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Protecting Cardholder Data in Modern Payment Systems: A Threat-Model-Based Comparative Analysis of Tokenization and Encryption for PCI DSS Scope and Breach-Risk Reduction

Year: 2026
Type: article
Abstract: Payment card data is among the most valuable targets in cybercrime, and the systems that process it are heavily regulated under the Payment Card Industry Data Security Standard (PCI DSS). Two design goals dominate its protection: reducing the scope of systems subject to the standard, and reducing the usefulness of card data if it is stolen. A family of techniques (point-to-point encryption, vaulted and vaultless tokenization, format-preserving encryption, and network or EMV payment tokenization) pursues these goals differently. This article compares them under an explicit method: we define a threat model and deployment assumptions, assess each technique separately against security, compliance, and integration criteria, and validate the comparison against three representative payment architectures rather than relying on qualitative positioning alone. We correct a common overstatement: tokenization does not automatically remove systems from PCI DSS scope. The tokenization system itself (vault, detokenization, and keys) remains in the cardholder data environment; a token-only system may be considered out of scope only if the PAN is not computationally recoverable from tokens, the environment is segmented from detokenization and key material, no other cardholder-data channels exist, and that segmentation is validated, with high-value tokens potentially remaining in scope. On the stakes, we report observed survey figures by edition: IBM?s Cost of a Data Breach Report put the financial-sector average at about USD 5.56 million in its 2025 edition and USD 6.29 million in its 2026 edition, the latter roughly 26% above that year?s USD 4.99 million cross-industry average; we do not attribute a quantified reduction to any single technique. We close with a decision framework and qualify the quantum-era discussion with explicit assumptions.
DOI: 10.21275/sr261104111214
Language: English
Open Access status: diamond
OpenAlex ID: W7225734186
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