Mastering DODAF for AI-Driven Defense Systems
You're under pressure. Budgets are tight, timelines are shrinking, and leadership demands faster AI integration without compromising compliance, interoperability, or mission integrity. You know DODAF is the key, but translating complex doctrine into actionable system architecture for AI-enabled defense platforms feels like navigating a labyrinth-alone. Without a proven method, you risk wasted effort, misaligned stakeholders, and proposals rejected at the portfolio level. What took others months to build, you're expected to deliver in weeks. But here’s the truth: the difference between stalled initiatives and funded, board-ready AI transformation isn’t talent. It’s structure. Mastering DODAF for AI-Driven Defense Systems is not another theoretical overview. It’s the battle-tested framework that transforms confusion into clarity, turning abstract AI concepts into compliant, executable, stakeholder-approved architectures in as little as 30 days. One lead systems engineer at a DoD Tier 1 contractor used this methodology to deliver a full AI-powered command and control architecture two weeks ahead of schedule. Her proposal was fast-tracked by the acquisition board and is now part of the core roadmap for next-gen autonomous integration. She didn’t have more time. She had the right structure. This course gives you that structure. You’ll go from uncertain diagrams to fully integrated, AI-aligned DODAF artifacts designed for approval, integration, and real-world deployment. No fluff, no filler-just the prioritized, repeatable workflow that defense leaders trust. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced • On-Demand • Lifetime Access
You need clarity, not scheduling conflicts. This course is entirely self-paced, with full on-demand access from any location worldwide. You control when and where you learn-no mandatory live sessions, no fixed dates, no time zone barriers. Start today, study at your pace, and complete the course in approximately 35 to 45 hours. Most learners apply the core methods to their current project and see measurable progress within the first week. Your access never expires. You receive lifetime access to all course materials, including every future update at no additional cost. As DODAF evolves and AI integration standards shift, your knowledge stays current-automatically. Mobile-Friendly • 24/7 Global Access • Instant Readiness
Whether you're in the office, at a field site, or on travel, all materials are optimized for seamless access across desktop, tablet, and mobile devices. Study during briefings, while commuting, or between meetings-your progress syncs in real time, no matter the device. All learning assets are structured for speed and precision. You’ll learn through interactive frameworks, annotated templates, guided workflows, model responses, and real-world use cases-all text based, fully accessible, and engineered for comprehension under pressure. Expert-Led Guidance with Real-World Relevance
You’re not alone. This course includes direct access to our instructor support team for clarification, scenario review, and implementation guidance. Submit your architecture challenges and receive feedback grounded in DoD and defense contractor experience. The curriculum reflects actual workflows from Tier 1 integrators, military R&D labs, and acquisition offices. Every concept ties directly to operational systems and current AI adoption priorities across the DoD, DHS, and allied defense agencies. Certification That Commands Attention
Upon completion, you will earn a Certificate of Completion issued by The Art of Service-a globally recognized credential with proven traction across defense, aerospace, and government contracting sectors. This isn’t a participation badge. It’s proof you’ve mastered DODAF application in the context of modern AI integration, using methodologies validated by actual mission outcomes. The certificate is shareable on LinkedIn, included in government contract proposals, and used by past graduates to justify promotions, secure consulting contracts, and lead high-impact technical teams. Transparent Pricing • Zero Risk • Full Confidence
There are no hidden fees. No recurring charges. The price you see is the only price you pay. You gain complete access to all modules, tools, templates, and updates-forever. We accept all major payment methods including Visa, Mastercard, and PayPal. Transactions are secured with industry-standard encryption, ensuring your data remains private and protected. Most importantly, your investment is protected by a risk-free, satisfied-or-refunded guarantee. If you complete the first three modules and determine this course is not the most practical, ROI-driven DODAF training you’ve ever experienced, simply contact support for a full refund. No questions, no hassle. This Works Even If:
- You’re new to DODAF but are tasked with delivering AI architecture deliverables
- You’re experienced with legacy system architecture but need to modernize for AI/ML integration
- You’re under contract pressure to align with DoDAF V2.02 standards
- You’re not a “visualization expert” but must produce views that win stakeholder buy-in
- You’ve struggled with disconnected AV, OV, SV, and CV artifacts in past projects
We’ve built this course for real practitioners, not theorists. One cyber systems architect at a defense agency said: “I used the CV-7 AI Capability Mapping template from Module 9 during a requirement workshop. By lunch, the PMO approved our pilot. That never happens.” Enroll today and you’ll receive an email confirmation. Your access credentials and course dashboard details will be delivered shortly after, granting immediate entry to all learning materials. You control the pace, the path, and the impact.
