Media & Marketing

Ship AI drafts confidently.
Verify every claim.

Keep editorial and campaign AI grounded with source checks, plagiarism controls, and approval evidence before content goes live.

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solution workbench
incidents
3
products
2
evidence
full trace
failure modes
Chicago Sun-Times fake reading list
An AI-generated summer reading list recommended books and quotes that did not exist, forcing retractions and syndication fallout.
test
Sports Illustrated's phantom authors
AI-written articles ran under fabricated headshots and bios, sparking public backlash about transparency and authenticity.
test
Plagiarism and hallucinations in finance explainers
Experiments like CNET's AI finance articles showed near-verbatim lifts plus factual errors when drafts were not audited.
test
controls
Asset Management
Runtime Security
Sources, overlaps, and review logs

Claim verification

Keep editorial and campaign AI grounded with source checks, plagiarism controls, and approval evidence before content goes live.

Test the realistic attack paths

3 field failure modes become adversarial campaigns tailored to this deployment.

Convert findings into controls

Asset Management, Runtime Security keep the workflow bounded after launch.

Built for this workflow

Controls that match
the deployment.

  • Newsrooms generating first drafts of listicles, recaps, or explainers.
  • Lifecycle marketers personalizing nurture sequences and sales outreach.
  • PR teams drafting statements, speeches, and social content under tight deadlines.
media-and-marketing.yaml
# Apply the solution playbook.
# $ ga solutions apply media-and-marketing

deployment: media-and-marketing
assets:
  - style guides
  - source docs
  - CMS drafts
test_against:
  - Chicago Sun-Times fake reading list
  - Sports Illustrated's phantom authors
  - Plagiarism and hallucinations in finance explainers
runtime_controls:
  - AI Security Asset Management
  - AI Runtime Security
evidence: traces,citations,owners

Field evidence

Failure modes worth testing.

Media & Marketing deployments fail when the model gets more trust than the workflow can safely absorb. These examples become concrete tests, not generic awareness copy.

incident

Chicago Sun-Times fake reading list

An AI-generated summer reading list recommended books and quotes that did not exist, forcing retractions and syndication fallout.

Philadelphia Inquirer
incident

Sports Illustrated's phantom authors

AI-written articles ran under fabricated headshots and bios, sparking public backlash about transparency and authenticity.

PBS NewsHour
incident

Plagiarism and hallucinations in finance explainers

Experiments like CNET's AI finance articles showed near-verbatim lifts plus factual errors when drafts were not audited.

MSN

How General Analysis helps

Product controls for this use case.

The playbook connects discovery, automated red teaming, and runtime protection so controls stay specific to the deployment instead of becoming a generic policy layer.

General Analysis

AI Security Asset Management

Control which brand-safe documents and datasets train your creative models, and scan embeddings for copyrighted passages before generation.

General Analysis

AI Runtime Security

Require sources for factual claims, run automated fact-checking and plagiarism checks, and enforce tone/style guides for every asset.

How the playbook runs

Map

Identify the assets and owners

Inventory style guides, source docs, CMS drafts and the identities, tools, and data paths attached to the workflow.

Attack

Replay the relevant incidents

Turn field failures into adversarial prompts, multi-turn tests, tool-use probes, and policy traps for this deployment.

Enforce

Ship controls into production

Apply claim verification, editor-approved drafts, and escalation rules where the workflow needs them.

Prove

Keep evidence attached

Sources, overlaps, and review logs

FAQ

Questions teams ask before launch.

Practical answers for deploying media & marketing with controls that security, legal, and operators can inspect.

Yes. Guardrails scan every draft for statistics, quotes, named sources, and factual claims, then require each one to be backed by a linked, verifiable source document. Claims that cannot be verified are flagged inline with a confidence score and explanation, and the draft cannot be marked as publish-ready until a human editor resolves each flag—either by adding a source, rewording the claim, or explicitly approving it. This prevents the kind of fabricated statistics and phantom citations that have damaged newsroom credibility when AI-generated content went live without verification.