{
  "title": "AI and LLM assessment scope worksheet",
  "version": "2026-11-05",
  "purpose": "Blank planning worksheet, not an assessment result or contractual scope.",
  "fields": [
    { "field": "Inventory", "record": "Application, model and deployment versions; retrieval stores; MCP servers; tools; external processors; system and data owners." },
    { "field": "Trust boundaries", "record": "Caller identities, roles, tenant boundaries, data classes, tool permissions and approval gates." },
    { "field": "Authorized cases", "record": "Approved synthetic inputs, expected allow/deny decisions, permitted read and write effects, test budget, monitoring and stop conditions." },
    { "field": "Evidence", "record": "Configuration, sanitized trace, policy decision, data or action reached, expected result and observed result. Record inconclusive cases and stochastic variation." },
    { "field": "Remediation", "record": "Control owner, proposed application or permission change, human approval and deployment reference." },
    { "field": "Retest", "record": "Original boundary case, legitimate control, approved alternate paths, tested version, outcomes and remaining limitations." }
  ],
  "exclusionsToAgree": ["Training infrastructure and training-data access", "Model weights and extraction testing", "Third-party services without written permission", "Production writes, destructive actions and load tests", "Regulatory certification and universal model-safety claims"],
  "source": "https://getfailsafe.com/ai-pentesting#assessment-sequence"
}