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AIG2358 Mastering AI Governance for National Security Program Leads

$199.00
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A tailored course, built for your situation

Mastering AI Governance for National Security Program Leads

Build repeatable, regulator-tested AI compliance frameworks that establish you as the internal authority across mission-critical initiatives

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
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.
Control documentation that requires rework under inspector general review cycles, especially when AI components weren’t architected with compliance baked in

The situation this course is for

Senior program leads at federal contractors are consistently reworking AI compliance packages during post-deployment audit prep, due to governance gaps introduced during rapid prototyping phases. These last-minute revisions erode credibility and delay follow-on funding.

Who this is for

Senior Program Manager or Technical Lead at a federal systems integrator, responsible for delivering AI-augmented national security solutions and ensuring compliance with DoD AI Ethical Principles, NIST AI RMF, and IG review standards

Who this is not for

Entry-level engineers, academic researchers, or commercial AI product teams without federal compliance exposure

What you walk away with

  • Produce regulator-ready AI governance packages in one draft
  • Establish consistent control mappings across AI use cases
  • Lead AI compliance without slowing delivery velocity
  • Serve as the internal reference when escalations arise
  • Shape program-wide AI governance standards from the delivery layer up

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Governance in National Security Contexts
Establish a working definition of AI governance tailored to federal mission assurance, distinguishing it from general IT compliance and aligning to DoD Directive 5000.01 and AI Ethical Principles.
12 chapters in this module
  1. Understanding the shift from experimental to accountable AI in defense programs
  2. Mapping federal AI policy directives to real-world project constraints
  3. Differentiating AI governance from legacy data governance frameworks
  4. Key components of a national security-oriented AI governance framework
  5. The role of the program lead in balancing innovation and compliance
  6. How IG scrutiny shapes AI system design decisions upfront
  7. Common failure points in AI governance during post-deployment audit
  8. Integrating ethical AI principles without slowing deployment
  9. Using NIST AI RMF as a baseline for federal program compliance
  10. Aligning AI governance with zero trust architecture initiatives
  11. Tracking AI-specific risk vectors like model drift and adversarial inputs
  12. Setting governance scope boundaries for multi-vendor AI integrations
Module 2. Operationalizing DoD AI Ethical Principles
Break down the six DoD AI Ethical Principles into actionable design and documentation requirements that survive program transitions and leadership changes.
12 chapters in this module
  1. Translating Responsible AI principles into technical specifications
  2. Documenting resiliency requirements for mission-critical models
  3. Ensuring AI system safety in high-consequence operational environments
  4. Building audit trails for AI-enabled decision support systems
  5. Verifying fairness in AI-assisted targeting and resource allocation
  6. Human oversight mechanisms for autonomous capabilities
  7. Implementing traceability from requirement to AI model output
  8. Creating governance evidence for dual-use AI technologies
  9. Addressing bias in training data for geospatial intelligence models
  10. Securing AI inference pipelines against manipulation
  11. Ensuring transparency without compromising operational security
  12. Establishing accountability chains for AI-augmented decisions
Module 3. AI Risk Assessment at Program Scale
Apply NIST AI RMF components to real-world project assessments, identifying high-risk use cases before deployment and justifying risk tolerance levels to oversight bodies.
12 chapters in this module
  1. Using NIST AI RMF to classify AI system risk levels
  2. Conducting AI-specific threat modeling for national security systems
  3. Integrating AI risk assessments into existing RMAT processes
  4. Documenting risk acceptances with appropriate justification
  5. Evaluating AI model performance under operational stress
  6. Assessing adversarial robustness in contested environments
  7. Identifying AI supply chain vulnerabilities in third-party models
  8. Measuring model explainability requirements by mission type
  9. Scoping AI red teaming exercises for high-risk deployments
  10. Establishing model monitoring baselines before fielding
  11. Creating risk heat maps for AI-enabled command and control
