Creating risk appetite alignment documents for audit committees
- Presenting risk treatment progress and mitigation milestones
- Communicating residual risk and escalation decisions
- Linking AI vendor risk to strategic business objectives
- Preparing concise, evidence-based incident disclosure narratives
- Using visual risk storytelling techniques for stakeholder engagement
- Demonstrating compliance with AI regulatory expectations
- Justifying investment in risk mitigation and control enhancements
- Documenting governance decisions for audit trails
- Integrating third party risk into enterprise risk registers
- Reporting on vendor concentration and single points of failure
- Mapping risk exposure to financial impact and insurance coverage
Module 8: Practical Application and Hands-On Risk Projects - Conducting a full risk assessment on a simulated AI vendor
- Building a vendor risk scoring model from scratch
- Drafting a board-ready third party risk briefing document
- Creating an audit playbook for AI model vendors
- Designing a vendor offboarding checklist with data sanitisation steps
- Running a tabletop exercise for AI failure scenario
- Developing a crisis communication plan for third party incidents
- Mapping critical vendor dependencies across business units
- Creating a risk-adjusted vendor prioritisation inventory
- Writing an AI ethics addendum for vendor contracts
- Developing a continuous monitoring implementation plan
- Generating a vendor risk maturity assessment report
- Designing a training program for procurement teams on AI risks
- Building a risk-aware procurement playbook
- Simulating a regulatory examination of third party controls
- Conducting a gap analysis against industry best practices
Module 9: Advanced Integration with Cybersecurity and Compliance - Integrating TPRM with Zero Trust Architecture principles
- Extending identity and access management to third party systems
- Securing API gateways and microservices in vendor ecosystems
- Implementing mutual TLS and certificate-based authentication
- Using multi-factor authentication (MFA) for vendor access portals
- Enforcing device compliance for vendor staff accessing corporate systems
- Monitoring for lateral movement risks from compromised vendors
- Securing AI model endpoints against adversarial attacks
- Validating model inputs to prevent prompt injection and data poisoning
- Linking vendor risk to cyber insurance underwriting requirements
- Aligning vendor controls with MITRE ATT&CK framework
- Integrating vendor risk into incident response playbooks
- Testing vendor containment procedures during breach simulations
- Ensuring log retention and forensic readiness across vendor platforms
- Conducting joint disaster recovery drills with critical vendors
Module 10: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: criteria and structure
- Submitting your completed third party risk program portfolio
- Receiving feedback from expert reviewers
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn, resume, and professional profiles
- Accessing exclusive alumni resources and industry updates
- Joining a global network of certified Third Party Risk professionals
- Identifying career advancement opportunities in AI governance
- Pursuing specialisation paths: AI ethics officer, digital supply chain auditor, TPRM lead
- Leveraging certification in salary negotiations and promotions
- Using your risk program as a live case study in job interviews
- Accessing continued learning modules on emerging AI regulations
- Participating in peer review forums and benchmarking exercises
- Contributing to open-source risk frameworks and templates
- Staying ahead of automation risk trends with curated intelligence briefings
- Building a personal brand as a future-ready risk leader
- Guiding organisational transformation with confidence and credibility
- Establishing yourself as the go-to expert in AI-era risk management
- Drafting AI-specific contractual clauses for model performance guarantees
- Defining ownership and usage rights for AI-generated content
- Liability allocation for algorithmic errors and biased outputs
- Data processing agreements aligned with global privacy standards
- Right-to-audit clauses with technical access to AI systems
- IP protection in machine learning models trained on client data
- Warranty provisions for model accuracy, reliability, and drift monitoring
- Penalty structures for service level agreement (SLA) violations
- Exit assistance and data portability commitments
- Subprocessor transparency and approval workflows
- Force majeure and business continuity obligations
- Insurance requirements for AI liability and cyber events
- Geographic restrictions on data processing and model hosting
- Conflict resolution mechanisms and jurisdiction clauses
- Ensuring enforceability across multinational vendor relationships
Module 6: Continuous Monitoring and Real-Time Risk Intelligence - Implementing automated monitoring for AI vendor performance and anomalies
- Integrating dark web and breach intelligence feeds into vendor oversight
