I’m building a small-scale, university-level prototype that recommends the most suitable cybersecurity framework for a payment-gateway provider after it supplies basic company data through a simple GUI. The tool must consider only cybersecurity frameworks—no governance or risk models—with the initial comparison set restricted to CIS Controls v8, PCI DSS, NIST 800-53, NIST 800-61, and MITRE ATT&CK. Scope of the build • Data layer: curate or simulate representative “company data”—size, transaction volume, geography, existing controls—and prepare it as the training / inference set. • Model: tune an appropriate NLP or rules-augmented model so that, given the company profile, it returns the single best-fit framework plus a short justification. Python with scikit-learn, spaCy, or a lightweight transformer is fine; I’m open to your suggestions. • Interface: a minimal GUI (Streamlit or similar) where users paste or upload company parameters and receive the framework recommendation instantly. • Validation: design measurable accuracy tests—cross-validation, confusion matrix, precision / recall—so I can cite quantitative performance in my paper. • Documentation: comment the code thoroughly and supply a brief methodology report I can reference in my dissertation. Acceptance criteria 1. GUI loads locally, accepts company data, and outputs exactly one of the five frameworks with rationale. 2. Model reaches an agreed-upon accuracy baseline (you’ll help define this during data design). 3. All source code, datasets, and a short report are delivered within 2-2.5 weeks. Timeline I need the full package—code, GUI, validation results, and write-up—within a month, so please outline a clear milestone plan when you respond The Ai's response has to be divided into 2 a business view and a technical view as its targeted for board level on why that framework is best suited for the company and the frameworks have to be in regards to payment gateways.