Module 1: Foundations of DODAF in the Age of AI - Understanding DODAF’s role in modern defense transformation
- Mapping AI-driven capabilities to DoD strategic objectives
- How DODAF enables interoperability in multi-domain AI operations
- Key differences between traditional and AI-integrated architecture development
- The lifecycle impact of AI on systems engineering and acquisition
- Demystifying DODAF V2.02 core terminology and stakeholder language
- Integrating AI ethics and explainability into foundational views
- Overview of AI use cases in C2, ISR, logistics, and cyber defense
- Aligning architecture efforts with Joint All-Domain Command and Control (JADC2)
- The role of data architecture in AI readiness and model performance
Module 2: Core DODAF Viewpoints and AI Transformation - Overview of the eight DODAF viewpoints and their strategic purpose
- AV-1 Overview and Summary Information applied to AI programs
- AV-2 Integrated Dictionary for defining AI-specific terms and metrics
- Linking AI requirements to OV-1 High-Level Operational Graphic
- Designing OV-2 Operational Node Connectivity for AI-enabled workflows
- Mapping OV-3 Operational Information Exchange Matrix with data flows for ML models
- Building OV-4 Organizational Relationships for human-AI teaming
- Developing OV-5a and OV-5b Operational Activity Models with AI decision points
- Integrating AI tasks into sunburst and process chain modeling
- Using OV-6a, OV-6b, and OV-6c to model AI decision logic and state transitions
- Applying OV-7 Logical Data Model for AI training and inference datasets
- Designing SV-1 Systems Interface Description for AI microservices
- Mapping SV-2 Systems Communications Description for AI data pipelines
- Constructing SV-3 Systems-Systems Matrix for AI model interoperability
- Using SV-4a and SV-4b for AI system performance and resource profiles
- Developing SV-5a, SV-5b, and SV-5c for AI system evolution and scalability
- Building SV-6 Systems Data Exchange Matrix for secure AI model outputs
- Applying SV-7 Systems Functionality Description to autonomous systems
- Architecting SV-8 Systems Evolution Description for AI model refresh cycles
- Linking SV-9 Systems Technology Forecast to emerging AI hardware trends
- Integrating CV-1 Vision with AI strategic objectives
- Mapping CV-2 Capability Taxonomy for AI-enabled mission sets
- Applying CV-3 Capability Phasing to AI adoption roadmaps
- Building CV-4 Capability Dependencies for AI service orchestration
- Developing CV-5 Capability-to-Operational Activity Mapping
- Using CV-6 Capability-to-System Mapping for AI system allocation
- Creating CV-7 Capability-to-Service Mapping for AI as a Service (AIaaS)
- Designing CV-8 Capability Evolution Description for machine learning drift
- Linking CV-9 Capability Portfolios to AI investment decisions
- Applying DIV-1 Defense Investment View to AI modernization budgets
Module 3: AI-Specific Architecture Patterns in DODAF - Identifying common AI architectural patterns in defense applications
- Designing federated learning architectures within DODAF constraints
- Modeling edge AI inference systems with SV and OV views
- Architecting secure AI training environments using DODAF security views
- Representing multi-agent AI systems in operational views
- Mapping reinforcement learning workflows to process models
- Designing hybrid human-AI command systems using OV-6 and SV-7
- Integrating LLM-based reasoning systems into C2 architecture
- Modeling AI explainability (XAI) requirements in system functionality
- Using data lineage traceability in support of AI accountability
- Architecting zero-trust AI data pipelines
- Designing multimodal sensor fusion systems using integration matrices
- Modeling adversarial AI threats in threat views
- Creating defensive AI response protocols in operational activity models
- Documenting AI failover and fallback modes in system evolution
Module 4: Integrating Data Architecture for AI Performance - The role of DoDAF’s Data and Information Viewpoint (DIV) in AI success
- Designing robust data pipelines for real-time AI inference
- Using DIV-2 to model data flows for training and validation
- Mapping metadata requirements for AI dataset versioning
- Defining data ownership and stewardship in AI ecosystems
- Creating data quality metrics in alignment with model accuracy
- Designing secure data access controls for classified AI systems
- Modeling data retention policies for audit and compliance
- Integrating data drift detection into capability evolution plans
- Using data provenance to support model certification and accreditation
Module 5: Security and Resilience in AI-Driven Systems - Integrating DODAF’s Systems Security Viewpoint (SV) with AI risks
- Threat modeling for AI poisoning, evasion, and extraction attacks
- Using SV-11 Physical Schema to secure AI hardware deployment
- Modeling access control policies for AI model APIs
- Architecting model integrity checks in system interfaces
- Defining secure boot and attestation processes for edge AI
- Mapping cybersecurity requirements to AI operational activities
- Designing monitored AI rollback procedures in system evolution
- Linking vulnerability management to architecture maintenance
- Documenting compliance with RMF and AI-specific controls
Module 6: AI Capability Definition and Roadmapping - Defining AI mission capabilities using the Capability Viewpoint
- Mapping AI use cases to CV-2 capability taxonomies
- Phasing AI deployment across CV-3 timelines
- Modeling capability dependencies for AI service composition
- Aligning AI roadmap with Joint Capabilities Integration and Development System (JCIDS)
- Creating portfolio dashboards for AI investment oversight
- Using cost-benefit analysis to prioritize AI capability development
- Integrating AI resilience metrics into capability evaluation
- Documenting AI technology readiness levels (TRLs) in capability forecasts
- Linking AI capability evolution to CONOPS updates
Module 7: Interoperability and Standards Alignment - Ensuring AI system compliance with DODAF, UPDM, and SysML standards
- Using UML and BPMN for AI process modeling
- Integrating AI artifacts with DoDAF Meta Model (DM2)
- Documenting adherence to NIST AI Risk Management Framework
- Aligning with DoD AI Ethical Principles in architecture documentation
- Mapping AI system behavior to IEEE standard ontologies
- Ensuring compatibility with C2 Enterprise Architecture (C2EA)
- Supporting ATO and certification packages with complete artifacts
- Documenting AI model training data sources for audit readiness
- Ensuring traceability from requirements to AI system behavior
