How to Secure Your GenAI App When You Don’t Know Where to Start
The fastest way to secure your GenAI app — even if you don’t know where to start.

The fastest way to secure your GenAI app — even if you don’t know where to start.
One of the toughest challenges for security teams adopting GenAI isn’t building defenses. It’s working out what defenses are most needed, and what’s the appropriate risk threshold for each use case.
Policy management in GenAI is messy by nature. You’re trying to control novel behaviors, across dynamic inputs and outputs, in a landscape where best practices barely exist.
So what happens?
Teams often default to blunt, blanket controls, or take ages over engineering—evaluating every threshold, toggling every setting—and lose sight of the real goal: unblocking innovation with effective safeguards quickly.
That’s why we redesigned policy management in Lakera Guard around battle-tested, expert-recommended security policies. It’s a simpler way to help teams deploy meaningful protections right away, and fine-tune only when they’re ready.
Not sure how to secure your GenAI application? Skip the guesswork with expert-recommended policies built by Lakera’s AI security team. Apply them in seconds, fine-tune when you’re ready, and get started with real protection from day one.
The Lakera team has accelerated Dropbox’s GenAI journey.
“Dropbox uses Lakera Guard as a security solution to help safeguard our LLM-powered applications, secure and protect user data, and uphold the reliability and trustworthiness of our intelligent features.”
With our new approach to policy management, security teams can get protected in seconds using expert-curated, battle-tested, pre-built policies, and then progressively introduce more control as their use cases and maturity evolve.
Here’s how it works:
We’ve introduced five one-click policies covering the most common GenAI deployment scenarios:
Each policy includes guidance on when to use it — and all are backed by Lakera’s security expertise.
Every policy can be tuned with one global flagging sensitivity. We provide clear guidance on how to choose the right level for your use case: from L1 (minimal disruption) to L4 (maximum protection).
As your security needs grow, you can edit templates, adjust guardrails, or fully customize behavior using our advanced settings — without having to start from scratch.
Most teams follow a natural progression when rolling out Lakera Guard:
This update is about supporting that journey: offering simplicity at the start, flexibility over time, and clarity throughout.
Security teams no longer need to start from a blank slate or interpret a dozen detection options. Instead, they can:
And for those running self-hosted, don’t worry you get these expert curated policy templates too, so you can adopt the same approach in your own infra.
This update isn’t just a UX refresh. It’s the result of everything we’ve learned from helping security teams deploy GenAI in production.
We’ve learned a lot from working closely with trailblazing GenAI-first companies, the teams pushing AI into production before most had figured out what secure even looked like. Those experiences taught us where security teams get stuck, what guardrail configurations actually work, and how to guide policy decisions that scale.
We’ve turned those lessons into clear, expert-backed starting points, so new customers can move faster, deploy confidently, and spend less time fine-tuning.
This is how we help teams operationalize AI security: with policy defaults that make sense, and flexibility that grows with you.
As AI security matures, policy management will become the heart of how companies translate their risk appetite into defensible controls. But we can’t expect every team to start with perfect answers.
That’s why the future of policy management is opinionated, contextual, and adaptable. And that’s exactly what we’ve just shipped.
If you want to see it in action, we’ll be sharing short walkthroughs soon — or get in touch for a hands-on demo.
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