Lakera

Lakera is an enterprise-focused Artificial Intelligence (AI) security provider that develops tools to safeguard Large Language Model (LLM) applications against prompt injection, data leakage, and related risks.

• AI security platform for monitoring, controlling, and protecting LLM-powered applications (AI security).
• Protection against prompt injection, jailbreaks, and other prompt-based attacks on LLM systems (application security).
• Detection and mitigation of sensitive data exposure and policy violations within AI interactions (data security and governance).
• Developer tooling and APIs to integrate AI security controls into existing software delivery workflows (DevSecOps).
• Security analytics and policy management for organizations deploying AI assistants, copilots, and other Generative AI (GenAI) solutions (security operations).

More About Lakera

Lakera focuses on AI security for enterprises that deploy LLM systems in production environments, including internal copilots, customer-facing assistants, and other GenAI applications. Its offerings target organizations that embed foundation models into workflows for support, content generation, software development assistance, or knowledge retrieval, and that require structured guardrails around model behavior, data access, and security posture.

The Lakera platform (AI security) centers on detecting and mitigating prompt injection, jailbreak attempts, and other adversarial inputs directed at LLMs. It inspects prompts and responses to classify whether queries or outputs violate defined security or compliance policies. This includes identifying attempts to override system instructions, exfiltrate confidential data, or induce unsafe behaviors in downstream tools connected to the LLM.

Lakera provides APIs and SDKs (DevSecOps) that allow engineering teams to embed AI security checks directly into application backends, middleware, or orchestration layers that sit between end users and model providers. This architecture aligns with common enterprise LLM patterns in which a security and governance layer proxies traffic to hosted or self-managed models. Lakera’s controls can be integrated with existing access management, logging, and monitoring systems so that prompts, responses, and risk decisions are observable through an organization’s standard Security Operations (SecOps) workflows.

The platform supports policy definition and enforcement (security operations), enabling security and compliance teams to specify rules governing what models may answer, what types of information may be exposed, and how the system should respond to suspicious or disallowed content. These policies can cover areas such as confidential data handling, safe use guidelines, or organization-specific constraints. Enforcement actions may include blocking, modifying, or flagging interactions for review, which provides an operational mechanism similar to existing web application firewalls but specialized for LLM traffic.

Compared with general-purpose application security tools, Lakera’s offerings are tailored to the behavior of generative models, including the context-window based architectures used by LLMs and Retrieval Augmented Generation (RAG) pipelines. The platform is positioned for organizations that integrate external model APIs or in-house models and want a consistent control plane, regardless of underlying model provider. In an enterprise technology directory, Lakera fits into categories such as AI security, application security for LLMs, data security and governance for AI workloads, and DevSecOps tooling for GenAI deployments.