Module 8: Real-World Application Projects - Project 1: Designing an AI-Enhanced Command and Control System
- Project 2: Architecting an Autonomous Logistics Routing Platform
- Project 3: Building an AI-Powered Cyber Threat Detection System
- Project 4: Developing an ISR Data Fusion Engine with ML Classification
- Project 5: Creating a Predictive Maintenance System for Combat Vehicles
- Using OV-1 to model mission context for AI C2
- Designing SV-1 interfaces for AI recommendation engines
- Building CV-6 mapping for sensor-to-decision AI pipelines
- Linking CV-7 service dependencies for multi-node AI inference
- Applying AV-2 dictionary to standardize AI model KPIs
- Documenting AI system evolution with SV-8 and CV-8
- Ensuring operational continuity during AI model updates
- Integrating human-in-the-loop checkpoints in AI workflows
- Using DIV-2 to visualize real-time data flows from UAV sensors to AI processors
- Linking AI performance metrics to capability outcomes in CV-9 dashboards
Module 9: Tools, Templates, and Accelerators - Access to AI-optimized DODAF template library (AV, OV, SV, CV, DIV)
- Fillable AV-1 and AV-2 templates for AI project documentation
- Pre-built OV-2 node diagrams for command, sensor, and cyber domains
- Customizable SV-3 interoperability matrices for AI services
- Capability phasing timeline templates (CV-3) with AI deployment markers
- AI capability dependency mapping worksheet (CV-4)
- AI system interface checklist (SV-1) with security annotations
- Data exchange matrix for AI model outputs (SV-6)
- AI evolution roadmap planner (SV-8 and CV-8 synchronized)
- Capability-to-service mapping for AIaaS deployment models
- Risk register template aligned with AI-specific threats
- AI model documentation annex for accreditation packages
- Stakeholder communication package: executive summaries and briefs
- Progress tracking dashboard with milestone metrics
- Architecture review checklist for AI compliance and completeness
Module 10: Integration with Defense Acquisition and Governance - Preparing DODAF artifacts for Milestone Decision Authority (MDA) reviews
- Aligning AI architecture with Acquisition Category (ACAT) requirements
- Supporting Initial Capabilities Document (ICD) with CV and OV artifacts
- Linking Architecture Description to Analysis of Alternatives (AoA)
- Using DODAF to justify Technology Development Strategy (TDS)
- Providing system specifications for Request for Proposal (RFP) development
- Documenting interoperability for DoD Information Enterprise (DoDIE)
- Supporting Test and Evaluation Master Plan (TEMP) with operational views
- Aligning AI evolution plans with Life Cycle Sustainment Plan (LCSP)
- Integrating sustainment metrics into system evolution documentation
Module 11: Advanced Techniques and Optimization - Optimizing DODAF models for clarity and stakeholder comprehension
- Using abstraction layers to manage AI system complexity
- Creating layered views for executive and technical audiences
- Automating traceability across AI capability, operational, and system views
- Using pattern libraries to accelerate AI architecture development
- Reducing redundancy in multi-view modeling
- Improving consistency across large AI program teams
- Validating model completeness with built-in checklists
- Integrating reuse strategies for AI capability components
- Creating modular architecture blocks for rapid AI prototyping
Module 12: Certification, Career Advancement, and Next Steps - Final assessment: Build a complete AI capability package using DODAF
- Submit your project for expert review and feedback
- How to present your architecture to technical and executive stakeholders
- Using your Certificate of Completion to enhance your credential portfolio
- Adding DODAF-AI mastery to your LinkedIn profile and resume
- Leveraging certification in government contract proposals
- Pursuing advanced roles: Lead Systems Architect, AI Integration Director, Chief Engineer
- Joining the DODAF-AI Practitioner Network for ongoing collaboration
- Access to updated templates and reference materials for licensed graduates
- Continuing education pathways in defense AI, model governance, and zero-trust architecture
- Becoming a mentor and subject matter expert in AI system architecture
- Preparing for DoD 8570.01-M compliance with aligned skills
- Using the course project as a portfolio centerpiece
- How to lead architecture reviews with confidence and authority
- Next steps: from certification to implementation in your organization
- Final checklist: from learning to leadership
- Understanding DODAF’s role in modern defense transformation
- Mapping AI-driven capabilities to DoD strategic objectives
- How DODAF enables interoperability in multi-domain AI operations
- Key differences between traditional and AI-integrated architecture development
- The lifecycle impact of AI on systems engineering and acquisition
- Demystifying DODAF V2.02 core terminology and stakeholder language
- Integrating AI ethics and explainability into foundational views
- Overview of AI use cases in C2, ISR, logistics, and cyber defense
- Aligning architecture efforts with Joint All-Domain Command and Control (JADC2)
- The role of data architecture in AI readiness and model performance
Module 2: Core DODAF Viewpoints and AI Transformation - Overview of the eight DODAF viewpoints and their strategic purpose
- AV-1 Overview and Summary Information applied to AI programs
- AV-2 Integrated Dictionary for defining AI-specific terms and metrics
- Linking AI requirements to OV-1 High-Level Operational Graphic
- Designing OV-2 Operational Node Connectivity for AI-enabled workflows
- Mapping OV-3 Operational Information Exchange Matrix with data flows for ML models
- Building OV-4 Organizational Relationships for human-AI teaming
- Developing OV-5a and OV-5b Operational Activity Models with AI decision points
- Integrating AI tasks into sunburst and process chain modeling
- Using OV-6a, OV-6b, and OV-6c to model AI decision logic and state transitions
- Applying OV-7 Logical Data Model for AI training and inference datasets
- Designing SV-1 Systems Interface Description for AI microservices
- Mapping SV-2 Systems Communications Description for AI data pipelines
- Constructing SV-3 Systems-Systems Matrix for AI model interoperability
- Using SV-4a and SV-4b for AI system performance and resource profiles
- Developing SV-5a, SV-5b, and SV-5c for AI system evolution and scalability
- Building SV-6 Systems Data Exchange Matrix for secure AI model outputs
- Applying SV-7 Systems Functionality Description to autonomous systems
