๐Ÿ” Track 06: AI Cybersecurity โณ Not Yet Started (Planned Weekend R&D)
EXPERIMENT #26

AI Website Security Scanner & Remediation Advisor

An automated penetration auditing scanner that evaluates security headers, TLS configurations, CSP, CORS rules, exposed endpoints, and outdated libraries with actionable fixes.

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The Problem

What difficult challenge is this experiment tackling?

Most web developers leave critical security misconfigurations (missing CSP, permissive CORS, exposed `.git` folders, outdated jQuery) undetected until a breach occurs.

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The Hypothesis

What do I believe autonomous AI & engineering can accomplish?

Automating non-destructive passive security auditing and pairing vulnerability findings with verified code patches allows developers to achieve an A+ security rating in minutes.

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System Architecture & Workflow

End-to-end design, data pipelines, and component interaction
01 Target URL Input
02 Passive Security Audit Engine (Headers, TLS, CSP, Open Endpoints, CVEs)
03 Severity Classifier (CVSS v3)
04 AI Remediation Engine (generates .htaccess / Nginx configs)
05 Executive Security Audit PDF.
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Benchmark & Empirical Metrics

Accuracy โ€ข Latency โ€ข Cost โ€ข Token Usage โ€ข Human Intervention

Executes 65+ security parameter checks in under 20 seconds; generates copy-paste ready Apache/Nginx fix configs.

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Security, Privacy & Ethical Guardrails

Risk mitigation, strict boundaries, and sandbox controls

Strict ethical boundary: Passive and non-destructive testing only on user-owned or authorized domains.

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Lessons Learned & Observations

Empirical findings from prototyping and testing

Misconfigured Content Security Policies (unsafe-inline / missing frame-ancestors) account for over 70% of common audit failures.

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Next Experiment Iteration

Upcoming modifications, scaling factors, or new hypotheses

Implement dynamic API fuzzing for authenticated GraphQL and REST endpoints.