Scalable AI Systems for Federal Infrastructure
Learn how to architect end‑to‑end AI systems that scale securely across mission workloads, data tiers, and accreditation boundaries.
🟤System decomposition: data, model, feature store, inference, observability

🟤Trust boundaries and data classifications (PII/FOUO) in federal contexts

🟤Reliability patterns (HA, failover, DR) for AI pipelines

🟤Model lifecycle: from experimentation to ATO‑ready services

🟤Documentation artifacts for tech volumes, evaluation plans, and SLAs

🟤Cost‑aware scaling: right‑sizing compute, storage, and throughput

Integration with Federal IT & Cloud (AWS GovCloud, Azure Gov)
Map AI services to existing federal networks, identity, logging, and compliance controls in AWS GovCloud and Azure Government.
🟤Reference architectures for GovCloud/Azure Gov deployments

🟤Identity & access (C2S patterns, Conditional Access, IAM, PIM)

🟤Network controls (private endpoints, Transit Gateway/ExpressRoute, IL‑zones)

🟤Data ingress/egress, cross‑domain solutions, and KMS/HSM keying.

🟤FedRAMP/NIST control inheritance and evidence generation

🟤Deployment automation (CloudFormation/Bicep/Terraform) and drift control

AI Hardware & Edge Computing in Defense Environments
Select and configure GPU/TPU and specialized accelerators for lab, production, and edge/denied‑connectivity scenarios🟤GPU vs. TPU vs. CPU: sizing, throughput, and thermal/power constraints

🟤Edge patterns: offline inference, model distillation, and secure update channels

🟤Ruggedized compute, SWaP considerations, and TEMPEST‑aware placement

🟤Data synchronization: intermittent links, store‑and‑forward, checksum integrity

🟤Telemetry at the edge: compression, privacy, and anomaly signaling

🟤Supply‑chain and firmware integrity checks for hardware trust

AI Security: Adversarial Risk, Breaches, and MLOps
Build a defensible AI security program that anticipates adversarial inputs, model/data breaches, and pipeline compromise, with MLOps controls to match.
🟤Threat modeling for AI systems (data, model, code, deploy surface)

🟤Adversarial testing (prompt/patch attacks, data poisoning, extraction) and breach pathways

🟤Monitoring: drift, anomaly, misuse detection, and integrity beacons

🟤MLOps hardening: artifact signing, SBOMs, reproducibility, rollbacks

🟤Incident response for AI: contain/rotate/retrain vs. decommission decisions

🟤Human‑centered red teaming and discernment scaffolds (e.g., reflection layers) to reduce reputational and behavioral risk.

Zero-Trust Architecture and AI Integration
Apply Zero‑Trust principles across identity, device, network, application, and data planes for AI services, agents, and automation.
🟤Mapping AI components to ZTA pillars and control families

🟤Policy enforcement (OPA/ABAC), least‑privilege service meshes, and micro‑segmentation

🟤Secure service‑to‑service auth (mTLS, workload identity, token lifetimes)

🟤Data‑centric controls: tokenization, differential privacy, redaction, watermarking

🟤Continuous authorization and posture‑based access for AI workloads

🟤Evidence collection/attestation for ATO and continuous monitoring 

Hands-on: Building a Model in TensorFlow or PyTorch (Optional Lab)
Build, evaluate, and deploy a small model with a hardened pipeline and runtime suitable for GovCloud/Azure Gov.

🟤Training a starter model (TF or PyTorch) with sound data hygiene

🟤Packaging for secure inference (containers, artifact signing, SBOM)

🟤CI/CD for ML: feature store, model registry, staged rollouts, canary/blue‑green

🟤Observability: metrics, logs, traces, and model‑specific health checks

🟤Deployment to AWS GovCloud/Azure Government with KMS‑backed secrets

🟤Post‑deploy monitoring: drift, bias checks, and rollback playbooks