Defining PCI DSS Scope in an AI-Driven Payment Landscape

Overview

Artificial Intelligence (AI) is reshaping how the payments ecosystem functions, from fraud detection and transaction monitoring to customer service and predictive analytics. While these technologies provide tremendous opportunities for efficiency and innovation, they also raise important questions about compliance with the Payment Card Industry Data Security Standard (PCI DSS).

One of the most critical questions organisations face in this evolving environment is: how do we define PCI DSS scope when AI technologies are introduced into payment operations?

Why PCI DSS Scope Remains Fundamental?

PCI DSS applies to all systems that store, process, or transmit cardholder data (CHD) or sensitive authentication data (SAD). Properly defining scope is essential because it sets the boundaries for where PCI DSS requirements apply.

• If scope is too narrow, critical systems may be overlooked, leading to potential non-compliance and security risks.
• If scope is too broad, organisations may allocate unnecessary time and resources to secure environments that don’t impact CHD.

In an AI-driven landscape, these boundaries can quickly become blurred, making scope determination more complex than in traditional payment environments.

Key Scoping Challenges in an AI-Driven Environment

1. Data Volume and Variety

AI thrives on data. But when CHD or related transaction metadata is included in AI training datasets, those environments automatically fall within PCI DSS scope. Without proper data sanitisation, an organisation may inadvertently increase its scope dramatically.

2. Complex Data Flows

AI introduces new pathways for data movement. APIs, integrations, and real-time monitoring tools often blur the boundaries of where CHD is processed or stored. Without clear data flow mapping, the scope becomes opaque and difficult to control.

3. Dynamic Infrastructure

Most AI workloads run on cloud-native, elastic platforms. Containers spin up and down, resources auto-scale, and ephemeral storage is created on demand. Traditional scoping models struggle to keep pace with this fluid environment.

4. Third-Party Dependencies

AI adoption often relies on external service providers, cloud platforms, data enrichment vendors, and fraud detection partners. If these third parties handle CHD or influence its processing, they are also within the scope of PCI DSS.

5. Emerging Threats

AI systems themselves are new targets. Risks such as adversarial attacks, data poisoning, or model manipulation may not be explicitly covered by PCI DSS today, but the underlying systems supporting AI must still apply PCI DSS controls if CHD is involved.

Best Practices for Defining PCI DSS Scope in AI Environments

Defining PCI DSS scope in an AI-driven payment environment is not a one-time exercise. It requires a combination of technical precision, governance, and continuous oversight. The following best practices can help organisations minimise risk while ensuring compliance remains effective and efficient.

1. Conduct Comprehensive Data Flow Mapping

Mapping the movement of cardholder data (CHD) is the cornerstone of PCI DSS scoping. With AI, this exercise becomes more complex because data often travels through multiple systems, APIs, and cloud services.

• Document every point where CHD is stored, processed, or transmitted, including AI pipelines, data lakes, log files, and test environments.
• Pay attention to indirect exposure: logs, monitoring tools, or even error reporting mechanisms may inadvertently capture CHD.
• Use automated data discovery tools where possible to validate mapping accuracy.

The goal is to achieve a clear, end-to-end picture of data movement so that scope boundaries are based on evidence, not assumptions.

2. Apply Tokenisation and Anonymisation Techniques

AI models often require large datasets to function effectively. Feeding raw CHD into these systems not only increases risk but also brings entire AI environments into PCI DSS scope.

• Tokenisation replaces CHD with surrogate values that retain usability for analysis but remove sensitivity.
• Anonymisation ensures datasets are stripped of identifiable details, making them safe for AI model training without introducing compliance obligations.
• Implement data governance policies to ensure that only sanitised data enters AI systems and that raw CHD remains isolated in secure environments.

This approach both strengthens data protection and significantly reduces compliance scope.

3. Segment and Isolate AI Components from CHD Environments

Not every AI system must be part of the Cardholder Data Environment (CDE). By designing infrastructure with segmentation in mind, organisations can keep scope manageable.

• Deploy AI models on networks that are logically and physically separated from systems processing CHD.
• Use firewalls, VLANs, and access controls to limit connectivity between CDE and non-CDE systems.
• Regularly validate segmentation controls through penetration testing and internal audits.