  12. Prioritizing AI mitigation efforts by consequence of failure
Module 4. AI Governance in Acquisition Lifecycle
Embed governance checkpoints into existing federal acquisition phases, ensuring compliance is maintained from RFP response through post-deployment audit.
12 chapters in this module
  1. Incorporating AI compliance requirements into proposal responses
  2. Translating contract AI clauses into technical implementation plans
  3. Establishing AI governance roles in multi-contractor environments
  4. Managing AI compliance across agile development sprints
  5. Integrating AI documentation into CDRL submissions
  6. Ensuring AI model lineage tracking through vendor transitions
  7. Conducting AI-specific technical reviews at milestone gates
  8. Preparing AI attestation packages for IG review cycles
  9. Documenting AI system changes during operations and maintenance
  10. Handling AI model updates under configuration management
  11. Maintaining AI compliance during system modernization efforts
  12. Delivering AI governance artifacts for program closeout
Module 5. Building AI Compliance Evidence Packages
Assemble regulator-ready documentation sets that address both technical and ethical dimensions of AI deployment, reducing rework during inspections.
12 chapters in this module
  1. Structuring AI system documentation for audit readiness
  2. Creating model cards that meet federal transparency requirements
  3. Documenting AI training data provenance and preprocessing
  4. Assembling model performance validation reports
  5. Building adversarial robustness test summaries
  6. Producing human oversight implementation records
  7. Generating model monitoring and drift detection logs
  8. Compiling AI ethics review board findings
  9. Mapping AI controls to NIST SP 800-53 security controls
  10. Creating AI-specific incident response playbooks
  11. Documenting AI model decommissioning procedures
  12. Packaging AI evidence for cross-program reuse
Module 6. AI Accountability and Organizational Alignment
Establish clear decision rights and escalation paths for AI systems, ensuring accountability is maintained across technical, operational, and compliance functions.
12 chapters in this module
  1. Defining AI decision ownership in joint mission environments
  2. Establishing AI escalation protocols for high-risk scenarios
  3. Creating cross-functional AI review boards
  4. Documenting AI model approval and sign-off processes
  5. Managing AI liability across government-contractor boundaries
  6. Ensuring chain of command awareness of AI capabilities
  7. Handling AI system failures in operational settings
  8. Establishing AI audit trails for after-action reviews
  9. Documenting AI limitations to operators and commanders
  10. Managing AI model versioning in distributed environments
  11. Ensuring AI system documentation survives personnel turnover
  12. Aligning AI governance with existing command structures
Module 7. AI Model Development and Testing Standards
Implement technical standards for AI model development that ensure reproducibility, validation, and compliance with federal requirements.
12 chapters in this module
  1. Applying NIST guidelines to AI model development
  2. Implementing model version control and lineage tracking
  3. Creating reproducible AI training environments
  4. Validating AI model performance across operational scenarios
  5. Testing AI robustness under degraded conditions
  6. Assessing AI model fairness in mission-specific contexts
  7. Documenting AI model assumptions and limitations
  8. Ensuring AI model interpretability for human operators
  9. Creating AI model security test plans
  10. Verifying AI system behavior in edge computing environments
  11. Testing AI resilience to adversarial data inputs
  12. Establishing AI model performance baselines for monitoring
Module 8. AI System Monitoring and Maintenance
Design operational monitoring for AI systems that detects degradation, ensures continued compliance, and triggers appropriate human intervention.
12 chapters in this module
  1. Establishing AI model performance thresholds
  2. Detecting concept drift in deployed AI systems
  3. Monitoring AI model input data quality
  4. Creating AI system health dashboards
  5. Implementing automated AI model retraining triggers
  6. Documenting AI model updates and revalidation
  7. Ensuring AI monitoring complies with privacy requirements
  8. Integrating AI alerts into existing incident response
  9. Testing AI fallback mechanisms during outages
  10. Maintaining AI compliance during system upgrades
  11. Auditing AI system decisions post-deployment
  12. Decommissioning AI models with proper documentation
Module 9. AI Vendor Management and Third-Party Risk
Govern AI components from commercial vendors and open-source projects, ensuring compliance extends beyond in-house development.
12 chapters in this module
