AI Prompt Injection Incident Budget Planner

Quantify the cash buffer you need for prompt-injection fallout. Provide daily prompt volume and the percentage of interactions that pierce guardrails to forecast monthly incidents, required SLA credits, analyst labor, and the total reserve you should hold to make users whole quickly.

Average interactive prompts or sessions served per day.
Percent of prompts that bypass guardrails (enter 0.004 for 0.004%).
Defaults to $750 if blank to reflect typical SLA credits.
Defaults to $250 if blank for analyst triage time.
Defaults to 30 days if blank.

Scenario planner only. Align outputs with your platform SLA language, insurance coverage, and legal team guidance.

Examples

  • 85,000 prompts/day, 0.004% success rate, defaults ⇒ Expected incidents per month: 102.00 • Monthly reserve recommendation: $102,000.00 USD • Contractual credits: $76,500.00 USD • Investigation labor budget: $25,500.00 USD
  • 120,000 prompts/day, 0.05% success, $1,000 payout, $400 labor, 31 days ⇒ Expected incidents per month: 1,860.00 • Monthly reserve recommendation: $2,604,000.00 USD • Contractual credits: $1,860,000.00 USD • Investigation labor budget: $744,000.00 USD

FAQ

How should I estimate the injection success rate?

Blend results from red-team exercises, production telemetry, and emerging threat reports to derive a conservative rate.

Does the calculator assume incidents are evenly distributed?

Yes. Re-run the model with higher daily prompt counts if you experience peak traffic windows that could cluster incidents.

Can I include human review saturation limits?

Use the investigation labor input to raise per-incident cost once analysts need overtime or external support.

Additional Information

  • Multiply the success rate by prompt volume to translate guardrail metrics into staffing and cash needs.
  • Including labor cost keeps the reserve aligned with SOC 2 incident response playbooks that require human review.
  • Adjust the payout input if your contracts offer tiered credits for repeated incidents within a billing cycle.

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