- Architecting SV-8 Systems Evolution Description for AI model refresh cycles
- Linking SV-9 Systems Technology Forecast to emerging AI hardware trends
- Integrating CV-1 Vision with AI strategic objectives
- Mapping CV-2 Capability Taxonomy for AI-enabled mission sets
- Applying CV-3 Capability Phasing to AI adoption roadmaps
- Building CV-4 Capability Dependencies for AI service orchestration
- Developing CV-5 Capability-to-Operational Activity Mapping
- Using CV-6 Capability-to-System Mapping for AI system allocation
- Creating CV-7 Capability-to-Service Mapping for AI as a Service (AIaaS)
- Designing CV-8 Capability Evolution Description for machine learning drift
- Linking CV-9 Capability Portfolios to AI investment decisions
- Applying DIV-1 Defense Investment View to AI modernization budgets
Module 3: AI-Specific Architecture Patterns in DODAF - Identifying common AI architectural patterns in defense applications
- Designing federated learning architectures within DODAF constraints
- Modeling edge AI inference systems with SV and OV views
- Architecting secure AI training environments using DODAF security views
- Representing multi-agent AI systems in operational views
- Mapping reinforcement learning workflows to process models
- Designing hybrid human-AI command systems using OV-6 and SV-7
- Integrating LLM-based reasoning systems into C2 architecture
- Modeling AI explainability (XAI) requirements in system functionality
- Using data lineage traceability in support of AI accountability
- Architecting zero-trust AI data pipelines
- Designing multimodal sensor fusion systems using integration matrices
- Modeling adversarial AI threats in threat views
- Creating defensive AI response protocols in operational activity models
- Documenting AI failover and fallback modes in system evolution
Module 4: Integrating Data Architecture for AI Performance - The role of DoDAF’s Data and Information Viewpoint (DIV) in AI success
- Designing robust data pipelines for real-time AI inference
- Using DIV-2 to model data flows for training and validation
- Mapping metadata requirements for AI dataset versioning
- Defining data ownership and stewardship in AI ecosystems
- Creating data quality metrics in alignment with model accuracy
- Designing secure data access controls for classified AI systems
- Modeling data retention policies for audit and compliance
- Integrating data drift detection into capability evolution plans
- Using data provenance to support model certification and accreditation
Module 5: Security and Resilience in AI-Driven Systems - Integrating DODAF’s Systems Security Viewpoint (SV) with AI risks
- Threat modeling for AI poisoning, evasion, and extraction attacks
- Using SV-11 Physical Schema to secure AI hardware deployment
- Modeling access control policies for AI model APIs
- Architecting model integrity checks in system interfaces
- Defining secure boot and attestation processes for edge AI
- Mapping cybersecurity requirements to AI operational activities
- Designing monitored AI rollback procedures in system evolution
- Linking vulnerability management to architecture maintenance
- Documenting compliance with RMF and AI-specific controls
Module 6: AI Capability Definition and Roadmapping - Defining AI mission capabilities using the Capability Viewpoint
- Mapping AI use cases to CV-2 capability taxonomies
- Phasing AI deployment across CV-3 timelines
- Modeling capability dependencies for AI service composition
- Aligning AI roadmap with Joint Capabilities Integration and Development System (JCIDS)
- Creating portfolio dashboards for AI investment oversight
- Using cost-benefit analysis to prioritize AI capability development
- Integrating AI resilience metrics into capability evaluation
- Documenting AI technology readiness levels (TRLs) in capability forecasts
- Linking AI capability evolution to CONOPS updates
Module 7: Interoperability and Standards Alignment - Ensuring AI system compliance with DODAF, UPDM, and SysML standards
- Using UML and BPMN for AI process modeling
- Integrating AI artifacts with DoDAF Meta Model (DM2)
- Documenting adherence to NIST AI Risk Management Framework
- Aligning with DoD AI Ethical Principles in architecture documentation
- Mapping AI system behavior to IEEE standard ontologies
- Ensuring compatibility with C2 Enterprise Architecture (C2EA)
- Supporting ATO and certification packages with complete artifacts
- Documenting AI model training data sources for audit readiness
- Ensuring traceability from requirements to AI system behavior
Module 8: Real-World Application Projects - Project 1: Designing an AI-Enhanced Command and Control System
- Project 2: Architecting an Autonomous Logistics Routing Platform
- Project 3: Building an AI-Powered Cyber Threat Detection System
- Project 4: Developing an ISR Data Fusion Engine with ML Classification
- Project 5: Creating a Predictive Maintenance System for Combat Vehicles
- Using OV-1 to model mission context for AI C2
- Designing SV-1 interfaces for AI recommendation engines
- Building CV-6 mapping for sensor-to-decision AI pipelines
- Linking CV-7 service dependencies for multi-node AI inference
- Applying AV-2 dictionary to standardize AI model KPIs
- Documenting AI system evolution with SV-8 and CV-8
- Ensuring operational continuity during AI model updates
- Integrating human-in-the-loop checkpoints in AI workflows
- Using DIV-2 to visualize real-time data flows from UAV sensors to AI processors
- Linking AI performance metrics to capability outcomes in CV-9 dashboards
Module 9: Tools, Templates, and Accelerators - Access to AI-optimized DODAF template library (AV, OV, SV, CV, DIV)
- Fillable AV-1 and AV-2 templates for AI project documentation
- Pre-built OV-2 node diagrams for command, sensor, and cyber domains
- Customizable SV-3 interoperability matrices for AI services
- Capability phasing timeline templates (CV-3) with AI deployment markers
- AI capability dependency mapping worksheet (CV-4)
- AI system interface checklist (SV-1) with security annotations
- Data exchange matrix for AI model outputs (SV-6)
- AI evolution roadmap planner (SV-8 and CV-8 synchronized)
- Capability-to-service mapping for AIaaS deployment models
- Risk register template aligned with AI-specific threats
- AI model documentation annex for accreditation packages
- Stakeholder communication package: executive summaries and briefs
- Progress tracking dashboard with milestone metrics
- Architecture review checklist for AI compliance and completeness
Module 10: Integration with Defense Acquisition and Governance - Preparing DODAF artifacts for Milestone Decision Authority (MDA) reviews
