A tailored course, built for your situation
Mastering AI Governance Frameworks for Defense Product Managers
A step-by-step system to command the structure, controls, and compliance lifecycle behind AI-driven defense products
$199 one-time
30-day money-back guarantee
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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.
Stop the last-minute audit scramble with a repeatable AI governance evidence package
The situation this course is for
Product managers in defense tech are caught between rapid AI integration and tightening compliance cycles. The result? High-effort, reactive audit narratives built from fragmented inputs. This course eliminates that drag by giving you a structured, reusable method to design governance into the product lifecycle from day one.
Who this is for
Senior product leaders in defense, aerospace, or government-contracting firms who own AI-enabled systems and must reconcile innovation speed with regulatory scrutiny
Who this is not for
Entry-level PMs, non-technical product owners, or those working in non-regulated commercial AI spaces will not get targeted value from this course
What you walk away with
- Map AI governance controls directly to product requirements and system design decisions
- Produce regulator-ready audit narratives without cross-team rework
- Anticipate compliance gaps during sprint planning, not pre-audit
- Own the AI risk register with framework-backed justification
- Align engineering teams on evidence collection timelines and formats
The 12 modules (with all 144 chapters)
Module 1. The AI Governance Landscape in Defense Contracting
Understand the current regulatory expectations from DoD, CMMC, and NIST AI RMF as they apply to product delivery teams.
12 chapters in this module
- Overview of AI governance in national security contexts
- Key differences between commercial and defense AI compliance
- How DoD AI Ethical Principles translate to product controls
- Mapping NIST AI RMF to product development phases
- Understanding CMMC 2.0 implications for AI-enabled systems
- Role of Section 809 recommendations in AI procurement
- Current enforcement focus areas from DIB SP team
- How classified vs unclassified AI systems differ in governance
- Common misconceptions about AI bias in defense applications
- Balancing innovation speed with auditability requirements
- The role of red teaming in AI product validation
- Preparing for future DoD AI certification frameworks
Module 2. From Policy to Product Requirements
Translate high-level AI governance mandates into actionable, testable product backlog items.
12 chapters in this module
- Decoding DoD AI policy documents for product relevance
- Extracting control objectives from compliance frameworks
- Writing user stories that embed governance by design
- Defining acceptance criteria for AI fairness and explainability
- Linking product decisions to AI risk categories
- Documenting rationale for model selection and training data
- Establishing traceability from requirement to evidence
- Integrating governance checkpoints into sprint planning
- Working with legal and compliance on boundary definitions
- Handling classified data in AI training pipelines
- Versioning governance requirements alongside product
- Creating living product documentation for auditors
Module 3. Control Mapping for AI Product Teams
Build a living control framework that aligns engineering output with compliance expectations.
12 chapters in this module
- Introduction to control mapping in AI product contexts
- Identifying existing controls in current product architecture
- Gap analysis between required and implemented controls
- Prioritizing controls by risk severity and audit likelihood
- Assigning control ownership across product and engineering
- Documenting control implementation in accessible formats
- Linking controls to system design documents and code
- Using diagrams to show control flow in AI systems
- Maintaining control maps through product iterations
- Automating control status reporting for leadership
- Preparing control evidence for third-party assessment
- Updating maps for new regulatory requirements
Module 4. Evidence Collection and Packaging
Design evidence collection workflows that minimize rework and maximize audit readiness.
12 chapters in this module
- Defining the minimum viable evidence package for AI products
- Scheduling evidence collection alongside development sprints
- Standardizing formats for model cards and data sheets
- Capturing training data provenance and lineage
- Documenting model performance across test environments
- Recording human oversight mechanisms and fail-safes
- Creating version-controlled evidence repositories
- Using automation to generate compliance reports
- Integrating evidence collection into CI/CD pipelines
- Handling classified evidence securely and efficiently
- Preparing evidence for external auditor consumption
- Maintaining evidence integrity through product lifecycle
Module 5. Audit Narrative Development
Craft compelling, evidence-backed narratives that satisfy regulator inquiries without rework.
12 chapters in this module
- Understanding what auditors look for in AI systems
- Structuring the narrative around control objectives
- Linking evidence to specific compliance requirements
- Writing clear, non-technical explanations of AI behavior
- Anticipating follow-up questions and preparing responses
- Using visual aids to demonstrate system safety
- Incorporating lessons from past audit findings
- Maintaining narrative consistency across product versions
- Collaborating with legal on sensitive disclosure language
- Versioning narratives alongside product updates
- Creating executive summaries for leadership review
- Archiving narratives for future reference and reuse
Module 6. Cross-Functional Alignment on AI Governance
Lead alignment between engineering, compliance, legal, and program management on governance expectations.
12 chapters in this module
- Identifying key stakeholders in AI governance workflows
- Establishing regular sync points across functions
- Creating shared vocabulary for AI risk and controls
- Facilitating joint control design sessions
- Resolving conflicts between speed and compliance
- Documenting agreements and decisions transparently
- Using RACI matrices for governance ownership
- Running tabletop exercises for incident response
- Incorporating feedback from compliance teams early
- Managing expectations around audit outcomes
- Building trust through consistent delivery
- Scaling alignment across multiple product lines
Module 7. AI Risk Register Management
Maintain a dynamic risk register that informs product decisions and demonstrates proactive governance.
