Secure AI Agents from Discovery 
to Runtime

AI agents are being built faster than security teams can govern them. Discover your agent landscape, assess risk, and enforce protection in real time.

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Dashboard showing interaction stats, detection rates, latency, flagged threats, and threat breakdown charts.Dashboard showing bar and area charts with data points, including 62 unflagged interactions on Nov 1, 2024.

AI systems behave differently and introduce new security gaps

Click each card to explore the security gaps created when AI agents operate across tools, APIs, and workflows.
Visibility Gap
You can’t secure what you can’t see. AI agents are deployed across tools and workflows without centralized visibility, making it unclear what exists or what they can access.
The Security Implication
Unknown agents can access sensitive data, trigger actions, or connect to tools without security review.
Traditional Controls
Traditional controls fail against AI-native attacks. Prompt injection, jailbreaks, and indirect attacks can manipulate model behavior and bypass existing defenses.
The Security Implication
Attackers can influence agent behavior, override intended controls, or trigger unsafe actions.
Agents act autonomously
Agents don’t just respond, they take action across APIs, tools, and connected systems with elevated privileges.
The Security Implication
A compromised or misdirected agent can expose data, trigger unauthorized actions, or disrupt workflows. (edited)

Security built for how AI actually works

AI Agent Security enforces security on every AI interaction your organization runs by inspecting what goes in, controlling what comes out, and governing what your agents do. It deploys in minutes, requires no changes to your models or prompts, and adds no meaningful latency to user experience.

Discover and Govern Your AI Agents

Before you can secure AI agents, you need to understand where they exist and what they can access.AI Agent Security provides visibility into agent usage and MCP-connected systems across your environment, including agents your teams did not explicitly build or register.

Discover AI agents and MCP-connected tools across your environment
Identify what data, APIs, and systems agents can access

Security Built for Enterprise AI Agents

AI Agent Security protects agents across the full lifecycle, from discovery and risk assessment to runtime enforcement. It helps security teams understand which agents exist, what they can access, and whether their actions should be allowed or blocked in context.

Built for the Full Agent Lifecycle

AI systems behave probabilistically, act autonomously, and communicate in natural language. Securing them requires controls designed specifically for those properties

Diagram showing user data processed and routed to cloud, database, email, web, and chat services.
Discover and Assess
Automatically discover agents across your enterprise environment and understand risk across tools, data access, MCP servers, and autonomy.
Govern and Enforce
Use policy and runtime enforcement to govern what agents can access, call, and do.
Protect in Real Time
Detect and block prompt attacks, data leakage, unsafe tool use, and unauthorized agent actions as agents operate.

How AI Agent Security works

Connect your agent ecosystem

Connect cloud, low-code, and custom environments to bring your agent landscape into view.

Understand your risk posture

Assess each agent based on its configuration, tools, data access, MCP servers, and autonomy.

Enforce protection in real time

Enforce access controls and runtime guardrails to block threats, prevent data leakage, and govern live agent actions.

What AI Agent Security protects against

AI Agent Security protects against risks across the agent lifecycle, from prompt attacks and data exposure to unsafe tool use and unauthorized actions.

Adversarial attacks

Prompt injection, jailbreaks, and adversarial instructions blocked before they reach the model.

Orange upward trending arrow icon symbolizing growth or increase.

Data and access risks

Sensitive data exposure in prompts and responses, unauthorized agent access, and gaps in AI interaction visibility.

Safety and policy violations

Harmful or non-compliant outputs, unsafe or unauthorized agent actions, and misuse beyond defined policies.

Where AI Agent Security Fits

AI Agent Security extends the AI Defense Plane to the agents organizations build and deploy, from discovery and governance to runtime protection.

MCP-connected systems

Blocks indirect injection through connected tools before agents act on compromised instructions

Applications

Identify safety and security failure modes before AI features and copilots reach production.

Agents

Contain unsafe actions, tool abuse, andconnected system risk at runtime.

”The Lakera team has accelerated our GenAI journey, allowing us to create secure GenAI experiences at scale.”

Adrian Wood

Security Engineer @ Dropbox

Learn more

Speak with a security expert about securing AI agents

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