- Aligning AI architecture with Acquisition Category (ACAT) requirements
- Supporting Initial Capabilities Document (ICD) with CV and OV artifacts
- Linking Architecture Description to Analysis of Alternatives (AoA)
- Using DODAF to justify Technology Development Strategy (TDS)
- Providing system specifications for Request for Proposal (RFP) development
- Documenting interoperability for DoD Information Enterprise (DoDIE)
- Supporting Test and Evaluation Master Plan (TEMP) with operational views
- Aligning AI evolution plans with Life Cycle Sustainment Plan (LCSP)
- Integrating sustainment metrics into system evolution documentation
Module 11: Advanced Techniques and Optimization - Optimizing DODAF models for clarity and stakeholder comprehension
- Using abstraction layers to manage AI system complexity
- Creating layered views for executive and technical audiences
- Automating traceability across AI capability, operational, and system views
- Using pattern libraries to accelerate AI architecture development
- Reducing redundancy in multi-view modeling
- Improving consistency across large AI program teams
- Validating model completeness with built-in checklists
- Integrating reuse strategies for AI capability components
- Creating modular architecture blocks for rapid AI prototyping
Module 12: Certification, Career Advancement, and Next Steps - Final assessment: Build a complete AI capability package using DODAF
- Submit your project for expert review and feedback
- How to present your architecture to technical and executive stakeholders
- Using your Certificate of Completion to enhance your credential portfolio
- Adding DODAF-AI mastery to your LinkedIn profile and resume
- Leveraging certification in government contract proposals
- Pursuing advanced roles: Lead Systems Architect, AI Integration Director, Chief Engineer
- Joining the DODAF-AI Practitioner Network for ongoing collaboration
- Access to updated templates and reference materials for licensed graduates
- Continuing education pathways in defense AI, model governance, and zero-trust architecture
- Becoming a mentor and subject matter expert in AI system architecture
- Preparing for DoD 8570.01-M compliance with aligned skills
- Using the course project as a portfolio centerpiece
- How to lead architecture reviews with confidence and authority
- Next steps: from certification to implementation in your organization
- Final checklist: from learning to leadership
- Identifying common AI architectural patterns in defense applications
- Designing federated learning architectures within DODAF constraints
- Modeling edge AI inference systems with SV and OV views
- Architecting secure AI training environments using DODAF security views
- Representing multi-agent AI systems in operational views
- Mapping reinforcement learning workflows to process models
- Designing hybrid human-AI command systems using OV-6 and SV-7
- Integrating LLM-based reasoning systems into C2 architecture
- Modeling AI explainability (XAI) requirements in system functionality
- Using data lineage traceability in support of AI accountability
- Architecting zero-trust AI data pipelines
- Designing multimodal sensor fusion systems using integration matrices
- Modeling adversarial AI threats in threat views
- Creating defensive AI response protocols in operational activity models
- Documenting AI failover and fallback modes in system evolution
Module 4: Integrating Data Architecture for AI Performance - The role of DoDAF’s Data and Information Viewpoint (DIV) in AI success
- Designing robust data pipelines for real-time AI inference
- Using DIV-2 to model data flows for training and validation
- Mapping metadata requirements for AI dataset versioning
- Defining data ownership and stewardship in AI ecosystems
- Creating data quality metrics in alignment with model accuracy
- Designing secure data access controls for classified AI systems
- Modeling data retention policies for audit and compliance
- Integrating data drift detection into capability evolution plans
- Using data provenance to support model certification and accreditation
Module 5: Security and Resilience in AI-Driven Systems - Integrating DODAF’s Systems Security Viewpoint (SV) with AI risks
- Threat modeling for AI poisoning, evasion, and extraction attacks
- Using SV-11 Physical Schema to secure AI hardware deployment
- Modeling access control policies for AI model APIs
- Architecting model integrity checks in system interfaces
- Defining secure boot and attestation processes for edge AI
- Mapping cybersecurity requirements to AI operational activities
- Designing monitored AI rollback procedures in system evolution
- Linking vulnerability management to architecture maintenance
- Documenting compliance with RMF and AI-specific controls
Module 6: AI Capability Definition and Roadmapping - Defining AI mission capabilities using the Capability Viewpoint
- Mapping AI use cases to CV-2 capability taxonomies
- Phasing AI deployment across CV-3 timelines
- Modeling capability dependencies for AI service composition
- Aligning AI roadmap with Joint Capabilities Integration and Development System (JCIDS)
- Creating portfolio dashboards for AI investment oversight
- Using cost-benefit analysis to prioritize AI capability development
- Integrating AI resilience metrics into capability evaluation
- Documenting AI technology readiness levels (TRLs) in capability forecasts
- Linking AI capability evolution to CONOPS updates
Module 7: Interoperability and Standards Alignment - Ensuring AI system compliance with DODAF, UPDM, and SysML standards
- Using UML and BPMN for AI process modeling
- Integrating AI artifacts with DoDAF Meta Model (DM2)
- Documenting adherence to NIST AI Risk Management Framework
- Aligning with DoD AI Ethical Principles in architecture documentation
- Mapping AI system behavior to IEEE standard ontologies
- Ensuring compatibility with C2 Enterprise Architecture (C2EA)
- Supporting ATO and certification packages with complete artifacts
- Documenting AI model training data sources for audit readiness
- Ensuring traceability from requirements to AI system behavior
Module 8: Real-World Application Projects - Project 1: Designing an AI-Enhanced Command and Control System
- Project 2: Architecting an Autonomous Logistics Routing Platform
- Project 3: Building an AI-Powered Cyber Threat Detection System
- Project 4: Developing an ISR Data Fusion Engine with ML Classification
- Project 5: Creating a Predictive Maintenance System for Combat Vehicles
- Using OV-1 to model mission context for AI C2
- Designing SV-1 interfaces for AI recommendation engines
