How is LLM security different from application security?

It overlaps. LLM security adds prompt-level concerns — masking, prompt security, prompt injection — that traditional appsec doesn't cover.

Is encryption enough?

No. Encryption protects data at rest and in transit but does not stop a provider from seeing the prompt contents.

Where do most LLM security failures come from?

Sensitive data leaking into prompts, missing prompt-level policy, and lack of an enforcement point for all AI traffic.

Glossary

What is LLM security?

LLM security is the discipline of controlling what data, prompts and policies reach a large language model in production.

Definition

LLM security is the discipline of controlling what data, prompts and policies reach a large language model in production.

Definition

LLM security — short definition

Why it matters

Why this matters

AI features increasingly handle real customer data. LLM security defines how that data is protected, how policy is enforced, and how prompts are audited end-to-end.

AI features increasingly handle real customer data. LLM security defines how that data is protected, how policy is enforced, and how prompts are audited end-to-end.

How it works

Layered AI controls— No single layer is sufficient. Each compensates for the others' gaps.

Framework

LLM security process

01
Mask sensitive data
PII masking at the edge prevents personal data from reaching providers.
02
Enforce prompt policy
Prompt security applies structural and content rules at the gateway.
03
Minimize retention
Zero-retention guarantees keep raw prompts and responses out of storage.

Related concepts

Implementation

Learn how this works in Privian

From definition to implementation, docs and architecture — the same idea at different layers.

FAQ

Frequently asked questions

How is LLM security different from application security?: It overlaps. LLM security adds prompt-level concerns — masking, prompt security, prompt injection — that traditional appsec doesn't cover.
Is encryption enough?: No. Encryption protects data at rest and in transit but does not stop a provider from seeing the prompt contents.
Where do most LLM security failures come from?: Sensitive data leaking into prompts, missing prompt-level policy, and lack of an enforcement point for all AI traffic.

Where to go next

From definition to implementation

This entry is part of the Privian content library. If you are evaluating how this concept is implemented in practice, the links below are the most direct paths.

AI Security Layer in Privian

How Privian addresses the prompt-privacy slice of LLM security in the request path.

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