Pricing your bad days and how to build an economic model for security decisions

Qualys's guidance on “pricing your bad days” and building an economic model for security decisions — as detailed by VP Risk Technology EMEA Ivan Milenkovic — offers a concrete framework for quantifying cyber‑risk losses and aligning spend with measurable outcomes. For website owners, this means moving beyond vague security budgets to a data‑driven approach that directly ties protection costs to the financial impact of specific threats, such as credential‑stuffing bots or ransomware that could corrupt llms.txt files. By mapping loss scenarios (e.g., a breach that exposes visitor analytics or a DDoS event that disables bot‑tracking scripts) to the assets that generate revenue, owners can justify investments in AI‑driven bot detection, WAF rules, and continuous compliance monitoring. This economic lens also helps prioritize which bots to block versus which to allow for legitimate AI services, reducing unnecessary friction for users while tightening defenses where the monetary risk is highest. **Actionable tips:** 1. **Create a loss‑scenario matrix:** Identify three worst‑case events (e.g., unauthorized scraping of llms.txt, credential‑stuffing attacks, or ransomware encrypting site backups) and assign a dollar value to each based on downtime, brand damage, and data loss. 2. **Integrate bot‑traffic metrics into the model:** Use your analytics platform to segment AI‑generated traffic, then calculate the cost per 1,000 malicious requests versus legitimate AI interactions; adjust WAF thresholds accordingly. 3. **Publish an updated llms.txt with risk‑based directives:** Include explicit `User‑Agent` rules that deny known malicious crawlers and specify rate limits for AI bots, then monitor compliance through your bot‑tracking solution to ensure the economic model reflects real‑time protection effectiveness.