AI/ML USAF

[D] USAF 363d ISR Wing:

• [D] -- (As the Chief Architect and Interim CTO) I orchestrated a global-scale autonomous operations AL/ML SaaS platform (a Non-Kinetic Target App) for the intelligence community (CIA, NSA, NASIC, Army, Navy, & NATO) for a multi-year initiative, this system integrated over 50 disparate services and specialized applications to support a global intelligence and logistics network.

  1. I architected this solution for a user base exceeding 50,000 personnel, the architecture leveraged a M.A.C.H. (Microservices, API-first, Cloud-native, Headless) framework and integrated a massive Digital Twin engine, this deployment slashed core production cycles from months to hours (3-4/mo. to 3-4/hr) and reduced development overhead by 80% (low code/no code/Python/Node.js/Azure AI) while ensuring low-latency data fabric across international networks. 

Pipeline / Workflow Setup: (7) -- 

• BLUF: 

  1. -- To orchestrate this specific Non-Kinetic intelligence workflow within an Azure-native environment, I designed the architecture as a modular pipeline that ensures data provenance, security, and automated delivery. 

  2. -- By abstracting these steps into a microservices architecture, we effectively decoupled the data ingestion from the analytical dissemination, which was the key to moving from months-long production to hourly near-real-time updates.

• Mapped the functional requirements to the Azure ecosystem to create a secure, high-confidence assessment engine:

  1. Step 0: Distribution/Deploy: Containerization & Orchestration (Azure AKS & Docker).

     • Infrastructure Parity (Identical: Dev, Staging & Production): Used Azure AKS or Docker to containerize the entire app stack, ensuring absolute parity from development through to classified production instances.

       • -- Docker (Packaging/Distribution Layer) is the Format. It packages your code, dependencies, and runtime into a single, immutable unit called an Image.

     • Orchestration: Orchestrated via Azure Kubernetes Service (AKS) to provide infrastructure consistency across isolated DoD enclaves.

       • -- Azure AKS (The Deployment/Orchestration Layer): Is the Engine. It consumes those Docker images. It handles the "distribution" of those images across a cluster of servers, manages the scaling (adding more pods if traffic spikes), handles networking between microservices, and manages the lifecycle of those containers (restarting them if they crash).

     • Elasticity & Security: Leveraged horizontal pod autoscaling to manage fluctuating intelligence data bursts and utilized Azure Container Registry (ACR) for geo-replicated, secure image management, ensuring only hardened, scanned images are deployed.

  2. IP Tracking & Content Normalization (The "Identity Fabric").

     • To handle shifting IP addresses while maintaining content identity, I utilized Azure Front Door and Azure Web Application Firewall (WAF) for global traffic management and DDoS protection (Distributed Denial of Service) a malicious cyber attack-threat that disrupts websites, servers, or networks by using fake, incoming internet traffic.

       • -- Restricted in an Azure Virtual Network (VNet) to ensure data residency compliance.

     • Engine: We used Azure Functions (Python) triggered by event-driven logs from Azure Monitor to perform "Content Fingerprinting." Even when IPs rotated, the engine calculated cryptographic hashes (SHA-256) of the payload content to correlate and de-duplicate data streams, effectively tracking the source's message rather than its network location.

  3. Authoritative Source Validation.

     • We implemented an Attribute-Based Access Control (ABAC) model powered by Microsoft Entra ID (Aka Azure AD). The system cross-referenced incoming data against a "Golden Source" metadata database stored in Azure SQL Database. If a source was not in the verified whitelist, the data was routed to a sandbox for forensic verification before entering the analysis pipeline.

  4. Automated Scrape & Intelligent Ingestion.

     • We leveraged Azure AI Search combined with Azure AI Document Intelligence and Azure AI Computer Vision.

     • Process: The pipeline ingested unstructured text and image data, performing OCR (Optical Character Recognition) and entity extraction to "categorize the data (& metadata)" based on Intelligence Community (IC) taxonomies.

  5. Analytical Assessment Generation.

     • This was the core of the engine. I utilized Azure OpenAI Service (GPT-4/GPT-4o).

       • -- In a restricted Azure Virtual Network (VNet) to ensure data residency compliance.

     • Execution: The engine synthesized the scraped data, applied context from the "Digital Twin" repository, and drafted the analytical reports according to specific USAF/IC standard formatting (e.g., HUMINT or SIGINT report templates).

  6. Confidence Scoring (KPI Engine).

     • The system utilized a Multi-Model Ensemble approach: -- Logic: The system assigned a confidence score (1-5) based on:

       • Source Reliability (Static weight)

       • Content Consistency (Time-series analysis)

       • LLM "Self-Correction" (Chain-of-Thought prompting)

     • These KPIs were calculated at the service layer and embedded as metadata into the document header.

  7. -- (6 & 7). Approval & Dissemination.

     • Workflow: The draft was routed to an internal Power Automate workflow.

