AI Security & Guardrails Services for LLM Applications

Prompt injection is when text in a user message or a retrieved document tricks the model into ignoring its instructions, leaking data, or calling a tool it should not. It is dangerous because the attack hides in normal-looking content, and a model that can act on the world can be steered into harmful actions. It is the top LLM risk for a reason.

No, and anyone claiming otherwise is overselling. It can be reduced sharply with layered defenses: input filtering, strict output validation, least-privilege tool access, and human approval on high-impact actions. We design so that even a successful injection cannot do much damage, because the model was never given the permissions to do harm in the first place.

Guardrails are checks around the model. Input guardrails screen what goes in, output guardrails validate what comes out before a user or system sees it, and tool guardrails limit what actions the model can take. They turn an open-ended model into a system with defined, enforceable boundaries, which is what makes it safe to deploy.

We redact PII before it reaches the model where possible, use zero-data-retention or private model deployments so prompts are not stored or used for training, and validate outputs to catch anything sensitive slipping through. Data leakage is often a design problem, so we review what the model can see and store, not just what it says.

Red-teaming is deliberately attacking your own system before an outsider does. We attempt prompt injections, jailbreaks, data exfiltration, and tool misuse against the OWASP LLM Top 10, then document what worked and fix it. A system that has never been attacked in testing will be attacked in production, and that is a worse place to find out.

Least privilege first. An agent gets only the tools it needs, each tool checks its own inputs, and high-impact actions require human approval. Tool calls run in a sandbox with rate limits and logging. The goal is that even a compromised agent cannot delete data or move money, because those actions were gated, not freely available.

Some checks add latency, so we place them by risk. Cheap input filters run on everything, heavier validation runs on high-impact outputs, and low-risk paths stay fast. We measure the added latency and let you decide the trade-off per action, rather than applying the same heavy checking everywhere and blaming the model for being slow.

Security is the technical layer that stops attacks at runtime: injection defense, guardrails, sandboxing, red-teaming. Governance is the policy and documentation layer: risk classification, audit trails, and regulatory mapping. You need both, and they connect, but this page is about the technical defenses that keep a live system from being exploited.

Before launch is ideal, so security is designed in rather than bolted on, but we also assess and harden systems already in production. A pre-launch red-team and guardrail review is far cheaper than an incident. If you are already live without these defenses, that is the most urgent case, and where we usually start.