AI & LLM Security for SaaS & Technology

AI & LLM security for SaaS protects the AI systems behind your products and operations from prompt injection, data leakage, model poisoning and excessive agency. CyberSigma red-teams these SaaS AI flows and maps governance to SOC 2, ISO/IEC 27001 and 42001, and GDPR/DPDP 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 SaaS AI risk: cross-tenant RAG leakage

A product copilot uses retrieval (RAG) over customer data. A flaw in the retrieval layer lets Tenant B's query surface Tenant A's confidential documents — a multi-tenant data breach delivered through the AI feature. We red-team RAG access control and tenant isolation, prompt injection in copilots, insecure handling of AI-generated code, and excessive agency in product agents.

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

We adversarially test your SaaS 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 SaaS & Technology

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

• SOC 2 and ISO 27001 controls extended to AI features.
• ISO/IEC 42001 AI Management System for responsible AI.
• Tenant isolation and access control in the RAG/vector layer.
• NIST AI RMF for AI risk management.

Do AI features affect our SOC 2 or ISO 27001 scope?

Yes — AI features that process customer data are in scope. We extend your control evidence to cover the model, prompts and retrieval layer, and can stand up ISO/IEC 42001 for AI governance.

How do you test multi-tenant AI for data isolation?

We attempt cross-tenant retrieval and prompt-injection attacks against your RAG and agent flows to prove tenant isolation holds, then map fixes and evidence.

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.