Lakera Guard
Runtime security for GenAI applications and AI agents against prompt injection and data leakage.
Info last updated on May 23, 2026
Vendor Information
Lakera Guard Overview
Lakera Guard is a developer-first AI security platform protecting large language model applications from prompt injection, jailbreaks, data leakage, and malicious content through real-time threat detection. The platform integrates via simple API calls with any LLM including OpenAI GPT, Google Gemini, Anthropic Claude, Meta Llama, and custom models, providing ultra-low latency protection optimized for production environments.
Lakera Guard defends against direct and indirect prompt injection attacks, prevents leakage of PII and confidential data, detects hallucinations, and provides content moderation for hate speech, sexual content, violence, and profanities with customizable thresholds. The platform's proprietary AI models continuously learn from Gandalf, Lakera's AI red team platform with over 80 million attack data points crowdsourced from 1 million+ users worldwide, including Microsoft for security training. This data flywheel grows by 100,000+ unique attacks daily, ensuring protection against emerging threats.
Founded in Zurich in 2021 by AI experts from Google and Meta, Lakera raised $30 million from Atomico, Citi Ventures, Dropbox Ventures, and redalpine. The company serves 35%+ of Fortune 100 companies including Dropbox, major financial institutions, and technology enterprises. Lakera holds SOC 2 Type II, GDPR, and NIST certifications with alignment to MITRE ATLAS and OWASP LLM Top 10 frameworks.
Key Capabilities
Standardized capabilities mapped to this product's security niche
Detects and blocks adversarial inputs designed to override system prompts, extract training data, or redirect model behavior. Detection approaches include pattern matching, input semantic analysis, and secondary model classification.
Intercepts prompts and completions to prevent sensitive data (PII, credentials, internal IP), from being transmitted to external LLM services or returned in model responses.
Evaluates model outputs against content policy, data classification rules, and format expectations before delivery to end users, blocking responses containing sensitive data or policy violations.
Enforces IAM-style policies on LLM API access, controlling which users and applications can invoke which models and data sources, with audit logging.
Records prompts, completions, and metadata for all AI interactions with tamper-resistant storage, supporting compliance, forensics, and policy investigation.
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