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SEC0420 Embedding Third-Party Risk Controls in AI-Native Security Operations

$199.00
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A tailored course, built for your situation

Embedding Third-Party Risk Controls in AI-Native Security Operations

Implementation-grade controls for third-party AI risk in modern security operations

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Pre-audit crunch over AI vendor evidence

The situation this course is for

Security leaders face mounting pressure to prove third-party AI tools meet governance standards, but evidence collection remains reactive, manual, and prone to last-minute rework during regulatory cycles.

Who this is for

Chief Information Security Officer in a tech-forward organization managing AI adoption, third-party risk, and compliance readiness across complex attack surfaces.

Who this is not for

Individuals seeking high-level AI ethics frameworks or introductory compliance content.

What you walk away with

  • Design auditable third-party risk controls using COBIT principles tailored to AI-native environments
  • Build reusable evidence packages that withstand regulator scrutiny
  • Reduce pre-audit rework time by streamlining vendor attestation workflows
  • Align AI vendor risk decisions with executive-level governance expectations
  • Shift from reactive firefighting to proactive control embedding in procurement cycles

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI-Native Third-Party Risk
Understand the unique risks introduced by AI vendors in security operations and why traditional TPRM fails.
12 chapters in this module
  1. Defining AI-native systems in enterprise security contexts
  2. How AI vendors expand the attack surface differently than SaaS tools
  3. Common failure points in AI vendor due diligence
  4. Regulatory expectations for algorithmic transparency and control
  5. Case study: AI tool misconfiguration leading to data exposure
  6. Differences between AI model providers and AI-enabled software vendors
  7. Mapping vendor AI usage across your current tech stack
  8. Key questions to ask AI vendors during initial scoping
  9. Understanding model drift and its operational impact
  10. Vendor lock-in risks specific to proprietary AI platforms
  11. The role of training data provenance in risk assessment
  12. Establishing baseline expectations for AI vendor accountability
Module 2. COBIT Framework Alignment for AI Risk
Apply COBIT domains to govern AI third-party relationships effectively.
12 chapters in this module
  1. Overview of COBIT's relevance to emerging technology risk
  2. Mapping APO12 Manage Risk to AI vendor engagements
  3. Using MEA01 Monitor Performance for ongoing AI vendor oversight
  4. Aligning DSS06 Manage Business Process Controls with AI workflows
  5. Applying BAI09 Manage Assets to AI model inventory and tracking
  6. Integrating COBIT with NIST AI Risk Management Framework
  7. Customizing COBIT goals for AI-specific control objectives
  8. Translating COBIT processes into actionable vendor requirements
  9. Creating COBIT-based scorecards for AI vendor evaluation
  10. Linking COBIT practices to internal audit checklists
  11. Documenting AI risk decisions within COBIT governance structures
  12. Scaling COBIT application across multiple AI vendor contracts
Module 3. Control Design for AI Vendor Environments
Develop targeted controls that address AI-specific vulnerabilities.
12 chapters in this module
  1. Identifying critical data flows in AI vendor integrations
  2. Designing input validation controls for AI model prompts
  3. Output integrity checks for AI-generated decisions
  4. Implementing logging standards for AI model interactions
  5. Access control models for multi-tenant AI platforms
  6. Secure API key management for AI service connections
  7. Encryption strategies for data in transit to AI vendors
  8. Model versioning and change control requirements
  9. Bias detection mechanisms in production AI systems
  10. Fail-safe modes for AI decision support tools
  11. Incident response planning for AI vendor outages
  12. Automated anomaly detection in AI usage patterns
Module 4. Evidence Collection and Audit Readiness
Create defensible, reusable documentation for regulatory reviews.
12 chapters in this module
  1. Structuring audit evidence packages for AI vendor risk
  2. Documenting control effectiveness without vendor disclosure
  3. Using screenshots and logs as acceptable evidence
  4. Third-party attestations vs. direct testing approaches
  5. Preparing for unannounced regulator inquiries on AI tools
  6. Version-controlled evidence repositories for consistency
  7. Redaction techniques for sensitive vendor information
  8. Timeline documentation for AI incident investigations
  9. Cross-referencing controls to multiple regulatory requirements
  10. Staging mock audits for AI vendor risk programs
  11. Maintaining evidence freshness between audit cycles
  12. Role-based access to audit documentation systems
Module 5. Vendor Contracting and SLA Negotiation
Embed risk controls directly into procurement agreements.
12 chapters in this module
  1. Required clauses for AI vendor contracts around model updates
  2. Negotiating access to performance metrics and uptime data
  3. Including audit rights for AI system configurations
  4. Penalty structures for AI output inaccuracies
  5. Data ownership and deletion rights in AI processing
  6. Intellectual property considerations for fine-tuned models
  7. Exit strategies and data portability requirements
  8. Service level agreements for AI response times
  9. Force majeure provisions related to AI training disruptions
  10. Subprocessor transparency and approval workflows
  11. Insurance requirements for AI liability coverage
  12. Change notification timelines for AI model revisions
Module 6. Continuous Monitoring and Control Validation
Maintain assurance throughout the vendor lifecycle.
12 chapters in this module
  1. Automated scanning for unauthorized AI tool usage
  2. API-based health checks for integrated AI services
  3. User behavior analytics to detect AI misuse
  4. Benchmarking AI outputs against expected norms
  5. Scheduled reassessment triggers based on usage volume
  6. Threshold-based alerts for anomalous AI activity
  7. Quarterly control validation checklists
  8. Sampling methodologies for ongoing compliance
  9. Integrating AI risk monitoring into SIEM tools
  10. Dashboards for executive visibility on vendor risk status
  11. Feedback loops from security teams to procurement
