What is Lakera? An overview of the AI security platform

It feels like every company on the planet is racing to plug Generative AI and Large Language Models (LLMs) into, well, everything. And while that’s been a fascinating shift to watch, it’s also created a whole new security headache. We’re not just talking about the usual cyberattacks anymore. Now, we have to worry about weirdly specific threats like prompt injection, data leakage, and sneaky model manipulation that can twist a helpful AI assistant into a serious liability.

As these new risks pop up, a new class of tools is emerging to handle them. Lakera is one of the big names in this space, building a platform from the ground up for AI security. Its recent acquisition by Check Point has pretty much solidified its position as a major player. So, what exactly is Lakera, and how does it all work? Let’s break it down.

What is Lakera?

At its heart, Lakera is a security platform built for the strange new world of LLMs, generative AI apps, and the autonomous agents they power. The whole point of Lakera is to let companies use AI without constantly worrying that something will go wrong. It’s designed to cover the entire lifecycle of an AI application, from the early testing stages all the way to protecting it in real-time once it’s live.

The company was founded by AI folks who came from places like Google and Meta, so they have a deep understanding of how these systems work. With offices in Zurich and San Francisco, they’ve built a reputation for being a serious, developer-focused security provider. The platform really boils down to two main products: Lakera Guard, for real-time protection, and Lakera Red, which helps teams squash security bugs before an application ever gets released.

Key features of the Lakera platform

Lakera’s power doesn’t come from a single magic bullet. It’s all about a layered security approach that combines a real-time defense system, proactive testing tools, and a steady stream of threat data gathered from a massive community.

Lakera Guard: Real-time runtime protection

You can think of Lakera Guard as a bouncer for your LLM. It stands at the door, checking every user prompt before it’s allowed to reach your model. The Guard API inspects inputs for threats on the fly, and its main jobs are to:

• Stop prompt injection: This is a big one. It detects and blocks attempts by users to trick or "jailbreak" the LLM. It prevents the model from doing things it shouldn’t, like ignoring its own safety rules or running harmful commands.

• Prevent data leakage: It serves as a safety net to keep the model from accidentally revealing sensitive information. This could be anything from customer PII and passwords to internal company secrets.

• Handle content moderation: It filters both what users type in and what the model says back. This helps keep conversations free of toxic, harmful, or just plain weird content, ensuring your AI stays on-brand and professional.

One of the most impressive things about it is how fast it is. Users like Dropbox have reported response times under 50 milliseconds. That means you can add a serious layer of security without making your users wait, which is a huge deal for user experience.

Lakera Red: Proactive risk-based red teaming

While Lakera Guard is your live-in-the-moment protection, Lakera Red is all about finding problems before your app goes live. It’s a tool built for security teams to essentially "red team" their own AI systems, which is a fancy way of saying they get to attack it themselves to find weak spots.

Lakera Red runs a whole battery of simulated attacks to see how the model reacts. By doing this in a controlled way, teams can pinpoint vulnerabilities, whether they’re in the model’s logic or in the way it handles data. After it finds a problem, Lakera Red gives you clear, actionable advice on how to patch it up, helping you harden your app against attacks you’d otherwise only discover in the wild.

Lakera Gandalf: Crowdsourced threat intelligence

This might be the most unique part of the whole Lakera setup. On the surface, Gandalf is a surprisingly fun (and addictive) cybersecurity game that’s been played by over a million people. But it’s much more than that; it’s a brilliant engine for gathering threat intelligence.

Every time someone plays Gandalf and tries to trick the AI, they’re contributing to a massive, global red team effort. The game logs every new attack pattern and clever workaround, feeding that data directly back into Lakera’s defense models. With a library of over 80 million adversarial examples, this feedback loop means Lakera’s security is always learning and adapting to the latest tricks hackers are trying.

This video demonstrates a user attempting to bypass the LLM protections in the Gandalf challenge created by Lakera.

Who uses Lakera? A look at real-world use cases

A growing number of Fortune 500 companies are turning to Lakera, especially those in highly regulated fields like banking and finance where data security isn’t just a good idea, it’s a legal requirement. Its enterprise-grade protection has made it a popular choice for businesses that are building customer-facing AI applications and can’t afford any mistakes.

