Sensitive data exposure
Customer names, emails, account numbers and internal IDs end up in prompts and are forwarded to third-party providers.
Pillar
What is LLM security?
How prompt security, privacy-first routing and PII masking help reduce sensitive data exposure in LLM applications.
A practical reference for engineering and security teams shipping AI features.
Definition
What is LLM security?
LLM security is the practice of reducing risk in applications that send prompts to, and consume responses from, large language models. It is not a single product — it is a set of controls applied at different stages of an LLM request.
Most of the work falls into three buckets: keeping sensitive data out of the prompt (see PII masking), controlling how requests are routed and authenticated to providers (see LLM gateway), and limiting what is retained at any hop between your application and the model (see zero retention).
Risks
Common LLM security risks
Prompt privacy
Even routine prompts can contain regulated or contractual data. Without a control point, every call is an exposure event.
Credential leakage
Provider API keys embedded in clients or scattered across services are difficult to rotate and easy to leak.
Internal copilot data
Employees paste internal documents into chat UIs. Without a gateway, that content goes straight to the model provider.
Customer support workflows
Support agents summarise tickets containing PII. The summary prompt is often the first place data leaves the perimeter.
Audit and retention gaps
Direct provider calls leave no central record of what was sent — and provider-side logs may persist beyond your control window.
Controls
How teams reduce LLM risk
There is no single fix. Teams that take LLM security seriously tend to converge on a similar shape:
- PII masking before the prompt leaves the application — detect personal values and replace them with deterministic placeholders.
- Prompt security policy enforced at a central edge, so behavior does not depend on every client doing the right thing.
- A privacy-first LLM gateway that owns provider routing, key handling and BYOK, instead of scattering credentials across services.
- Limited retention at the gateway itself — structural counters for observability, not raw prompt or response bodies.
- Developer workflows that make the secure path the default path — a single endpoint, the same JSON shape, no per-team reinvention.
How Privian fits
Where Privian fits
Privian is a privacy-first LLM gateway. It focuses on one slice of LLM security: reducing sensitive data exposure in prompts. It does that by detecting supported personal and sensitive values, replacing them with deterministic placeholders, routing the masked prompt to your configured provider using your own keys, and rehydrating the response on the way back.
Privian does not currently claim to block prompt injection, defend against jailbreaks, or guarantee downstream model behavior. Those are separate problems that we do not address yet.
See the AI Security Layer pillar for the category framing, or jump straight to PII masking, prompt security and the LLM gateway.
Related
Related reading
- AI Security Layer (category)
- What is an LLM gateway? (glossary)
- What is PII masking? (glossary)
- What is prompt security? (glossary)
- What is prompt injection? (glossary)
- Article: Prompt security vs. prompt injection
- Article: What is an LLM firewall?
- Article: Privacy-first LLM gateways, explained
- Developer docs
- Concept: AI Security Layer
FAQ
Frequently asked questions
- What is LLM security?
- LLM security is the broad practice of reducing risk in applications built on large language models. In practice it covers data exposure to providers, prompt privacy, credential handling, output handling, and operational controls like routing, audit and retention.
- How is LLM security different from cybersecurity?
- Cybersecurity is concerned with the security of systems and networks generally. LLM security is the subset that focuses on risks introduced by sending prompts to — and receiving outputs from — third-party language models. The threat model shifts from intrusion to data exposure, instruction handling, and downstream behavior of an opaque model.
- Does prompt security mean prompt-injection defense?
- No. The terms are often confused. Prompt security typically refers to controls on what your application sends to the model (privacy, masking, policy). Prompt-injection defense is the separate problem of preventing adversarial input from hijacking model instructions. Privian focuses on the privacy side and does not currently claim prompt-injection defense.
- How can teams reduce sensitive data exposure to LLMs?
- The common controls are: detect and mask personal or sensitive values before the prompt leaves your stack, route requests through a central gateway you control, use your own provider keys (BYOK), and keep no raw prompts or responses at the gateway itself.
- Does Privian handle prompt injection or jailbreaks?
- Not in the current beta. Privian focuses on prompt privacy — masking sensitive data and routing requests with limited retention. Prompt-injection and jailbreak defense are separate problems that we do not claim to address.