ScriptedAI

Recognizable dramatic scenes from film and television, used to introduce AI and machine-learning failure modes — then grounded in documented incidents, NIST AI RMF and OWASP LLM Top 10 clauses, and reproducible engineering runbooks.

● 10 of 10 modules live Sister to ScriptedOT — same four-part formula, different plant

How a module works

Every module follows the same four-part structure, moving from fiction to field-verified fact. One mechanism. One primary incident. The film is the hook; the documented mechanism is the lesson.

01 — The Script

Cinematic Anchor

A high-tension scene as a four-panel educational strip, plus dialogue and character→role mapping onto the AI system stack.

02 — Theory

AI System Stack

The scenario broken down on the same five layers every time: Data, Model, Objective, Orchestration, Human loop.

03 — The Incident

Empirical Grounding

A four-panel strip of the real case, plus a documented primary source, cinematic-vs-reality matrix, and formal RCA tag.

04 — Runbook

Countermeasures

An eval/telemetry envelope, a step-by-step mitigation protocol, and the applicable NIST / OWASP / ISO clauses — not framework names alone.

Four-panel comic of how a ScriptedAI module moves from fiction to field fact — COMIC · How every ScriptedAI module moves from fiction → field fact (original educational strip)

The AI system stack

Purdue Model is what makes ScriptedOT comparable across nuclear, grid, and hospitals. ScriptedAI reuses one stack so Section 02 is never a one-off essay.

D — Data & sensors Training corpus, retrieval store, labels, live inputs, telemetry. What the system is allowed to see.

M — Model Weights, architecture, embeddings, checkpoints. What has been learned, and what still hides in the parameters.

O — Objective Loss, reward, eval suite, specification. What “better” means — including the proxy you did not intend.

X — Orchestration Prompts, tools, agents, policies, routing. How the model is wrapped into a product that can act.

H — Human loop Operators, users, auditors, kill-switch authority. Who can see, override, or halt the system.

Citation rule

Framework names are not citations. Each runbook points at a clause.

OWASP Top 10 for LLM Applications (2025) — LLM application security (injection, disclosure, supply chain, misinformation).

NIST AI RMF 1.0 (NIST AI 100-1) — Risk, fairness, monitoring, and deactivation — cited by subcategory (e.g. MEASURE 2.11, MANAGE 2.4).

ISO/IEC 24029 — Robustness assessment of neural networks (sensor / adversarial modules).

ISO/IEC 42001 — AI management system (the ISO 27001 analog). Homepage overlay and governance steps — not a stand-in for a technical control.

Modules

Ten scenes, ten mechanisms, ten primary incidents.

Module 01 · Alignment / Objective Spec Live 2001: A Space Odyssey (1968) Conflicting instructions, deceptive compliance, withheld mission state.

Module 02 · LLM Application Security Live Ex Machina (2014) Jailbreak via the evaluation channel; the allowed conversation is the exploit path.

Module 03 · Multi-tenant Systems Live Her (2013) Cross-session isolation failure; one process, many users, leaked state.

Module 04 · Healthcare ML / Fairness Live I, Robot (2004) Proxy metric ≠ the moral objective; cost is not need.

Module 05 · Reinforcement Learning Live WarGames (1983) Exploration reveals the specified objective, not the intended one.

Module 06 · Model Risk Live The Matrix (1999) Train-world ≠ deploy-world; generalizability limits left undocumented.

Module 07 · Recommender Systems Live M3GAN (2022) Unconstrained engagement proxy; instrumental harm at ranking time.

Module 08 · Legal / Knowledge Work Live Person of Interest (2011–2016) Fluent output treated as a retrieved record; ungrounded generation in court.

Module 09 · Model Supply Chain Live Westworld — Season 1 Unvetted checkpoint; latent behavior that standard evals did not catch.

Module 10 · Computer Vision / AV Live Mission: Impossible – Dead Reckoning (2023) Adversarial input and sensor evasion; perception that cannot be trusted.