Secure LLM use in media and entertainment: keep unreleased titles and talent names out of AI prompts

Shadow is for studios, broadcasters, publishers, music labels, games companies and agencies whose creative, marketing and business teams use large language models on material that has not been released. It runs on your own laptops and servers, replaces working titles, production codenames, talent and character names, deal parties and contact details with consistent fakes before a request reaches the AI provider, and restores them in the answer. It hides what a project is and who is on it; it does not hide the story.

What data leaks into AI tools in media and entertainment

Workflow	What typically ends up in the prompt
Script coverage, notes and rewrites	Working title, character names, attached talent, full scenes
Marketing and release planning	Unannounced titles, release windows, campaign codenames, partner brands
Talent and business affairs	Talent names, agents, fees, contract terms, personal contact details
Localisation and subtitles	Dialogue from unreleased episodes, episode titles
Games and music	Unannounced game titles, track lists, featured artists, label deals
Audience and subscriber analytics	Subscriber names, emails, viewing histories in exports

The risk is a spoiler or an early announcement, a broken NDA, or a leaked deal, often with direct financial consequences.

How Shadow protects it

Shadow detects sensitive values in every outbound AI request and uploaded file, swaps them for fakes from a persistent persona, keeps the mapping in an encrypted local vault, and restores the answer, including streamed responses.

Data	How Shadow handles it
Working titles, production and campaign codenames	Dictionary terms, consistent fakes
Talent, characters, crew	person: terms for known names; the name detector, NER sidecar or Presidio for free text
Studios, distributors, brand partners	org: terms
Agents’ and talent emails, phones, addresses	Built-in detectors
Scripts, treatments, decks, budgets	DOCX, PPTX and XLSX masked in place; PDF text extracted and masked
Subscriber exports	report intent with typed tokens such as [PERSON_0042]
Internal hostnames and API keys	hostname and secret detectors

organization = ["Northlight Pictures LLC"]
terms = [
  "Project Nightjar",                      # production codename
  "term:The Long Winter",                  # working title
  "person:Ava Brennan", "person:Marcus Hale", # attached talent
  "person:Captain Ilsa Varga",             # character
  "org:Globex Streaming",                  # distributor
]
internal_domains = ["northlight.example"]

[files]
pdf = "text"
images = "block"     # stills, storyboards and screenshots are not masked

Add names as they are attached to a project:

shadow terms add "person:Jun Park" "term:Episode 104 - Glasshouse"

What masking does not hide

• The story, dialogue and plot are content. A distinctive premise can identify a project even with every name replaced.
• Images, video and audio are not masked. No OCR.
• Names not in your term list depend on detection. Free-text names are found by the name detector and, with better recall, the NER sidecar or Presidio, but detection is best effort. Run shadow scan on a sample script before relying on it.

Regulations and frameworks

Requirement	How Shadow helps
Studio and production content security programmes, TPN	A technical control for AI tools: local, no telemetry, minimal interception, encrypted vault. Not an assessment or a substitute for studio requirements
NDAs and talent agreements	Keeps talent, title and deal-party names out of third-party AI services
GDPR and US state privacy laws for subscriber data	Data minimisation and pseudonymisation before data leaves; pseudonymised data remains personal data for you (Recital 26)
ISO/IEC 27001, SOC 2	Evidence of a control on AI data flows

Not legal advice

This page describes technical capabilities, not legal conclusions. Shadow does not satisfy content security, contractual or privacy obligations on its own. Check production contracts and studio policies before sending unreleased material to any AI service.

Example workflows

Script notes on a scene

What the writer sends (all data fictional):

Give notes on this scene from The Long Winter (Project Nightjar). Ava Brennan plays
Captain Ilsa Varga; we want it tighter before Globex Streaming sees the cut.

What the provider sees (your fakes will differ):

Give notes on this scene from Lumeth Draane (Velora Quilent). Ying Chen plays
Captain Hui Yang; we want it tighter before Zhilan Media Co., Ltd. sees the cut.

The notes come back referring to Captain Ilsa Varga and The Long Winter. The scene text itself is unchanged.

Marketing deck review

A marketer uploads a PPTX for an unannounced campaign. Title, codename, partner brand and talent names in the slide text runs are replaced, the deck stays a valid presentation, and suggestions come back with the real names. Images on the slides are not masked, so with images = "block" any embedded screenshot uploaded separately is refused.

Subscriber churn analysis

An analyst attaches a CSV of subscribers and viewing data and asks “Analyse these subscriber records: which titles drive cancellations?”. The request reads as a report, so the report intent tokenises subscriber names and emails; title names in your term list get consistent fakes. The analysis is restored locally.

Deployment patterns

Pattern	When to use it
Endpoint install for writers, producers and marketers: shadow install --apply	API-backed chat clients and desktop apps that honour the system proxy or PAC file
Gateway for internal tools: http://127.0.0.1:8877/gw/anthropic and similar	Coverage, localisation and marketing assistants you build
Sidecar in Kubernetes	Product features such as recommendations or support bots. See Docker and Kubernetes

Consumer web chat apps such as chatgpt.com are not built-in intercepted hosts, so give teams an approved API-backed client. There is no central admin console yet; distribute shadow.toml and term lists through your existing tooling. Shared vaults for teams working on one production are on the roadmap; today you copy persona.json, vault.bin and vault.key.

Related

• Redact documents before uploading to AI and shadow AI governance
• Consulting and professional services and legal
• File masking, detection, security model
• Detection quality: benchmarks. Other approaches: compare

Frequently asked questions

Can writers and producers use AI on unreleased scripts without leaking the title?

Shadow replaces the titles, production codenames, character and talent names you list as terms, plus detected people, emails and phone numbers, before the request leaves. The story itself is content and still reaches the model. Whether a script may be sent to an external AI service at all is a decision for the production and its contracts.

Does Shadow mask screeners, stills or video?

No. Shadow masks text and text inside DOCX, XLSX, PPTX and PDF uploads. Images and video are not masked and there is no OCR; set images = "block" so stills and screenshots cannot be uploaded through Shadow.

Does Shadow help with TPN or studio content security requirements?

It can be a documented technical control for AI tools: it runs on your machines, has no telemetry, decrypts only known AI hosts and keeps the mapping in an encrypted local vault. It does not give you a TPN assessment result or satisfy a studio's requirements on its own.

How do we protect a production codename?

Add it as a dictionary term, for example shadow terms add "Project Nightjar" "person:Ava Brennan". The term always maps to the same fake, so long conversations and agents stay consistent, and the answer comes back with the real name.

Will AI feedback on a script get worse if names are replaced?

Usually not much. Fakes are realistic names and titles from one coherent persona, so dialogue and structure still read naturally. Feedback that depends on a real-world person or franchise will lose that context.