AI Security for E-commerce

AI security for e-commerce means securing and governing the AI and LLM features online retailers ship — product recommendations, support chatbots, fraud detection and dynamic pricing — against the OWASP Top 10 for LLM Applications and the AI governance standards (EU AI Act, NIST AI RMF, ISO/IEC 42001). CyberSigma threat-models your AI stack, tests guardrails and data isolation, reviews governance, and hands you a prioritised, board-ready report. We are CERT-In empanelled and PCI QSA (CEMEA) authorised.

The AI security risks that matter most in e-commerce

E-commerce was an early adopter of customer-facing AI, which puts models directly in front of untrusted users and close to payment and personal data. An assessment is only useful if it targets the risks your AI use cases actually create:

• Prompt injection in customer-facing chatbots that tricks the assistant into leaking data, issuing refunds or making promises you must honour.
• PII and order-data leakage through prompts, logs or model responses.
• Recommendation and pricing models manipulated through poisoned inputs or scraping at scale.
• AI features sitting near the cardholder-data environment, pulling PCI DSS scope into the model layer.
• Over-reliance on AI for fraud or content decisions without human oversight or audit trail.

Which AI standards should online retailers map to?

Beyond the sector-specific risks above, these are the cross-cutting AI standards we assess against:

• EU AI Act — the risk-tiered AI regulation that applies extraterritorially to anyone placing AI systems on the EU market or whose AI output is used in the EU; high-risk and general-purpose AI carry specific obligations.
• NIST AI Risk Management Framework (AI RMF 1.0) — the leading voluntary framework for governing, mapping, measuring and managing AI risk.
• ISO/IEC 42001:2023 — the certifiable AI Management System standard, with ISO/IEC 23894 for AI risk management.
• OWASP Top 10 for LLM Applications — the de-facto checklist for securing LLM and generative-AI features.
• Data-protection and PCI DSS — AI features touch customer personal data and sit near the payment flow, so privacy law and PCI DSS both apply.

What a CyberSigma AI security review covers for e-commerce

We assess how your AI systems behave under attack and how they handle data, not just your model card. In a typical engagement we:

• Threat-model the AI/LLM stack against the OWASP Top 10 for LLM Applications — prompt injection, insecure output handling, sensitive-information disclosure, excessive agency and the rest.
• Test guardrails for real: jailbreak and prompt-injection resistance, input/output filtering, and what the model does with untrusted content from tools, RAG sources and users.
• Review data governance for AI: what personal or sensitive data feeds training, fine-tuning and prompts, the lawful basis for it, and whether customer data leaks across tenants or into a foundation model.
• Secure the model pipeline: access to model registries, secrets and MLOps tooling, supply-chain risk in third-party and foundation models, and protection against model and data exfiltration.
• Assess governance and oversight against ISO/IEC 42001 and the NIST AI RMF: accountability, human oversight, documentation, and evaluation of high-risk use cases.
• Check monitoring and incident response for AI-specific failure modes — abuse, drift, harmful output — not just classic infrastructure alerts.

Representative engagement: an online retailer’s support copilot

A useful way to picture the work: an online retailer’s support copilot could look up orders and issue goodwill credits. We red-teamed it against the OWASP LLM Top 10, proved a prompt-injection path to unauthorised credits, and handed them guardrail and authorisation fixes ordered by risk. This example is representative of how we structure these reviews; named client references are available under NDA on request.

How long does an AI security review take, and what does it cost?

Most AI security reviews run a few weeks, depending on how many models, applications and data flows are in scope. Cost follows that scope rather than a fixed list price, so we run a short, free discovery call, agree the scope in writing, and give you a fixed quote before any work starts. If you are working to a launch or customer deadline, tell us the date and we will tell you honestly whether it is achievable.

Can our AI chatbot be tricked into giving refunds or leaking data?

Often, yes, if it has tool access and weak guardrails — prompt injection is the number-one LLM risk. We test exactly these paths and show you how to constrain what the assistant is allowed to do and disclose.

Does adding AI features change our PCI DSS scope?

It can. If an AI feature can touch cardholder data or sits in the payment flow, it can pull the model layer into PCI DSS scope. As a PCI QSA (CEMEA) authorised firm, we assess that overlap directly.

How do you test a recommendation or pricing model?

We look at data-poisoning and manipulation paths, scraping resistance, and whether outputs can be gamed, alongside the privacy questions around the personal data feeding the model.

How often should we review our AI features?

At least annually, and again whenever you add tool access, change the model, or expose a new AI feature to customers.