- Building CV-6 mapping for sensor-to-decision AI pipelines
- Linking CV-7 service dependencies for multi-node AI inference
- Applying AV-2 dictionary to standardize AI model KPIs
- Documenting AI system evolution with SV-8 and CV-8
- Ensuring operational continuity during AI model updates
- Integrating human-in-the-loop checkpoints in AI workflows
- Using DIV-2 to visualize real-time data flows from UAV sensors to AI processors
- Linking AI performance metrics to capability outcomes in CV-9 dashboards
Module 9: Tools, Templates, and Accelerators - Access to AI-optimized DODAF template library (AV, OV, SV, CV, DIV)
- Fillable AV-1 and AV-2 templates for AI project documentation
- Pre-built OV-2 node diagrams for command, sensor, and cyber domains
- Customizable SV-3 interoperability matrices for AI services
- Capability phasing timeline templates (CV-3) with AI deployment markers
- AI capability dependency mapping worksheet (CV-4)
- AI system interface checklist (SV-1) with security annotations
- Data exchange matrix for AI model outputs (SV-6)
- AI evolution roadmap planner (SV-8 and CV-8 synchronized)
- Capability-to-service mapping for AIaaS deployment models
- Risk register template aligned with AI-specific threats
- AI model documentation annex for accreditation packages
- Stakeholder communication package: executive summaries and briefs
- Progress tracking dashboard with milestone metrics
- Architecture review checklist for AI compliance and completeness
Module 10: Integration with Defense Acquisition and Governance - Preparing DODAF artifacts for Milestone Decision Authority (MDA) reviews
- Aligning AI architecture with Acquisition Category (ACAT) requirements
- Supporting Initial Capabilities Document (ICD) with CV and OV artifacts
- Linking Architecture Description to Analysis of Alternatives (AoA)
- Using DODAF to justify Technology Development Strategy (TDS)
- Providing system specifications for Request for Proposal (RFP) development
- Documenting interoperability for DoD Information Enterprise (DoDIE)
- Supporting Test and Evaluation Master Plan (TEMP) with operational views
- Aligning AI evolution plans with Life Cycle Sustainment Plan (LCSP)
- Integrating sustainment metrics into system evolution documentation
Module 11: Advanced Techniques and Optimization - Optimizing DODAF models for clarity and stakeholder comprehension
- Using abstraction layers to manage AI system complexity
- Creating layered views for executive and technical audiences
- Automating traceability across AI capability, operational, and system views
- Using pattern libraries to accelerate AI architecture development
- Reducing redundancy in multi-view modeling
- Improving consistency across large AI program teams
- Validating model completeness with built-in checklists
- Integrating reuse strategies for AI capability components
- Creating modular architecture blocks for rapid AI prototyping
Module 12: Certification, Career Advancement, and Next Steps - Final assessment: Build a complete AI capability package using DODAF
- Submit your project for expert review and feedback
- How to present your architecture to technical and executive stakeholders
- Using your Certificate of Completion to enhance your credential portfolio
- Adding DODAF-AI mastery to your LinkedIn profile and resume
- Leveraging certification in government contract proposals
- Pursuing advanced roles: Lead Systems Architect, AI Integration Director, Chief Engineer
- Joining the DODAF-AI Practitioner Network for ongoing collaboration
- Access to updated templates and reference materials for licensed graduates
- Continuing education pathways in defense AI, model governance, and zero-trust architecture
- Becoming a mentor and subject matter expert in AI system architecture
- Preparing for DoD 8570.01-M compliance with aligned skills
- Using the course project as a portfolio centerpiece
- How to lead architecture reviews with confidence and authority
- Next steps: from certification to implementation in your organization
- Final checklist: from learning to leadership
- Integrating DODAF’s Systems Security Viewpoint (SV) with AI risks
- Threat modeling for AI poisoning, evasion, and extraction attacks
- Using SV-11 Physical Schema to secure AI hardware deployment
- Modeling access control policies for AI model APIs
- Architecting model integrity checks in system interfaces
- Defining secure boot and attestation processes for edge AI
- Mapping cybersecurity requirements to AI operational activities
- Designing monitored AI rollback procedures in system evolution
- Linking vulnerability management to architecture maintenance
- Documenting compliance with RMF and AI-specific controls
Module 6: AI Capability Definition and Roadmapping - Defining AI mission capabilities using the Capability Viewpoint
- Mapping AI use cases to CV-2 capability taxonomies
- Phasing AI deployment across CV-3 timelines
- Modeling capability dependencies for AI service composition
- Aligning AI roadmap with Joint Capabilities Integration and Development System (JCIDS)
- Creating portfolio dashboards for AI investment oversight
- Using cost-benefit analysis to prioritize AI capability development
- Integrating AI resilience metrics into capability evaluation
- Documenting AI technology readiness levels (TRLs) in capability forecasts
- Linking AI capability evolution to CONOPS updates
Module 7: Interoperability and Standards Alignment - Ensuring AI system compliance with DODAF, UPDM, and SysML standards
- Using UML and BPMN for AI process modeling
- Integrating AI artifacts with DoDAF Meta Model (DM2)
- Documenting adherence to NIST AI Risk Management Framework
- Aligning with DoD AI Ethical Principles in architecture documentation
- Mapping AI system behavior to IEEE standard ontologies
- Ensuring compatibility with C2 Enterprise Architecture (C2EA)
- Supporting ATO and certification packages with complete artifacts
- Documenting AI model training data sources for audit readiness
- Ensuring traceability from requirements to AI system behavior
Module 8: Real-World Application Projects - Project 1: Designing an AI-Enhanced Command and Control System
- Project 2: Architecting an Autonomous Logistics Routing Platform
- Project 3: Building an AI-Powered Cyber Threat Detection System
- Project 4: Developing an ISR Data Fusion Engine with ML Classification
- Project 5: Creating a Predictive Maintenance System for Combat Vehicles
- Using OV-1 to model mission context for AI C2
- Designing SV-1 interfaces for AI recommendation engines
- Building CV-6 mapping for sensor-to-decision AI pipelines
- Linking CV-7 service dependencies for multi-node AI inference