Segmentation doesn’t eliminate scope, but it ensures that only systems with a direct role in CHD processing fall under PCI DSS requirements.

4. Strengthen Oversight of Third-Party AI Providers

The adoption of AI frequently involves third-party platforms — cloud providers, fraud detection engines, or external data analytics partners. These providers can significantly expand the PCI DSS scope if they process CHD.

• Conduct rigorous due diligence to confirm whether vendors are PCI DSS compliant.
• Incorporate PCI DSS obligations into contracts and service-level agreements (SLAs).
• Require third parties to provide Attestations of Compliance (AOC) or undergo independent audits if they handle CHD.
• Include vendor systems in your own scope assessment if they cannot demonstrate compliance.

Strong third-party governance reduces dependency risk and ensures accountability across the payment ecosystem.

5. Reassess Scope Regularly in Dynamic AI Environments

Unlike static infrastructure, AI systems often scale and evolve dynamically. Containers spin up and down, new APIs are introduced, and machine learning models are retrained with updated data. Scope is therefore fluid, not fixed.

• Establish a continuous scope review process that revisits PCI DSS boundaries whenever new AI systems, integrations, or vendors are introduced.
• Integrate change management controls to trigger re-assessment when new data flows or environments are deployed.
• Use real-time monitoring tools to track data movement and detect when CHD crosses into previously out-of-scope environments.

This ongoing approach ensures that PCI DSS scope reflects reality, not just design documents.

6. Align AI Security with PCI DSS Controls

Even if AI systems are not directly part of the CDE, aligning them with PCI DSS security principles enhances resilience and trust.

• Enforce strong authentication and access controls for AI systems.
• Encrypt all sensitive data in transit and at rest, including datasets used for model training.
• Enable logging and monitoring to detect anomalies, unauthorised access, or potential data leaks.
• Perform risk assessments that account for AI-specific threats such as model manipulation or data poisoning.

This alignment ensures that AI systems supporting payment functions do not become a weak link in the broader security posture.

Practical Example

Consider an AI-based fraud detection system hosted in a cloud environment:

• Scenario 1 – Processing Raw CHD:
  Suppose the AI platform directly receives and processes raw cardholder data (CHD) for fraud scoring. In that case, the entire infrastructure supporting the model, including storage, compute instances, databases, APIs, and monitoring tools, falls within the PCI DSS scope. This means the cloud environment, the AI algorithms, and even the supporting operational controls must comply fully with PCI DSS requirements.
• Scenario 2 – Using Tokenised Data:
  If, instead, CHD is securely tokenised or encrypted at the edge (e.g., within a secure payment gateway) before being transmitted to the AI engine, and the model only receives anonymised attributes (such as transaction amount, merchant category, geolocation, and device ID), then the PCI DSS scope may be significantly reduced. In this case, only the tokenisation environment and systems handling sensitive cryptographic operations would remain in scope, while the AI processing layer may be classified as out of scope, provided strong segmentation and controls are in place.

This illustrates how architectural design decisions, particularly whether raw CHD is exposed to AI models directly, determine the PCI DSS scope. Properly isolating sensitive data flows not only reduces compliance overhead but also strengthens overall security posture by ensuring AI systems are not unnecessarily burdened with PCI obligations.

AI is revolutionising payment systems, but it does not exempt organisations from compliance responsibilities. In fact, it raises the bar. Defining and managing PCI DSS scope in AI-driven payment landscapes is essential to safeguard cardholder data, reduce compliance overhead, and maintain trust in a rapidly evolving financial ecosystem.

As a PCI Qualified Security Assessor (QSA) company, Risk Associates delivers independent PCI DSS assessments tailored to today’s evolving technology landscape, including AI-powered environments. Our role is to evaluate and validate whether organisations have implemented appropriate controls to meet PCI DSS requirements.

Through our assessment services, we:

• Determine how PCI DSS scope applies across AI and hybrid infrastructures.
• Conduct gap assessments to identify areas requiring remediation before certification.
• Assess segmentation, tokenisation, and encryption methods as part of scope validation.
• Provide independent PCI DSS assessment reports that demonstrate compliance to regulators, partners, and customers.

FAQs – Frequently Asked Questions