12 chapters in this module
- Defining AI-specific risk categories for defense systems
- Establishing risk scoring criteria aligned with DoD standards
- Documenting risk mitigation strategies in product backlog
- Linking risks to specific control implementations
- Updating risk assessments after model retraining
- Communicating risk status to program leadership
- Using risk register to prioritize technical debt reduction
- Incorporating red team findings into risk assessments
- Maintaining version history of risk decisions
- Preparing risk register for auditor review
- Automating risk status reporting
- Retiring risks after successful control validation
Module 8. Governance in Agile Development Cycles
Embed governance practices into sprint planning, reviews, and retrospectives without slowing delivery.
12 chapters in this module
- Integrating governance into sprint planning meetings
- Defining governance-specific Definition of Done criteria
- Running governance-focused sprint reviews
- Using retrospectives to improve compliance workflows
- Balancing technical debt and governance debt
- Managing governance work during crunch periods
- Prioritizing governance tasks in product backlog
- Using story points for governance effort estimation
- Creating governance epics and themes
- Tracking governance velocity alongside feature velocity
- Adapting governance practices for rapid prototyping
- Scaling agile governance across multiple teams
Module 9. Vendor and Partner Governance Oversight
Extend governance controls to third-party AI components and subcontractors.
12 chapters in this module
- Assessing vendor AI governance maturity pre-contract
- Including governance requirements in RFPs and contracts
- Validating vendor control implementation
- Managing data sharing with third-party AI providers
- Auditing subcontractor AI development practices
- Handling open-source AI components in products
- Ensuring supply chain transparency for AI models
- Managing model updates from external vendors
- Establishing escalation paths for governance issues
- Conducting joint tabletop exercises with partners
- Documenting vendor governance oversight activities
- Preparing vendor evidence packages for auditors
Module 10. Incident Response and Model Monitoring
Design monitoring systems and response protocols for AI model degradation or failure.
12 chapters in this module
- Defining AI incident types in defense contexts
- Establishing model performance thresholds
- Creating real-time monitoring dashboards
- Setting up alerting for anomalous behavior
- Documenting incident response playbooks
- Conducting post-incident reviews and root cause analysis
- Updating models and controls after incidents
- Communicating incidents to stakeholders
- Maintaining audit trail of incident responses
- Testing response protocols through simulations
- Integrating monitoring into DevSecOps pipelines
- Archiving incident records for compliance
Module 11. Training and Knowledge Transfer
Scale governance understanding across product and engineering teams through structured training.
12 chapters in this module
- Assessing team knowledge gaps in AI governance
- Designing role-specific training modules
- Creating onboarding materials for new hires
- Running workshops on control implementation
- Developing quick-reference guides for engineers
- Using case studies from past audits and incidents
- Measuring training effectiveness through assessments
- Updating training materials for regulatory changes
- Creating internal certification programs
- Documenting training completion for auditors
- Scaling training across distributed teams
- Maintaining a living knowledge base
Module 12. Continuous Improvement and Future-Proofing
Build feedback loops that evolve your governance approach with changing technology and regulations.
12 chapters in this module
- Establishing metrics for governance effectiveness
- Collecting feedback from auditors and regulators
- Benchmarking against industry best practices
- Adapting to new DoD AI policy directives
- Incorporating lessons from red team exercises
- Updating control frameworks for emerging threats
- Planning for AI certification and accreditation
- Engaging with standards development organizations
- Anticipating future regulatory changes
- Building internal subject matter expertise
- Creating innovation sandboxes with guardrails
- Documenting evolution of governance program
How this maps to your situation
- Pre-audit preparation
- Cross-functional product delivery
- Regulator-facing documentation
- AI integration in defense systems
Before vs. after
Before
Spending 80+ hours assembling audit narratives from fragmented inputs across engineering, compliance, and program teams
After
Producing regulator-ready narratives in 6 hours using a structured, repeatable evidence package
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 90 minutes per week over 12 weeks, with the ability to accelerate through modules based on current needs.
If nothing changes
Without a structured approach, AI governance remains reactive, leading to last-minute scrambles, inconsistent evidence, and increased audit risk that could delay product fielding or certification.
How this compares to the alternatives
Unlike generic AI ethics courses or broad compliance overviews, this program delivers a tactical, product-specific framework used by leading defense contractors to ship AI systems with built-in auditability.
Frequently asked
Is this course focused on technical implementation or product leadership?
It's designed for product leaders who must bridge technical execution and compliance requirements. You'll learn how to structure governance so engineering teams can deliver, and auditors can verify.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with CMMC 2.0 AI requirements?
Yes, the course includes specific guidance on aligning AI product controls with CMMC 2.0 practices, especially in domains related to system and communications protection.
$199 one-time. Approximately 90 minutes per week over 12 weeks, with the ability to accelerate through modules based on current needs..
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