Indexof Ethical Hacking [TESTED]

For a typical enterprise with 3 critical web apps (monthly → 80), 200 internal hosts (quarterly → 60), 50 non-critical (annually → 20). Weighted average ≈ 67 . 2.3 Depth (D) – Weight 25% The sophistication level of testing. Inspired by PTES (Penetration Testing Execution Standard).

| Frequency | Score Multiplier | Typical Use Case | |-----------|----------------|-------------------| | Continuous (daily) | 100 | Bug bounty + DAST in CI/CD | | Monthly | 80 | Critical APIs / public apps | | Quarterly | 60 | Internal infrastructure | | Bi-annually | 40 | Non-critical internal systems | | Annually | 20 | Low-risk assets | | Less than annually | 0 | None |

Formula: F = (Sum over all assets of [multiplier × asset_criticality_weight]) / Total criticality weight indexof ethical hacking

| Component | Max Score | Calculation | |-----------|-----------|--------------| | External IPs | 30 | (tested IPs / total IPs) × 30 | | Internal IPs | 25 | (tested subnets / total subnets) × 25 | | Web apps | 25 | (tested apps / total critical apps) × 25 | | APIs | 10 | (tested endpoints / total documented endpoints) × 10 | | Mobile apps | 5 | (tested builds / total production builds) × 5 | | IoT/OT | 5 | (tested device types / total types) × 5 |

If an org tests 80% of external IPs, 50% of internal subnets, 100% of web apps, 0% APIs, 100% mobile, 0% OT → C = (24 + 12.5 + 25 + 0 + 5 + 0) = 66.5 2.2 Frequency (F) – Weight 20% How often each asset type is tested. Continuous testing earns highest scores. For a typical enterprise with 3 critical web

The proposed Index of Ethical Hacking (IoEH) transforms subjective opinions (“We do penetration tests”) into a data-driven score from 0 to 100, where 100 represents continuous, adversarial, full-scope testing with zero remediation lag. The IoEH is defined as:

R = max(0, critical_score + high_score - reopened_penalty) Assesses the process quality, not just technical results. Inspired by PTES (Penetration Testing Execution Standard)

| Metric | Weight | Formula | |--------|--------|---------| | Critical findings closed within SLA (e.g., 7 days) | 50 | (closed on time / total critical) × 50 | | High findings closed within SLA (e.g., 30 days) | 30 | (closed on time / total high) × 30 | | Reopened findings rate | -20 | subtract (reopened / total closed) × 20 |

Author: AI Research Desk Date: April 17, 2026 Abstract Ethical hacking has evolved from an ad-hoc practice to a critical component of enterprise security. However, organizations lack a standardized metric to assess the depth, frequency, scope, and maturity of their ethical hacking efforts. This paper introduces the Index of Ethical Hacking (IoEH) , a composite scoring system that measures an organization’s proactive security testing posture. The IoEH comprises five sub-indices: Coverage (C) , Frequency (F) , Depth (D) , Remediation Velocity (R) , and Methodology Maturity (M) . We provide a mathematical model, a scoring rubric, and a practical implementation guide. The IoEH enables security leaders, auditors, and regulators to compare ethical hacking rigor across departments, subsidiaries, or industry peers. 1. Introduction Traditional security metrics focus on vulnerabilities found or patches applied. These lagging indicators fail to capture the proactive capability of an organization to think like an attacker. Ethical hacking—whether performed by internal red teams, external consultants, or bug bounty hunters—varies wildly in quality and usefulness. The central question this paper answers: How can we objectively measure an organization’s ethical hacking effectiveness?