  1. Assessing third-party AI model trustworthiness
  2. Evaluating vendor AI governance maturity
  3. Managing risks of pre-trained foundation models
  4. Ensuring AI supply chain transparency
  5. Validating vendor AI testing claims
  6. Handling AI model updates from external sources
  7. Maintaining control over proprietary AI components
  8. Establishing AI-specific SLAs with vendors
  9. Managing AI model licensing and redistribution
  10. Ensuring vendor AI compliance with federal standards
  11. Auditing third-party AI model performance
  12. Creating exit strategies for vendor-dependent AI systems
Module 10. AI Governance Across Mission Domains
Adapt governance approaches for different national security applications including intelligence, logistics, cyber, and command and control.
12 chapters in this module
  1. Tailoring AI governance for SIGINT applications
  2. Ensuring AI compliance in battlefield logistics
  3. Governing AI in cyber defense and offense systems
  4. Managing AI in command and control decision support
  5. Applying AI governance to autonomous platforms
  6. Ensuring AI reliability in space-based systems
  7. Governing AI-enabled electronic warfare
  8. Managing AI in multi-domain operations
  9. Adapting AI governance for coalition environments
  10. Ensuring AI interoperability across services
  11. Governing AI in nuclear command and control
  12. Applying AI governance to disaster response systems
Module 11. AI Ethics Review and Oversight Processes
Establish formal review processes that evaluate AI systems against ethical principles while maintaining operational effectiveness.
12 chapters in this module
  1. Creating AI ethics review board charters
  2. Conducting pre-deployment AI ethics reviews
  3. Documenting AI ethics decision rationales
  4. Ensuring review board independence and expertise
  5. Balancing AI innovation with ethical constraints
  6. Handling classified AI ethics reviews
  7. Incorporating lessons learned into future projects
  8. Ensuring AI ethics compliance in emergency deployments
  9. Reviewing AI-human teaming arrangements
  10. Evaluating AI impact on civilian populations
  11. Managing AI ethics reviews in joint operations
  12. Updating AI ethics policies based on operational experience
Module 12. Scaling AI Governance Across the Enterprise
Transition from project-level compliance to organization-wide AI governance standards that maintain consistency while allowing mission-specific adaptation.
12 chapters in this module
  1. Creating enterprise AI governance templates
  2. Establishing AI governance centers of excellence
  3. Training personnel on AI compliance requirements
  4. Developing AI governance playbooks for new programs
  5. Sharing AI lessons learned across contracts
  6. Standardizing AI documentation formats
  7. Creating AI model repositories for reuse
  8. Ensuring AI governance survives leadership changes
  9. Measuring AI governance program effectiveness
  10. Aligning AI governance with enterprise architecture
  11. Scaling AI compliance to multi-billion dollar programs
  12. Sustaining AI governance excellence through transitions

How this maps to your situation

  • Pre-deployment governance planning
  • Mid-cycle compliance assurance
  • Post-deployment audit readiness
  • Cross-program standards development

Before vs. after

Before
Spending cycles retroactively aligning AI deployments with compliance requirements, leading to rework and credibility erosion during IG reviews
After
Producing regulator-ready AI governance packages on first draft, establishing consistent control mappings, and being sought out as the internal reference for AI compliance

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 8-10 hours of self-paced learning, designed to fit around mission delivery cycles.

If nothing changes
Continuing to treat AI governance as a post-deployment exercise increases exposure to IG findings, delays follow-on funding, and cedes leadership opportunities to peers who systematize compliance.

How this compares to the alternatives

Unlike generic AI ethics courses, this program focuses on actionable compliance for national security practitioners, combining DoD directives, NIST frameworks, and real-world program challenges to build immediately applicable governance skills.

Frequently asked

Is this course focused on technical AI implementation or governance strategy?
It's focused on operational governance, how to implement, document, and verify AI compliance in real programs without slowing delivery.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me during IG audit cycles?
Yes, each module builds toward producing regulator-ready documentation that withstands inspection.
$199 one-time. Approximately 8-10 hours of self-paced learning, designed to fit around mission delivery cycles..

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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