AI & LLM Security for E-commerce & Retail

AI & LLM security for e-commerce protects the AI systems behind your products and operations from prompt injection, data leakage, model poisoning and excessive agency. CyberSigma red-teams these e-commerce AI flows and maps governance to PCI DSS for payments, GDPR and India's DPDP Act for customer data, plus OWASP LLM Top 10, NIST AI RMF, ISO/IEC 42001 and MITRE ATLAS. We are CERT-In empanelled and PCI QSA (CEMEA) authorised.

A real e-commerce AI risk: the manipulated shopping assistant

A shopping assistant is coaxed into applying unauthorised discounts, leaking another customer's order details, or driving an action it shouldn't through a connected tool. Combined with bot-driven abuse, AI features become a fraud and privacy surface. We red-team chatbot data leakage, prompt injection, agent/action abuse, and manipulation of recommendation or pricing models.

What we test (OWASP Top 10 for LLMs + MITRE ATLAS)

We adversarially test your e-commerce LLM and GenAI applications the way a real attacker would:

• Prompt injection — direct and indirect (documents, web pages, tools).
• Sensitive information disclosure — PII, secrets and system-prompt leakage.
• Insecure output handling — XSS, SSRF and code execution from model output.
• Excessive agency — agents/plugins taking unauthorised or destructive actions.
• Training-data poisoning and model/data supply-chain risks.
• Jailbreaks, guardrail bypass, model extraction and denial-of-wallet.

AI governance & compliance for E-commerce & Retail

We map AI controls to your sector's obligations and the global AI frameworks:

• PCI DSS for AI touching payment data.
• GDPR / DPDP for customer personal data in AI.
• Bot, fraud and abuse controls around AI agents.
• ISO/IEC 42001 + NIST AI RMF.

Can a shopping chatbot be a security risk?

Yes — it can be manipulated to leak order/customer data, apply unauthorised actions, or be abused at scale. We red-team these flows and map fixes to PCI DSS and privacy law.

How do you secure AI recommendation and pricing models?

We threat-model for poisoning and manipulation, test data and access controls, and assess monitoring so adversaries can't skew recommendations or pricing.

How does AI red-teaming differ from normal penetration testing?

Traditional pen testing targets code and infrastructure; AI red-teaming additionally targets the model's behaviour via prompts, poisoned context and connected tools to make it leak data or act without authorisation. Mature programmes use both — we provide each and can combine them.