Securing enterprise LLM applications with Lakera: The Dropbox case

One of the best public examples comes from how Dropbox uses Lakera Guard to protect its AI-powered features. When the Dropbox team started using LLMs for things like smart search and document summarization, they knew security had to be a top priority. They needed something fast, effective, and, crucially, something they could run inside their own infrastructure to guarantee user privacy.

They looked at a few different options but landed on Lakera for a couple of key reasons:

1. In-house deployment: They could run Lakera Guard in a Docker container as an internal microservice. This was a dealbreaker because it meant no user data ever had to leave their network.

2. Low latency: It was fast enough to meet their performance standards, so adding security didn’t mean slowing down the product for their users.

3. Effective protection: It did its job well, aprotecting their LLMs from prompt injection and helping them moderate content, which was essential for maintaining trust in their new AI features.

The Dropbox story is a perfect illustration of how a technical team can use Lakera to solve a very real, infrastructure-level security challenge.

Beyond Lakera security: Ensuring AI is accurate and trustworthy

Blocking malicious inputs is a massive piece of the puzzle, but it’s not the whole game. Once you’ve made sure the bad stuff stays out, you still have to make sure the right stuff comes out.

Security platforms like Lakera are vital for protecting the technical side of an LLM. But business teams have a different set of worries. They need to be sure their AI agents are only answering questions using approved company information, like a public help center or internal documentation. That’s where a platform like eesel AI comes in. It offers a simple, self-serve way for anyone to build AI agents that are grounded in your company’s knowledge, giving you both safety and accuracy.

The challenge of implementing Lakera

If you’re thinking about using a tool like Lakera, it’s good to have a realistic idea of what it takes to get it running. It’s a powerful tool, but it’s not quite a magic wand you can wave over your application.

The need for technical expertise

Let’s be honest: setting up Lakera isn’t a five-minute job for your support manager. As the Dropbox example makes clear, this is a tool for a technical team. The implementation process usually involves working with APIs, deploying and managing infrastructure like Docker containers, and weaving the service into your existing application pipeline. This requires time from developers or a dedicated security team, which can be a hurdle for smaller teams or those trying to move fast.

Lakera security is just one piece of the AI trust puzzle

Lakera does an excellent job for security pros, but what about the business teams who are on the hook for the AI agent’s performance? Their concerns are different. They need to know if the AI is actually helpful, if it’s giving correct answers, and how it will behave when thousands of real customers start using it.

To build an AI experience that people can truly trust, you need to pair strong security with practical, business-level controls. For example, before an AI agent ever talks to a customer, a support lead should be able to test it on thousands of past support tickets to see how it would have performed. This kind of simulation, a core feature of eesel AI, can help forecast its resolution rate and find gaps in its knowledge. It allows teams to launch AI with confidence, not just from a security perspective, but from a performance one, too.

Lakera pricing

Lakera has two main pricing tiers for different kinds of users. The "Community" plan is free, which is perfect for solo developers or small teams who want to kick the tires and see how it works. The catch is that it’s limited to 10,000 requests per month and doesn’t have the more advanced features you’d need for a full-scale business application.

For bigger companies, there’s the "Enterprise" plan. This plan is fully customizable and includes everything from self-hosting options to premium support and advanced security tools like SSO. The only thing is, you have to get in touch with their sales team for a custom quote. This can slow down the process for teams that prefer the straightforward approach of a self-serve business plan.

Feature	Community	Enterprise
Price	$0 / month	Custom (Contact Sales)
Requests	10k / month	Flexible
Maximum Prompt Size	8k tokens	Configurable
Hosting	SaaS	SaaS or Self-hosted
Support	Community	Enterprise-level
Advanced Features	Not included	SSO, RBAC, SIEM integration

Building a complete AI trust and safety stack with Lakera

This product demo provides a concise overview of how Lakera Guard is used to protect GenAI applications in real-time.

Lakera plays an essential role in locking down the foundational security layer of the AI stack. It’s a serious, enterprise-ready solution for protecting your models from an ever-growing list of threats, and the Check Point acquisition just highlights how important this piece of the puzzle is.

But a complete AI strategy needs more than just a good defense. Real success comes from building applications that are not only secure but also consistently accurate, reliable, and easy for business teams to oversee. The best AI is both well-protected and genuinely helpful.

Once you have your AI security baseline handled, the next logical step is to build useful, trustworthy applications on top of it. If you’re looking to deploy AI agents that learn from your company’s knowledge base and work directly within your helpdesk, you can build, test, and launch your first one in just a few minutes with eesel AI.