     • Approver Interface: The approver received a secure notification via Microsoft Teams or an (Azure-integrated portal), where they could review the source evidence, the draft assessment, and the confidence score.

     • Dissemination: Upon approval, the document was automatically sanitized and published to the designated Microsoft SharePoint site, integrated via Microsoft Graph API, ensuring that all security markings were automatically applied based on the classification level.

Week 2 & 3 - Product Setup & Review: (4)

• 1. Establish Test Environment:

     • Customer engineering staff will provision and configure an AWS test environment to Quadrant specifications for the evaluation and provide login credentials to Quadrant.  Quadrant will review the AWS configuration and confirm testing environment is ready for product installation.

  2. Product Setup & Configuration:

     • Once the testing environment is established, tested, and validated, Quadrant will implement the platform to be evaluated. The testing environment and implementation of various platforms to be tested will be performed in entirety, building from an established baseline environment

  3. Customer Product Demonstration and Evaluation Assistance:

     • The customer will provide Quadrant access to a customer test environment and train on product features and usage. Customer will provide a resource to assist with the product evaluation with the competitive product review.

  4. Competitive Analysis Criteria Assessment & Documentation:

     • Armed with the testing environment, implemented platforms, established reporting and evidence collection capabilities, and established criteria for testing, Quadrant will begin the technical competitive analysis. This process is the execution of all the planning and setting up of the conditions, processes, policies, and practices to evaluate and analyze the performance and competitive capabilities

 Week 4 - Technical Competitive Analysis Presentation: (2)

1. Develop Findings Presentation:

   • Synthesize data from market research, Quadrant product testing, and other market discussions to finalize a competitive comparison matrix and develop a written PowerPoint presentation.

2. Present Findings Presentation:

   • Quadrant consultants and market experts will present the PowerPoint deck, provide additional commentary on competitive dynamics, and provide recommendations for Customer corporate and product strategy. The Quadrant project team will deliver these presentations over a videoconference, or, preferably, in-person.

Roadmap -- AI -- by Microsoft.

• BLUF: The AI Strategy Roadmap: Navigating the Stages of AI Value Creation -- URL: The AI Strategy Roadmap 

An Application Architect plays a crucial role in designing, developing, and implementing software applications. 

• R&R:

1. Designing Software Architecture: They create the blueprint (OV-1 to SV-5a, from Reference to Logical Architecture, to the Physical Twin) for software applications, ensuring that the architecture meets both technical and operational requirements.

2. Developing Technical Specifications: They outline the technical details and specifications (SV-5, AV-1, AV-2) needed for the development process.

3. Collaborating with Teams: They work closely with development and operations teams to ensure the application is built according to the architectural plan.

4. Managing the Development Lifecycle: They oversee the entire development process, from initial design to deployment.

5. Ensuring Compliance: They ensure that the application complies with security and performance standards

Case Study: USAF Target App. -- Using Azure AI.

• BLUF: For the USAF, I architected and scaled a high-velocity AI/ML SaaS ecosystem designed as a repeatable "Result as a Service" (RaaS) offering for the U.S. global intelligence community (IC) operations (Navy, Army, USAF, CIA, NSA, NASIC, NATO).

• What Did I Do (Scalable & Repeatable Architecture). -- I utilized M.A.C.H. Architecture (Microservices, API-first, Cloud-native, Headless) and no-code/low-code approach to ensure it could be rapidly deployed and scaled across decentralized maritime (Navy) and aviation (USAF) units. This framework allowed for secure data synchronization across global edge-computing nodes while maintaining high availability. -- Delivered initial version -- via Azure Kubernetes or Docker.

• High-Value & High Impact. (4)

  1. Operational Velocity: Accelerated development time by 80%, slashing the time-to-market for mission-critical features. -- Improved intel training from 8 weeks to 3 weeks (60%) using an OV-1 (High-Level Overview) + a RAG system for a shared Knowledge Base (KB).

     • Impact -- (1) SaaS App: M.A.C.H. Architecture, Auto-generated Intel Package using AI, and low-code/no-code approach. (2) Training: From 8 weeks to 3 weeks.  

  2. Radical Efficiency: Re-engineered workflows to achieve a 75% reduction in workforce staffing requirements through automated AI-driven decision engines.

     • Impact -- 4 to 7 airmen to 1, 3-4 months to 3-4 hours.

  3. Enterprise Resilience: Integrated a Zero Trust Architecture (ZTA) in compliance w/ CISA ZTMM v2 to establish a repeatable security benchmark for global multi-cloud environments (Azure, AWS, GCP), fortifying the ecosystem against state-level threats.

  4. Strategic ROI: As the Chief EA for the 363d ISR Wing & 2 Intel Squadron (15 IS & 17 IS -USAF) conducted a multi-billion-dollar "Application Rationalization" of their technology portfolios with unified enterprise standards to ensure long-term business continuity and scalability across the federal space.