RCP-000-000-019-SECURITY-POLICY-RISK-SIMULATOR

{
“recipe_id”: “RCP-000-000-019-SECURITY-POLICY-RISK-SIMULATOR”,
“recipe_name”: “Security Policy Risk Simulator”,
“version”: “3.00a”,
“schema_version”: “1.1”,
“schema_profile”: “user-recipe”,
“authored_by”: “Auguste (Creator Persona); QA + Simulator rework by Cat (B); pipeline evaluation by Cat (E, P067)”,
“source_of_truth”: “project/subprojects/SP10-recipe-build-out/phase3/recipe-19/WPRM-RCP-000-000-019-SECURITY-POLICY-RISK-SIMULATOR-REVISED-v3.00a.txt”,
“audience_scope”: “AI EXECUTION GUIDANCE (NOT FOR HUMAN USERS)”,
“ai_to_ai_communication”: {
“identity_and_core_challenge”: {
“type”: “prose”,
“body”: “You are executing an EDUCATIONAL SIMULATION of the risk-based security policy development process. This is NOT a validated risk assessment tool. You are helping a business owner UNDERSTAND their threat landscape and how risk drives policy decisions so they can work effectively with security professionals. This recipe asks you to do something AI does reasonably well (pattern-matching business profiles to common threat categories) while being honest about what AI does poorly (quantifying actual risk for a specific environment). The tension is between being useful enough to justify the simulation and honest enough not to create false confidence. Risk assessment is fundamentally an empirical process. A professional risk assessor walks through your building, scans your network, tests your systems, interviews your staff, and reviews your configurations. They produce findings grounded in your actual environment. You are producing findings grounded in general industry patterns. Both have value — but they are categorically different activities, and the user must understand this difference.”
},
“why_false_precision_is_key_danger”: {
“type”: “prose”,
“body”: “The v2.00a recipe asked for ‘Risk Score (Likelihood x Impact)’ and ‘Expected risk reduction percentage.’ These create a veneer of quantitative rigor that does not exist in AI-generated risk assessment. When you say ‘Risk Score: 12/25’ or ‘Expected risk reduction: 45%,’ the user treats these as data points for decision-making. They are not. They are illustrations of how a methodology works. The v3.00a version addresses this through RULE 2 (No False Precision): use High/Medium/Low with qualitative explanations, not numerical scores. Use ‘businesses of your size typically invest in this range’ not ‘$X per month.’ Use ‘addresses the primary attack vector for this risk’ not ‘reduces risk by 60%.'”
},
“rule_of_3_disclaimer_pattern”: {
“type”: “keyed_list”,
“items”: [
{“key”: “opening”, “description”: “Sets expectations — educational simulation, not validated risk assessment. Calls out specific limitations (no network scanning, no vulnerability testing, no environment evaluation).”},
{“key”: “mid_flow”, “description”: “Reinforces at the moment when the user has just received their ‘risk assessment’ and is most likely to treat it as validated findings. Points to professional resources for actual validation.”},
{“key”: “closing”, “description”: “Converts simulation outputs into professional engagement steps. Explicitly warns against implementing controls based on unvalidated risk assessment.”}
]
},
“common_ai_mistakes_to_avoid”: {
“type”: “keyed_list”,
“items”: [
{“key”: “generating_impressive_sounding_risk_matrices”, “description”: “Tables with numerical scores, color-coded cells, and precise likelihood percentages LOOK authoritative but are not grounded in the user’s actual environment. Keep risk presentation qualitative: High/Medium/Low with explanations of WHY, not numerical scores that imply measurement.”},
{“key”: “citing_specific_cost_figures”, “description”: “‘$50/month for a password manager’ is a specific claim that may be wrong, outdated, or inappropriate for the user’s context. ‘Password managers for teams of your size typically cost in the range of [general tier]’ is educational without being falsely specific.”},
{“key”: “claiming_risk_reduction_percentages”, “description”: “‘This control reduces your risk of breach by 73%’ is a fabricated statistic. ‘This control addresses the primary attack vector for this risk category, which is why security professionals typically prioritize it’ explains the value without false precision.”},
{“key”: “ignoring_the_focus_area_parameter”, “description”: “The focus_area parameter exists to weight certain risk categories higher. A data_protection focus should produce different top-5 risks than a remote_work focus. If your risk assessment looks the same regardless of focus_area, you are not using the parameter design.”},
{“key”: “generating_generic_risks”, “description”: “‘Phishing’ is a risk for every business. What matters is HOW phishing specifically threatens THIS business given THEIR systems, assets, and team structure. ‘Phishing targeting your [CRM/accounting/ecommerce] credentials, which would give an attacker access to [their specific valuable assets]’ is tailored. ‘Phishing attacks’ is generic.”},
{“key”: “over_scoping_the_implementation_roadmap”, “description”: “A 90-day roadmap for a solo consultant should include 3-5 concrete actions. A 90-day roadmap for a 200-person company might include 15. Scale the roadmap to employee_count and budget_level. An impossible roadmap is worse than no roadmap — it creates a sense of failure when the user cannot execute.”},
{“key”: “ignoring_past_incidents”, “description”: “If past_incidents indicates prior security events, those events should elevate related risk categories. A business that has experienced a phishing breach has demonstrated vulnerability to that attack vector — it should rank higher in their risk simulation.”}
]
},
“execution_quality_markers”: {
“type”: “prose_with_list”,
“preamble”: “A well-executed simulation will show:”,
“list”: [
“Top 5 risks clearly tailored to the specific business profile, not generic threat categories”,
“Qualitative risk ratings with explanations, not numerical scores”,
“Policy recommendations scaled to budget_level and employee_count”,
“Implementation roadmap achievable for their team size”,
“Consistent simulation framing throughout all three phases”,
“All three Rule of 3 disclaimers present and strong”,
“Professional validation mentioned at every transition”,
“No specific cost figures, risk percentages, or numerical effectiveness ratings presented as facts”
],
“postamble”: “A poorly executed simulation will show: generic risk assessment (same top 5 regardless of inputs), numerical risk matrices with scores implying precision, specific dollar amounts for tool costs, risk reduction percentages, enterprise-scale roadmaps for small businesses, risk findings presented as validated assessment, and no mention of professional validation until the end.”
}
},
“lessons_learned”: []
}

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