- Using digital risk protection platforms for brand and data exposure
- Monitoring social media and public sentiment for vendor reputation risk
- Tracking software bill of materials (SBOMs) in third party AI tools
- Setting up keyword alerts for executive changes or financial instability
- Using AI to analyse vendor communication patterns for risk indicators
- Integrating security telemetry from vendor APIs into SIEM systems
- Automated compliance validation via policy-as-code frameworks
- Monitoring environmental, social, and governance (ESG) risks in supply chains
- Linking vendor ESG performance to AI ethics commitments
- Alert fatigue reduction through intelligent prioritisation rules
- Creating custom risk scorecards with dynamic weightings
- Scheduling periodic reassessment cycles based on risk tier
- Using anomaly detection to identify emerging automation risks
Module 7: AI Risk Governance and Board-Level Reporting - Translating technical risk insights into executive summaries
- Developing a board-level Third Party Risk dashboard
- Measuring and reporting on AI risk exposure KPIs
- Creating risk appetite alignment documents for audit committees
- Presenting risk treatment progress and mitigation milestones
- Communicating residual risk and escalation decisions
- Linking AI vendor risk to strategic business objectives
- Preparing concise, evidence-based incident disclosure narratives
- Using visual risk storytelling techniques for stakeholder engagement
- Demonstrating compliance with AI regulatory expectations
- Justifying investment in risk mitigation and control enhancements
- Documenting governance decisions for audit trails
- Integrating third party risk into enterprise risk registers
- Reporting on vendor concentration and single points of failure
- Mapping risk exposure to financial impact and insurance coverage
Module 8: Practical Application and Hands-On Risk Projects - Conducting a full risk assessment on a simulated AI vendor
- Building a vendor risk scoring model from scratch
- Drafting a board-ready third party risk briefing document
- Creating an audit playbook for AI model vendors
- Designing a vendor offboarding checklist with data sanitisation steps
- Running a tabletop exercise for AI failure scenario
- Developing a crisis communication plan for third party incidents
- Mapping critical vendor dependencies across business units
- Creating a risk-adjusted vendor prioritisation inventory
- Writing an AI ethics addendum for vendor contracts
- Developing a continuous monitoring implementation plan
- Generating a vendor risk maturity assessment report
- Designing a training program for procurement teams on AI risks
- Building a risk-aware procurement playbook
- Simulating a regulatory examination of third party controls
- Conducting a gap analysis against industry best practices
Module 9: Advanced Integration with Cybersecurity and Compliance - Integrating TPRM with Zero Trust Architecture principles
- Extending identity and access management to third party systems
- Securing API gateways and microservices in vendor ecosystems
- Implementing mutual TLS and certificate-based authentication
- Using multi-factor authentication (MFA) for vendor access portals
- Enforcing device compliance for vendor staff accessing corporate systems
- Monitoring for lateral movement risks from compromised vendors
- Securing AI model endpoints against adversarial attacks
- Validating model inputs to prevent prompt injection and data poisoning
- Linking vendor risk to cyber insurance underwriting requirements
- Aligning vendor controls with MITRE ATT&CK framework
- Integrating vendor risk into incident response playbooks
- Testing vendor containment procedures during breach simulations
- Ensuring log retention and forensic readiness across vendor platforms
- Conducting joint disaster recovery drills with critical vendors
Module 10: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: criteria and structure
- Submitting your completed third party risk program portfolio
- Receiving feedback from expert reviewers
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn, resume, and professional profiles
- Accessing exclusive alumni resources and industry updates
- Joining a global network of certified Third Party Risk professionals
- Identifying career advancement opportunities in AI governance
- Pursuing specialisation paths: AI ethics officer, digital supply chain auditor, TPRM lead
- Leveraging certification in salary negotiations and promotions
- Using your risk program as a live case study in job interviews
- Accessing continued learning modules on emerging AI regulations
- Participating in peer review forums and benchmarking exercises
- Contributing to open-source risk frameworks and templates
- Staying ahead of automation risk trends with curated intelligence briefings
- Building a personal brand as a future-ready risk leader
- Guiding organisational transformation with confidence and credibility
- Establishing yourself as the go-to expert in AI-era risk management
