Using AI Safely In DoD And Federal Work

Generative AI has moved into everyday government work faster than the guardrails around it. A staff officer summarizes a report, a cyber analyst drafts detection logic, a logistics planner explores options — and each is one paste away from a spill, a fabricated citation, or an unauditable decision. The hard part was never picking the right model; it's that the same prompt can be safe in one enclave and a reportable incident in another. This course teaches the judgment that comes before speed, for the people who sit at the keyboard in DoD, federal agencies, and supporting contractors. No machine learning background is assumed — only that you work under real constraints, including classification, CUI, PII, proprietary data, and a clock. You learn to sort content rather than documents, matching data types to approved environments and finding the first irreversible step before you paste anything. You turn the five DoD Responsible AI principles into artifacts a reviewer can actually check, confront how generative AI fails through fluent errors and prompt injection, and match verification to what would cause harm if it were wrong. From there the course builds the governance around those habits: human oversight sized to consequence rather than a signature line, the legal and policy buckets that decide permissibility, fairness measured as disparate error rates, and a trust gate of edge cases, red teaming, and stop conditions kept alive through the NIST AI RMF loop. You finish with three mission scenarios triaged to a go or no-go, and a playbook you can run under pressure: approved environment before tool, classify before you share, oversight that matches the consequence, verify before the output travels, log the decision, and escalate what you can't resolve.