- Applying AV-2 dictionary to standardize AI model KPIs
- Documenting AI system evolution with SV-8 and CV-8
- Ensuring operational continuity during AI model updates
- Integrating human-in-the-loop checkpoints in AI workflows
- Using DIV-2 to visualize real-time data flows from UAV sensors to AI processors
- Linking AI performance metrics to capability outcomes in CV-9 dashboards
Module 9: Tools, Templates, and Accelerators - Access to AI-optimized DODAF template library (AV, OV, SV, CV, DIV)
- Fillable AV-1 and AV-2 templates for AI project documentation
- Pre-built OV-2 node diagrams for command, sensor, and cyber domains
- Customizable SV-3 interoperability matrices for AI services
- Capability phasing timeline templates (CV-3) with AI deployment markers
- AI capability dependency mapping worksheet (CV-4)
- AI system interface checklist (SV-1) with security annotations
- Data exchange matrix for AI model outputs (SV-6)
- AI evolution roadmap planner (SV-8 and CV-8 synchronized)
- Capability-to-service mapping for AIaaS deployment models
- Risk register template aligned with AI-specific threats
- AI model documentation annex for accreditation packages
- Stakeholder communication package: executive summaries and briefs
- Progress tracking dashboard with milestone metrics
- Architecture review checklist for AI compliance and completeness
Module 10: Integration with Defense Acquisition and Governance - Preparing DODAF artifacts for Milestone Decision Authority (MDA) reviews
- Aligning AI architecture with Acquisition Category (ACAT) requirements
- Supporting Initial Capabilities Document (ICD) with CV and OV artifacts
- Linking Architecture Description to Analysis of Alternatives (AoA)
- Using DODAF to justify Technology Development Strategy (TDS)
- Providing system specifications for Request for Proposal (RFP) development
- Documenting interoperability for DoD Information Enterprise (DoDIE)
- Supporting Test and Evaluation Master Plan (TEMP) with operational views
- Aligning AI evolution plans with Life Cycle Sustainment Plan (LCSP)
- Integrating sustainment metrics into system evolution documentation
Module 11: Advanced Techniques and Optimization - Optimizing DODAF models for clarity and stakeholder comprehension
- Using abstraction layers to manage AI system complexity
- Creating layered views for executive and technical audiences
- Automating traceability across AI capability, operational, and system views
- Using pattern libraries to accelerate AI architecture development
- Reducing redundancy in multi-view modeling
- Improving consistency across large AI program teams
- Validating model completeness with built-in checklists
- Integrating reuse strategies for AI capability components
- Creating modular architecture blocks for rapid AI prototyping
Module 12: Certification, Career Advancement, and Next Steps - Final assessment: Build a complete AI capability package using DODAF
- Submit your project for expert review and feedback
- How to present your architecture to technical and executive stakeholders
- Using your Certificate of Completion to enhance your credential portfolio
- Adding DODAF-AI mastery to your LinkedIn profile and resume
- Leveraging certification in government contract proposals
- Pursuing advanced roles: Lead Systems Architect, AI Integration Director, Chief Engineer
- Joining the DODAF-AI Practitioner Network for ongoing collaboration
- Access to updated templates and reference materials for licensed graduates
- Continuing education pathways in defense AI, model governance, and zero-trust architecture
- Becoming a mentor and subject matter expert in AI system architecture
- Preparing for DoD 8570.01-M compliance with aligned skills
- Using the course project as a portfolio centerpiece
- How to lead architecture reviews with confidence and authority
- Next steps: from certification to implementation in your organization
- Final checklist: from learning to leadership
- Ensuring AI system compliance with DODAF, UPDM, and SysML standards
- Using UML and BPMN for AI process modeling
- Integrating AI artifacts with DoDAF Meta Model (DM2)
- Documenting adherence to NIST AI Risk Management Framework
- Aligning with DoD AI Ethical Principles in architecture documentation
- Mapping AI system behavior to IEEE standard ontologies
- Ensuring compatibility with C2 Enterprise Architecture (C2EA)
- Supporting ATO and certification packages with complete artifacts
- Documenting AI model training data sources for audit readiness
- Ensuring traceability from requirements to AI system behavior
Module 8: Real-World Application Projects - Project 1: Designing an AI-Enhanced Command and Control System
- Project 2: Architecting an Autonomous Logistics Routing Platform
- Project 3: Building an AI-Powered Cyber Threat Detection System
- Project 4: Developing an ISR Data Fusion Engine with ML Classification
- Project 5: Creating a Predictive Maintenance System for Combat Vehicles
- Using OV-1 to model mission context for AI C2
- Designing SV-1 interfaces for AI recommendation engines
- Building CV-6 mapping for sensor-to-decision AI pipelines
- Linking CV-7 service dependencies for multi-node AI inference
- Applying AV-2 dictionary to standardize AI model KPIs
- Documenting AI system evolution with SV-8 and CV-8
- Ensuring operational continuity during AI model updates
- Integrating human-in-the-loop checkpoints in AI workflows
- Using DIV-2 to visualize real-time data flows from UAV sensors to AI processors
- Linking AI performance metrics to capability outcomes in CV-9 dashboards
Module 9: Tools, Templates, and Accelerators - Access to AI-optimized DODAF template library (AV, OV, SV, CV, DIV)
- Fillable AV-1 and AV-2 templates for AI project documentation
- Pre-built OV-2 node diagrams for command, sensor, and cyber domains
- Customizable SV-3 interoperability matrices for AI services
- Capability phasing timeline templates (CV-3) with AI deployment markers
- AI capability dependency mapping worksheet (CV-4)
- AI system interface checklist (SV-1) with security annotations
- Data exchange matrix for AI model outputs (SV-6)
- AI evolution roadmap planner (SV-8 and CV-8 synchronized)
- Capability-to-service mapping for AIaaS deployment models
- Risk register template aligned with AI-specific threats
- AI model documentation annex for accreditation packages
- Stakeholder communication package: executive summaries and briefs
- Progress tracking dashboard with milestone metrics
- Architecture review checklist for AI compliance and completeness
Module 10: Integration with Defense Acquisition and Governance - Preparing DODAF artifacts for Milestone Decision Authority (MDA) reviews
