{
  "id": 17421913,
  "title": "Decision Firewall: what building an AI governance framework taught me",
  "url": "https://urgent.news/2026/10/31/decision-firewall-what-building-an-ai-governance-framework-taught-me",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-31T15:30:00.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/anuj-sadani/decision-firewall-what-building-an-ai-governance-framework-taught-me-io1"
  },
  "original_language": "en",
  "account": "A refund example was used to illustrate the decision-making process when building an AI governance framework. The scenario involved a duplicate charge claim, which required verification of a second payment record and cancellation eligibility based on timing and usage. A refund policy allowed for automatic refunds for verified duplicates and unused subscriptions cancelled within seven days, with eligible amounts up to ₹1,000 processed automatically and larger amounts requiring review. A daily automated limit of ₹10,000 was set to handle excess demand, with simulated payments used for demonstration purposes.\n\nThe article describes the development of Decision Firewall, an open-source reference for AI action authorization and audit. The author started with a refund-recovery example to explain the framework's purpose and used simulated payments to test its functionality. The refund recovery process involved interpreting the request, authorizing the exact action, dispatching and losing the response, and recovering without a second refund. The framework made provisions for idempotency keys and execution state durability to ensure accurate refunds.\n\nAs the project progressed, the author incorporated more features, such as history ingestion, dataset snapshots, named Python rules, and optional context retrieval and telemetry. These features aimed to make the framework more versatile and useful for developers. However, the author later realized that the framework's benefits needed to be demonstrated compared to other methods of implementing AI governance. This realization prompted a reevaluation of the design and implementation process.",
  "summary": "A refund-recovery example, measured comparisons and lessons from building Decision Firewall, an open-source reference for AI action authorization and audit. This is an adapted edition of the original article. It preserves the section argument and evidence; use the source link for the published version and any interactive material. Read the original article 01 / The starting problem A refund is…",
  "key_points": [
    "Decision Firewall is an open-source AI governance framework",
    "Developed using a refund example to illustrate decision-making",
    "Features include idempotency keys, execution state durability"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}