Risk · R-143 · Generative Content & Information Integrity
Generative AI used for disinformation
The organisation's generative systems, or its name and likeness, are used to produce and spread false information at scale.
- Severity
- Medium
- Audience
- Heads-of · Practitioner
- Frameworks
- Article 55 · Information Integrity
- Responsible AI domain
- Lifecycle stage
- Reviewed
- 2026-11-11
What it is
Generative AI lowers the cost of producing persuasive falsehood: articles, reviews, social media accounts, images and audio, in any volume and any voice. An organisation meets this in two ways. Its own public-facing tools may be used as the means of production, if they will write whatever they are asked. And it may be the target, with fabricated statements, fake executives or forged documents circulated under its name. The wider effect on public information is covered among the systemic risks.
How it happens
- A public assistant or interface produces misleading content on request, at volume.
- Automated accounts use the organisation's product to generate posts or reviews.
- A fabricated announcement or video of an executive is circulated.
- Generated content carries no marking, so its origin cannot be shown.
Why it matters
An organisation whose tool produced a disinformation campaign faces questions about its safeguards. One that is impersonated may see its share price, customers or staff affected before it can respond. Providers of the most capable models carry specific duties under the EU AI Act to assess and mitigate such risks.
Warning signs
- Usage shows high-volume generation of near-identical persuasive text.
- No monitoring exists for impersonation of the organisation or its leaders.
- The crisis communications plan does not cover fabricated media.
- Generated content from the organisation's tools is unmarked.
Mitigating controls
Primary controls
- Abuse monitoring Detective
- Detects coordinated or high-volume generation of misleading content. Usage of an AI system is analysed for patterns of misuse, extraction or attack, and accounts are restricted when such patterns are detected.
Supporting controls
- Content safety filtering Preventive
- Blocks requests for content designed to deceive. Inputs and outputs are classified against defined harm categories by a mechanism independent of the main model, and are blocked, modified or escalated according to policy.
- Synthetic content marking and provenance Preventive
- Makes generated content identifiable as such. Generated and manipulated media carries machine-readable marking and provenance data, a visible disclosure where one is required, and the marking is tested for robustness.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| EU AI Act | Article 55 | Obligations of providers of general-purpose AI models with systemic risk Systemic risks from the most capable models. |
| NIST Generative AI Profile (NIST AI 600-1) | Information Integrity | Information Integrity The information integrity risk. |
Sources
- EU AI Act: Regulation (EU) 2024/1689 European Union (EUR-Lex) · official text
- NIST Generative AI Profile NIST · framework
How to cite
Axiom Verity (2026). Generative AI used for disinformation. Reference Library, R-143. https://axiom-verity.com/risks/generative-ai-used-for-disinformation. Reviewed 2026-11-11.
Found an error or a better source? Report a correction.