- Aligning AI architecture with Acquisition Category (ACAT) requirements
- Supporting Initial Capabilities Document (ICD) with CV and OV artifacts
- Linking Architecture Description to Analysis of Alternatives (AoA)
- Using DODAF to justify Technology Development Strategy (TDS)
- Providing system specifications for Request for Proposal (RFP) development
- Documenting interoperability for DoD Information Enterprise (DoDIE)
- Supporting Test and Evaluation Master Plan (TEMP) with operational views
- Aligning AI evolution plans with Life Cycle Sustainment Plan (LCSP)
- Integrating sustainment metrics into system evolution documentation
Module 11: Advanced Techniques and Optimization - Optimizing DODAF models for clarity and stakeholder comprehension
- Using abstraction layers to manage AI system complexity
- Creating layered views for executive and technical audiences
- Automating traceability across AI capability, operational, and system views
- Using pattern libraries to accelerate AI architecture development
- Reducing redundancy in multi-view modeling
- Improving consistency across large AI program teams
- Validating model completeness with built-in checklists
- Integrating reuse strategies for AI capability components
- Creating modular architecture blocks for rapid AI prototyping
Module 12: Certification, Career Advancement, and Next Steps - Final assessment: Build a complete AI capability package using DODAF
- Submit your project for expert review and feedback
- How to present your architecture to technical and executive stakeholders
- Using your Certificate of Completion to enhance your credential portfolio
- Adding DODAF-AI mastery to your LinkedIn profile and resume
- Leveraging certification in government contract proposals
- Pursuing advanced roles: Lead Systems Architect, AI Integration Director, Chief Engineer
- Joining the DODAF-AI Practitioner Network for ongoing collaboration
- Access to updated templates and reference materials for licensed graduates
- Continuing education pathways in defense AI, model governance, and zero-trust architecture
- Becoming a mentor and subject matter expert in AI system architecture
- Preparing for DoD 8570.01-M compliance with aligned skills
- Using the course project as a portfolio centerpiece
- How to lead architecture reviews with confidence and authority
- Next steps: from certification to implementation in your organization
- Final checklist: from learning to leadership
- Access to AI-optimized DODAF template library (AV, OV, SV, CV, DIV)
- Fillable AV-1 and AV-2 templates for AI project documentation
- Pre-built OV-2 node diagrams for command, sensor, and cyber domains
- Customizable SV-3 interoperability matrices for AI services
- Capability phasing timeline templates (CV-3) with AI deployment markers
- AI capability dependency mapping worksheet (CV-4)
- AI system interface checklist (SV-1) with security annotations
- Data exchange matrix for AI model outputs (SV-6)
- AI evolution roadmap planner (SV-8 and CV-8 synchronized)
- Capability-to-service mapping for AIaaS deployment models
- Risk register template aligned with AI-specific threats
- AI model documentation annex for accreditation packages
- Stakeholder communication package: executive summaries and briefs
- Progress tracking dashboard with milestone metrics
- Architecture review checklist for AI compliance and completeness
Module 10: Integration with Defense Acquisition and Governance - Preparing DODAF artifacts for Milestone Decision Authority (MDA) reviews
- Aligning AI architecture with Acquisition Category (ACAT) requirements
- Supporting Initial Capabilities Document (ICD) with CV and OV artifacts
- Linking Architecture Description to Analysis of Alternatives (AoA)
- Using DODAF to justify Technology Development Strategy (TDS)
- Providing system specifications for Request for Proposal (RFP) development
- Documenting interoperability for DoD Information Enterprise (DoDIE)
- Supporting Test and Evaluation Master Plan (TEMP) with operational views
- Aligning AI evolution plans with Life Cycle Sustainment Plan (LCSP)
- Integrating sustainment metrics into system evolution documentation
Module 11: Advanced Techniques and Optimization - Optimizing DODAF models for clarity and stakeholder comprehension
- Using abstraction layers to manage AI system complexity
- Creating layered views for executive and technical audiences
- Automating traceability across AI capability, operational, and system views
- Using pattern libraries to accelerate AI architecture development
- Reducing redundancy in multi-view modeling
- Improving consistency across large AI program teams
- Validating model completeness with built-in checklists
- Integrating reuse strategies for AI capability components
- Creating modular architecture blocks for rapid AI prototyping
Module 12: Certification, Career Advancement, and Next Steps - Final assessment: Build a complete AI capability package using DODAF
- Submit your project for expert review and feedback
- How to present your architecture to technical and executive stakeholders
- Using your Certificate of Completion to enhance your credential portfolio
- Adding DODAF-AI mastery to your LinkedIn profile and resume
- Leveraging certification in government contract proposals
- Pursuing advanced roles: Lead Systems Architect, AI Integration Director, Chief Engineer
- Joining the DODAF-AI Practitioner Network for ongoing collaboration
- Access to updated templates and reference materials for licensed graduates
- Continuing education pathways in defense AI, model governance, and zero-trust architecture
- Becoming a mentor and subject matter expert in AI system architecture
- Preparing for DoD 8570.01-M compliance with aligned skills
- Using the course project as a portfolio centerpiece
- How to lead architecture reviews with confidence and authority
- Next steps: from certification to implementation in your organization
- Final checklist: from learning to leadership
- Optimizing DODAF models for clarity and stakeholder comprehension
- Using abstraction layers to manage AI system complexity
- Creating layered views for executive and technical audiences
- Automating traceability across AI capability, operational, and system views
- Using pattern libraries to accelerate AI architecture development
- Reducing redundancy in multi-view modeling
- Improving consistency across large AI program teams
- Validating model completeness with built-in checklists
- Integrating reuse strategies for AI capability components
- Creating modular architecture blocks for rapid AI prototyping