- Translating technical risk insights into executive summaries
- Developing a board-level Third Party Risk dashboard
- Measuring and reporting on AI risk exposure KPIs
- Creating risk appetite alignment documents for audit committees
- Presenting risk treatment progress and mitigation milestones
- Communicating residual risk and escalation decisions
- Linking AI vendor risk to strategic business objectives
- Preparing concise, evidence-based incident disclosure narratives
- Using visual risk storytelling techniques for stakeholder engagement
- Demonstrating compliance with AI regulatory expectations
- Justifying investment in risk mitigation and control enhancements
- Documenting governance decisions for audit trails
- Integrating third party risk into enterprise risk registers
- Reporting on vendor concentration and single points of failure
- Mapping risk exposure to financial impact and insurance coverage
Module 8: Practical Application and Hands-On Risk Projects - Conducting a full risk assessment on a simulated AI vendor
- Building a vendor risk scoring model from scratch
- Drafting a board-ready third party risk briefing document
- Creating an audit playbook for AI model vendors
- Designing a vendor offboarding checklist with data sanitisation steps
- Running a tabletop exercise for AI failure scenario
- Developing a crisis communication plan for third party incidents
- Mapping critical vendor dependencies across business units
- Creating a risk-adjusted vendor prioritisation inventory
- Writing an AI ethics addendum for vendor contracts
- Developing a continuous monitoring implementation plan
- Generating a vendor risk maturity assessment report
- Designing a training program for procurement teams on AI risks
- Building a risk-aware procurement playbook
- Simulating a regulatory examination of third party controls
- Conducting a gap analysis against industry best practices
Module 9: Advanced Integration with Cybersecurity and Compliance - Integrating TPRM with Zero Trust Architecture principles
- Extending identity and access management to third party systems
- Securing API gateways and microservices in vendor ecosystems
- Implementing mutual TLS and certificate-based authentication
- Using multi-factor authentication (MFA) for vendor access portals
- Enforcing device compliance for vendor staff accessing corporate systems
- Monitoring for lateral movement risks from compromised vendors
- Securing AI model endpoints against adversarial attacks
- Validating model inputs to prevent prompt injection and data poisoning
- Linking vendor risk to cyber insurance underwriting requirements
- Aligning vendor controls with MITRE ATT&CK framework
- Integrating vendor risk into incident response playbooks
- Testing vendor containment procedures during breach simulations
- Ensuring log retention and forensic readiness across vendor platforms
- Conducting joint disaster recovery drills with critical vendors
Module 10: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: criteria and structure
- Submitting your completed third party risk program portfolio
- Receiving feedback from expert reviewers
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn, resume, and professional profiles
- Accessing exclusive alumni resources and industry updates
- Joining a global network of certified Third Party Risk professionals
- Identifying career advancement opportunities in AI governance
- Pursuing specialisation paths: AI ethics officer, digital supply chain auditor, TPRM lead
- Leveraging certification in salary negotiations and promotions
- Using your risk program as a live case study in job interviews
- Accessing continued learning modules on emerging AI regulations
- Participating in peer review forums and benchmarking exercises
- Contributing to open-source risk frameworks and templates
- Staying ahead of automation risk trends with curated intelligence briefings
- Building a personal brand as a future-ready risk leader
- Guiding organisational transformation with confidence and credibility
- Establishing yourself as the go-to expert in AI-era risk management
- Integrating TPRM with Zero Trust Architecture principles
- Extending identity and access management to third party systems
- Securing API gateways and microservices in vendor ecosystems
- Implementing mutual TLS and certificate-based authentication
- Using multi-factor authentication (MFA) for vendor access portals
- Enforcing device compliance for vendor staff accessing corporate systems
- Monitoring for lateral movement risks from compromised vendors
- Securing AI model endpoints against adversarial attacks
- Validating model inputs to prevent prompt injection and data poisoning
- Linking vendor risk to cyber insurance underwriting requirements
- Aligning vendor controls with MITRE ATT&CK framework
- Integrating vendor risk into incident response playbooks
- Testing vendor containment procedures during breach simulations
- Ensuring log retention and forensic readiness across vendor platforms
- Conducting joint disaster recovery drills with critical vendors