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About
Lakera AI Guard is the leading AI-native security platform designed to safeguard GenAI applications, autonomous agents, and LLM-powered pipelines at enterprise scale. Unlike traditional security tools not built for generative AI, Lakera provides real-time runtime protection that continuously evolves with emerging threats—no manual updates required. Core capabilities include AI Agent Security for live threat detection and prompt attack prevention, AI Red Teaming for risk-based vulnerability discovery with collaborative remediation guidance, and Gandalf, an interactive AI security training simulation used by over 1 million hackers to stress-test LLM defenses. The platform manages risks such as direct and indirect prompt injection, AI data leaks, toxic content generation, compliance violations, and multilingual and multimodal attacks. Lakera's context-aware approach reduces risks by 3–4 orders of magnitude compared to traditional methods, while its API-first, cloud-native architecture enables effortless scaling from zero to hundreds of prompts per second. Central policy controls let security teams customize protection horizontally across applications without requiring code changes. The platform is model-agnostic and supports multimodal inputs including chatbots and audio bots. Ideal for enterprise security teams, AI engineers, and compliance-focused organizations looking to accelerate GenAI adoption without compromising safety, Lakera integrates seamlessly into existing development workflows and supports use cases from conversational agents and RAG pipelines to GenAI gateways and connected multi-agent systems.
Key Features
- Real-Time Runtime Protection: Monitors and intercepts threats in live GenAI applications with sub-50ms latency, preventing prompt injections, jailbreaks, and data leakage before they impact users.
- AI Red Teaming: Risk-based vulnerability management with direct and indirect attack simulations, providing collaborative remediation guidance to harden GenAI systems proactively.
- Gandalf Security Training: Interactive AI security simulation game that has trained over 1 million participants to understand and recognize prompt injection and LLM exploitation techniques.
- Central Policy Control: Customize and enforce security policies across all GenAI applications horizontally from a single control plane without requiring code changes in individual apps.
- Model-Agnostic Multimodal Support: Secures any LLM-powered application regardless of underlying model, with support for text, audio, and expanding modalities across chatbots and agentic pipelines.
Use Cases
- Securing customer-facing conversational AI chatbots against prompt injection and jailbreak attempts in production environments.
- Protecting RAG (Retrieval-Augmented Generation) pipelines and document agents from indirect prompt injection embedded in retrieved content.
- Enabling enterprise compliance and regulatory risk management for GenAI deployments in financial services, healthcare, and legal industries.
- Conducting AI red teaming exercises to proactively discover and remediate vulnerabilities in LLM-powered applications before launch.
- Monitoring and governing multi-agent and MCP-connected AI systems to prevent data leakage and unauthorized actions across connected tools.
Pros
- Ultra-Low Latency: Sub-50ms response times ensure security checks do not degrade end-user experience, even for large prompts and long context windows.
- Continuously Evolving Threat Intelligence: Security models update dynamically based on real-world attack data from 1M+ adversarial interactions, keeping protection current without manual intervention.
- Enterprise-Grade Scalability: API-first, cloud-native architecture scales seamlessly from prototype to hundreds of prompts per second, fitting startups and Fortune 500s alike.
- Broad Risk Coverage: Addresses a wide attack surface including prompt injection, indirect injection, data leaks, toxic content, compliance risks, and multilingual/multimodal threats.
Cons
- Pricing Transparency: Full enterprise pricing details are not publicly listed, requiring a sales conversation to understand costs at scale.
- GenAI-Specific Focus: Designed exclusively for LLM and GenAI workloads, so it does not replace traditional application security tooling for non-AI systems.
- Learning Curve for Red Teaming: The AI Red Teaming module may require security expertise to configure and interpret vulnerability reports effectively.
Frequently Asked Questions
What types of attacks does Lakera AI Guard protect against?
Lakera protects against direct and indirect prompt injection attacks, AI data leakage, jailbreaks, toxic content generation, compliance violations, and multilingual and multimodal adversarial attacks.
Is Lakera compatible with any LLM or AI model?
Yes, Lakera is model-agnostic and works with any LLM or GenAI model, including those from OpenAI, Anthropic, Meta, Mistral, and others, as well as custom fine-tuned models.
How does Lakera integrate into existing development workflows?
Lakera uses an API-first architecture that integrates with minimal code changes. It supports cloud-native deployment, enterprise integrations, and central policy controls that don't require per-app code modifications.
What is Gandalf and how does it relate to Lakera's security?
Gandalf is an interactive AI security training game by Lakera where users attempt to extract secrets from an LLM. It has been used by over 1 million people and serves as both a public awareness tool and a source of real-world adversarial data that strengthens Lakera's threat models.
Does Lakera offer a free tier or trial?
Yes, Lakera offers a free starting option. Enterprise plans with full runtime protection, red teaming, and policy management are available via a sales conversation for teams requiring production-scale coverage.
