AI Security for SaaS

AI security for saas means securing and governing the AI and LLM features SaaS and software companies ship — in-product copilots, RAG assistants and AI agents built on customer data — 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 authorised (CEMEA, Asia Pacific and the USA).

The AI security risks that matter most in saas

For SaaS, AI features are usually multi-tenant and built on customer data, so the headline risks are tenant isolation, training-data boundaries and the prompt-injection surface a product-embedded LLM creates. An assessment is only useful if it targets the risks your AI use cases actually create:

• Customer data leaking across tenants through a shared model, vector store or prompt context.
• Customer data silently used to train or fine-tune shared models, breaching contracts and privacy law.
• Prompt injection through customer-supplied content in RAG pipelines and AI agents.
• Excessive agency in agents with access to customer systems and tools.
• Foundation-model and plugin supply-chain risk passed straight through to your customers.

Which AI standards should SaaS and software companies 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 law and customer contracts — multi-tenant SaaS must prove customer data is not leaked across tenants or used to train shared models, which both privacy law and DPAs require.

What a CyberSigma AI security review covers for saas

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: a B2B SaaS in-product copilot

A useful way to picture the work: a B2B SaaS company’s in-product copilot answered questions over each customer’s own data. We red-teamed it against the OWASP LLM Top 10, focused on cross-tenant isolation and training-data boundaries, and delivered a remediation plan that unblocked enterprise security reviews. 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.

How do you test that customer data cannot leak across tenants?

We probe the model, retrieval layer and prompt context for cross-tenant leakage, and check that isolation holds under prompt injection and adversarial inputs — the question enterprise buyers ask most.

Can you confirm we are not training shared models on customer data?

Yes. We trace whether customer data flows into training or fine-tuning sets, and check it against your contracts (DPAs) and the applicable privacy law.

How do we satisfy enterprise security reviews for our AI features?

We map findings to the frameworks buyers ask about — OWASP LLM Top 10, NIST AI RMF, ISO 42001 — and give you the evidence and remediation plan to pass the review.

How often should we review our AI features?

At least annually, and again whenever you add a new AI agent, change the model, or expand what customer data the feature can reach.