LEARNING PATH // SECURITY LEARNERS, POLICY READERS, SIMULATION DESIGNERS, AND ANALYSTS
Separate three meanings of AI kill chain, analyze function-specific authority, follow defensive interruption points, and design safe instructional simulations.
Estimated total: 215 minutes
This learning path is repository-authored curation of public educational routes. It records no participant testing, accepts no files or free text, sends no choices to a remote service, and grants no source authentication, specialist approval, accessibility certification, publication authority, operational authority, activation authority, production approval, or tuning authority.
A neutral map of AI-system attacks, AI-enabled military decision systems, kill chains, kill webs, JADC2, lifecycle human control, cybersecurity, procurement, evidence, and safe simulation.
/psyops/ai-kill-chainsDefinitions for kill chain, kill web, AI-enabled function, autonomy, evidence stage, human control, and defensive interruption.
/psyops/ai-kill-chains/terms-and-analytical-boundariesA non-operational defensive model of Recon, Poison, Hijack, Persist, Impact, and the Iterate/Pivot loop in agentic systems.
/psyops/ai-kill-chains/attacks-against-ai-systemsA neutral, non-operational explanation of where AI can assist sensing, fusion, classification, prioritization, assignment, guidance, and assessment—and why those functions do not all transfer the same authority.
/psyops/ai-kill-chains/ai-enabled-military-targetingA lifecycle map showing how authority, assumptions, and accountability can be distributed across policy, design, data, testing, mission configuration, activation, supervision, engagement, and review.
/psyops/ai-kill-chains/human-control-across-the-lifecycleAn evidence-centered exploration of upstream human removal: how filtering, fusion, ranking, defaults, and hidden discarded observations can precondition a later human decision.
/psyops/ai-kill-chains/algorithmic-gatekeeperA synthetic three-mode comparison showing how manual, AI-assisted, and preauthorized defensive architectures distribute speed, workload, and authority differently.
/psyops/ai-kill-chains/90-seconds-human-command-versus-machine-speedA function-specific analysis of what an uncrewed or automated system may do when communications are unavailable, delayed, jammed, obstructed, or unable to scale.
/psyops/ai-kill-chains/lost-link-authority-after-silenceA human-factors module about nominal approval, automation bias, queue pressure, defaults, evidence opacity, and the conditions required for meaningful judgment.
/psyops/ai-kill-chains/rubber-stamp-paradoxA planning-room documentary about geographic, temporal, evidentiary, target-profile, communications-loss, abort, and deactivation rules configured before a live event.
/psyops/ai-kill-chains/mission-policy-before-activationA high-level, evidence-disciplined explanation of terminal defensive chains and Iron Dome’s detect–track–predict–prioritize–assign–intercept–update loop.
/psyops/ai-kill-chains/terminal-defense-and-iron-domeA strict evidence framework for claims that a fielded system selects a specific target and initiates destructive force without target-specific human approval.
/psyops/ai-kill-chains/current-use-evidence-and-unknownsA synthetic crisis simulation showing how automation bias, correlated sensor error, communications delay, preauthorization, and interacting defensive systems can accelerate a local mistake into a wider escalation.
/psyops/ai-kill-chains/last-human-decision-and-systemic-escalationAn event-driven instructional architecture for desktop and WebXR that teaches causality, uncertainty, defensive intervention, and governance without becoming an attack-execution or targeting platform.
/psyops/ai-kill-chains/safe-3d-and-vr-simulation