An AI writing incident can be a fabricated source, undisclosed generated material, confidential input, copied expression, harmful wording, incorrect automation, or a draft published without approval. Responding well requires both content correction and operational discipline. Preserve enough evidence to learn what happened while limiting further exposure.
Open an incident record
Create a single incident record with a unique identifier, reporter, discovery time, affected document or URL, current publication status, owner, and a plain-language description. Record the visible harm or risk without speculating about root cause. Capture the prompt or instructions, source list, generated draft, human edits, approval history, tool or model label shown by the service, automation run, and downstream destinations. Do not paste secrets or unnecessary personal data into the incident ticket.
NIST's Generative AI Profile says that logging, recording, and analyzing generative AI incidents can support information sharing and that change records, version history, and metadata can help incident responders. It also emphasizes documenting and reviewing third-party inputs and plugins because access control may be inconsistent. Preserve provenance and integration details, not just the final prose.
Contain the problem
Pause the affected publishing job, shared template, prompt library, or integration. Unpublish or clearly quarantine content when continued exposure creates harm, but retain an access-controlled evidence copy. Search for other outputs produced by the same run, prompt, source packet, tool configuration, or approval route. Restrict changes to the smallest affected scope; avoid disabling unrelated systems unless evidence supports it.
Use a severity rule based on impact. A private draft with a formatting error is different from a public page containing false safety advice or confidential customer information. Escalate legal, privacy, security, or safety concerns to the appropriate owner rather than trying to resolve them only as an editing problem.
NIST SP 800-61 Revision 3 integrates incident response across preparation, detection, response, and recovery activities. Its purpose includes improving the efficiency and effectiveness of incident detection, response, and recovery. For a writing workflow, that means the incident record should continue through correction and verified recovery, not stop after the bad sentence is removed.
Analyze and correct
Separate confirmed facts from hypotheses. Determine whether the failure came from an unsupported prompt, inaccessible source, model behavior, human edit, missing gate, stale template, permission problem, or publication automation. Re-research disputed claims from reliable sources. Compare the affected content with the last approved version and identify every downstream copy.
Correct only from verified evidence. Repeat the normal factual, copyright, disclosure, security, and editorial checks. If the content was public, decide whether readers need a correction note and preserve the previous version internally. Do not silently rewrite a consequential error when transparency is warranted. Verify caches, feeds, newsletters, and scheduled posts so the incident is not reintroduced.
Communicate and recover
Notify people based on impact and responsibility. Give editors the affected scope and containment status; give technical operators the automation or integration details; give business or legal owners the confirmed impact and decisions needed. Avoid forwarding raw prompts or documents more broadly than necessary. If people were directly affected, provide accurate, limited information and a correction or recourse path.
NIST's AI RMF Playbook recommends maintaining records of errors, near misses, incidents, impacts, and responses. It also recommends documenting detected negative impacts and regularly reviewing response plans. Close the incident only after the corrected content is verified at every intended destination and the risky workflow is either repaired, restricted, or retired.
Complete the record
Add the root cause, affected items, actions, decision owners, recovery evidence, communications, elapsed time, and residual risk. Record what prevented or delayed detection. Turn the lesson into a concrete control: required source manifests, named versions, human approval, disclosure checks, permission changes, automated link validation, or a stop condition for high-risk topics. Assign the action and due date, then test it.
A complete AI writing incident record shows what happened, what evidence supports the conclusion, how spread was stopped, how content was corrected, and how recurrence will be reduced. That is more useful than a vague note that the AI made a mistake.
Sources
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — National Institute of Standards and Technology, July 26, 2024
- NIST SP 800-61 Rev. 3 — National Institute of Standards and Technology, April 3, 2025
- AI RMF Playbook: Manage — National Institute of Standards and Technology