Lakera Guard

Lakera is a security platform for generative AI. Its product Lakera Guard protects AI applications and agents at runtime, and the wider platform covers how employees use AI tools and how companies test their AI systems. It is aimed at enterprise teams adopting GenAI, agents and MCP servers.

AI agent security

Runtime protection for AI applications includes real-time threat detection, prompt attack prevention and data leakage protection. The site says the system observes more than 100 languages in real time and delivers runtime latency under 50 milliseconds, so it can sit in front of a live application. Its threat knowledge is described as learning from more than one million hackers around the world, a reference to Lakera's public prompt-hacking challenges.

Workforce AI security

For employee use of AI, Lakera offers shadow AI discovery across apps and browsers, context-aware data protection in prompts and granular policy controls by user, app and action. This helps security teams see which AI tools staff use and stop sensitive data being pasted into them.

AI red teaming

Lakera also offers risk-based red teaming for GenAI, with vulnerability management, collaborative remediation guidance and simulations of direct and indirect attacks, so teams can find weaknesses before launch.

Getting started

You can get started from the site or talk to sales for enterprise deployment. The site highlights easy integration and says Lakera is used by companies from startups to the Fortune 500.

Who it suits

Lakera suits security teams and developers building customer-facing chatbots, retrieval systems or agents that can take actions, where a prompt injection could leak data or trigger harmful actions.

Things to consider

No filter catches every attack, so treat a guard layer as one part of defence in depth, alongside limiting what tools and data your agents can reach. Test the guard against your own use cases to check false positives, which can block legitimate users. Review where prompts are processed and stored, especially for regulated data. Red teaming is most useful when repeated after major model or prompt changes.

For teams just starting with AI security, the workforce discovery tools give a quick picture of current AI use across the company.