The direct answer is to identify the minimum writing and publishing functions the business must continue, export the evidence and configuration needed to reconstruct them, assign recovery roles, and test a restore in a separate workspace. Plan for provider outage, account lockout, bad update, lost connector, corrupted draft, and accidental publication as distinct events.
Define essential functions and recovery targets
List the steps from topic selection through research, drafting, source review, approval, scheduling, publishing, and correction. Mark which steps can pause safely and which have deadlines. Set a recovery time objective for resuming essential work and a recovery point objective for how much recent drafting or approval history the business can lose.
NIST contingency planning guidance recommends identifying business functions, recovery priorities, procedures, roles, and validation. Translate that into named owners for provider access, source archives, editorial approval, publishing credentials, and public corrections.
Preserve the recovery package
Export current prompts or policies, templates, style rules, source maps, approved drafts, revision history, metadata, user roles, connector inventory, and publishing runbook. Store at least one owner-controlled copy outside the primary AI provider. Encrypt sensitive exports, restrict access, and store recovery keys separately.
Do not put provider passwords, API keys, or publishing secrets in the runbook. Record where the approved secret is stored, who can retrieve it, and how to rotate it. Keep a list of dependencies and contacts so recovery does not depend on the person who built the workflow.
Design safe degraded operation
Prepare a manual path for research, drafting, source review, and publication. Use ordinary documents and a checklist when the AI system is unavailable. Require the same source, copyright, privacy, and approval gates; an outage is not permission to publish unverified material. Pause scheduled publication if evidence or ownership cannot be preserved.
The NIST AI Risk Management Framework applies governance, trustworthiness, and evaluation across the AI lifecycle. Define what must be revalidated after recovery: source links, factual claims, opinion labels, privacy controls, permissions, and publishing blocks.
Test realistic scenarios
In a non-production workspace, restore one approved article with its sources, metadata, comments, and approval record. Simulate a provider outage by using the manual path. Simulate account lockout by having the backup owner retrieve the archive and follow the runbook. Simulate a bad update by reverting to the last approved configuration.
NIST's Generative AI Profile recommends documented, iterative testing and evaluation. Record expected result, actual result, recovery time, missing data, permission problems, and corrective action. Do not call the plan tested because a file downloaded successfully; prove that another person can understand and use it.
Recover without duplicating publication
Before resuming automation, inventory queued drafts, scheduled jobs, published pages, and manual work completed during the outage. Use stable article identifiers and publication states to prevent duplicate releases. Restart with one low-risk item, verify the live result, then release the remaining queue gradually.
Maintain the plan
Review after staff, provider, model, connector, policy, or publishing changes. Test at a practical interval and after a significant incident. Keep the recovery package dated, remove obsolete secrets and private source material, and assign deadlines to every failed test.
The plan passes when an authorized backup owner can restore the approved evidence, continue essential work under the same quality gates, reconcile the queue, and return to automation without losing accountability or publishing duplicates.
Sources
- SP 800-34 Rev. 1, Contingency Planning Guide for Federal Information Systems — National Institute of Standards and Technology, May 1, 2010
- AI Risk Management Framework — National Institute of Standards and Technology
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — National Institute of Standards and Technology, July 26, 2024