  12. Updating risk ratings based on new threat intelligence
Module 7. Incident Response for AI Vendor Events
Respond effectively when AI systems fail or behave unexpectedly.
12 chapters in this module
  1. Classifying AI incidents: errors, bias, hallucinations, breaches
  2. Initial containment steps for compromised AI models
  3. Coordinating with vendors during active AI incidents
  4. Communicating AI failures to internal stakeholders
  5. Customer notification protocols for AI-driven mistakes
  6. Forensic data preservation from AI vendor platforms
  7. Root cause analysis methods for AI decision errors
  8. Regulatory reporting thresholds for AI incidents
  9. Post-mortem documentation specific to AI events
  10. Lessons learned integration into future vendor selection
  11. Simulating AI failure scenarios in tabletop exercises
  12. Escalation paths for high-severity AI disruptions
Module 8. Executive Communication and Governance Reporting
Translate technical AI risk into strategic insights.
12 chapters in this module
  1. Summarizing AI vendor risk for non-technical leaders
  2. Risk heat maps that highlight AI exposure areas
  3. Budget justification for AI risk mitigation investments
  4. Progress tracking on AI control implementation
  5. Benchmarking against peer organizations' AI maturity
  6. Presenting AI risk trends over time
  7. Connecting AI controls to business continuity planning
  8. Highlighting success stories in AI risk reduction
  9. Anticipating board-level questions on AI strategy
  10. Balancing innovation speed with risk tolerance
  11. Articulating residual risk after control implementation
  12. Positioning AI risk work as an enabler of trust
Module 9. Integration with Existing GRC Programs
Weave AI vendor risk into broader governance workflows.
12 chapters in this module
  1. Adding AI vendors to existing third-party risk registers
  2. Aligning AI risk assessments with overall risk appetite
  3. Incorporating AI into enterprise risk management reports
  4. Training GRC teams on AI-specific risk indicators
  5. Updating policies to include AI vendor considerations
  6. Synchronizing AI risk calendars with audit schedules
  7. Leveraging existing control frameworks for AI extensions
  8. Sharing AI risk data across compliance functions
  9. Consolidating findings from AI audits into master reports
  10. Using GRC platforms to track AI-related action items
  11. Standardizing terminology across AI and traditional risk teams
  12. Measuring program maturity using capability models
Module 10. Automation and Tooling for Scalability
Scale AI vendor risk management efficiently.
12 chapters in this module
  1. Selecting tools for automated AI vendor discovery
  2. Configuring workflow automation for risk assessments
  3. Integrating AI risk data into IT asset management
  4. Building custom dashboards for real-time monitoring
  5. Scripting routine evidence collection tasks
  6. Using AI to analyze vendor security questionnaires
  7. Automated reminder systems for contract renewals
  8. Orchestrating cross-team approvals digitally
  9. API integrations between GRC and procurement systems
  10. Natural language processing for policy gap analysis
  11. Machine learning models to predict vendor risk spikes
  12. Low-code solutions for rapid control prototyping
Module 11. M&A Integration and Vendor Rationalization
Manage AI vendor risk during organizational changes.
12 chapters in this module
  1. Assessing AI vendor exposure in target companies
  2. Due diligence checklists for AI-heavy acquisitions
  3. Consolidating overlapping AI vendor relationships
  4. Harmonizing control standards post-merger
  5. Decommissioning legacy AI systems securely
  6. Re-negotiating contracts under new corporate terms
  7. Integrating acquired AI tools into central monitoring
  8. Addressing licensing conflicts in merged environments
  9. Uncovering shadow AI usage during integration
  10. Standardizing AI development practices across teams
  11. Retraining staff on consolidated AI governance rules
  12. Measuring synergy benefits from vendor consolidation
Module 12. Future-Proofing and Emerging Threats
Stay ahead of evolving AI risk landscapes.
12 chapters in this module
  1. Tracking regulatory developments in AI governance
  2. Anticipating new attack vectors in generative AI
  3. Preparing for AI-powered adversary tools
  4. Quantum computing implications for AI encryption
  5. Ethical AI certification programs and their value
  6. Supply chain attacks targeting open-source AI models
  7. Deepfake detection requirements for identity verification
  8. Autonomous agent coordination risks
  9. Regulatory sandboxes for experimental AI use cases
  10. International jurisdiction challenges in AI enforcement
  11. Long-term model degradation monitoring
  12. Succession planning for AI system maintainers

How this maps to your situation

  • Pre-audit evidence preparation
  • Vendor contract negotiation
  • Ongoing monitoring and alerting
  • Executive reporting and budget justification

Before vs. after

Before
Manual, reactive efforts to compile AI vendor risk evidence under time pressure, leading to inconsistent documentation and audit delays.
After
A structured, repeatable process for embedding COBIT-aligned controls into AI vendor management, producing auditable outcomes with minimal rework.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 18, 24 hours of focused learning, designed for completion in short sessions over several weeks.

If nothing changes
Without a systematic approach, organizations face increased audit findings, regulatory penalties, and operational disruptions from uncontrolled AI vendor usage.

How this compares to the alternatives

Unlike generic AI ethics courses or broad TPRM overviews, this program delivers implementation-grade control designs specifically for AI-native security operations using COBIT as the governing framework.

Frequently asked

Is this course focused on technical implementation or strategic oversight?
It bridges both, providing strategic alignment through COBIT while delivering concrete control designs and evidence templates for operational use.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-COBIT environments?
Yes, the control concepts are transferable, though the course uses COBIT as the primary governance anchor for consistency and audit alignment.
$199 one-time. Approximately 18, 24 hours of focused learning, designed for completion in short sessions over several weeks..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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