Lakera
Runtime security layer that inspects what goes into your AI agents and controls what comes out
If you have LLM features or agents in production and a security budget, Lakera is one of the few vendors that only does GenAI risk — prompt injection, data leakage, unsafe agent actions, MCP-connected systems — rather than bolting an AI tab onto a general platform. Named deployments at Dropbox and in regulated banking, plus its citation in OWASP's LLM and GenAI Security Landscape Guide 2025, back that up. Compare it against Snyk or Cloudflare if you want one vendor for everything, and against open-source guardrails if you can staff the maintenance. It is runtime protection, not a full AI governance suite.
Verified 1h ago · liveness 82/100 · cite: rightaichoice.com/tools/lakera
- Enterprise security teams with GenAI apps in production
- Regulated industries (banking, healthcare, financial services)
- Organizations governing employee shadow AI usage
- Teams securing AI agents and MCP-connected systems
- Organizations with no GenAI in production
- Teams that need the AI security layer to run outside a vendor cloud
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Skip Lakera if your organization has no generative AI in production yet, or if you need the prompt-inspection decision made entirely inside infrastructure you operate rather than in a vendor cloud.
Shadow AI discovery needs browser extensions deployed across your fleet — budget IT rollout and ongoing extension management, not just a license.
Lakera prices as enterprise infrastructure sitting on the request path of production AI, which places it alongside other enterprise security platforms rather than against low-cost open-source guardrails. The credible comparison is a general-purpose security vendor that also advertises AI protection — Lakera's case is depth on GenAI-specific threats in exchange for being a single-purpose layer you add rather than fold into an existing contract.
In short
Lakera — Runtime security layer that inspects what goes into your AI agents and controls what comes out. Best for Enterprise security teams with GenAI apps in production, Regulated industries (banking, healthcare, financial services), Organizations governing employee shadow AI usage. Contact Sales pricing.
No head-to-head comparison for Lakera yet.
What people actually say about Lakera — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
26 mentions across 2 sources (Hacker News, Product Hunt), 19 more we could not attribute · researched Oct 10, 2026.
Weighted by the 45 posts each of 2 sources contributed.
- +Clean API and clear documentation — reviewers set up and integrated with OpenAI quickly
- +Multi-category moderation (hate speech, off-context output) works with configurable thresholds
- +Sub-50ms claimed latency is attractive for real-time runtime enforcement
- +Multimodal coverage (text, audio, image) across 100+ languages is unusually broad
- +Agent and MCP-tool discovery addresses a genuinely new enterprise attack surface
- −Community doubts classifier defenses can stop semantic attacks like 'act as a SQL expert'
- −Performance claims (latency, FP rate) are vendor-only — no independent verification in data
- −No public pricing; 'contact us' blocks self-serve evaluation for smaller teams
- −Organic Hacker News chatter is dominated by Gandalf, not real paid-product results
- −Miss rate at runtime is unknown — no community post reports real attack telemetry
- • No public pricing — sales cycle and NDA required before any cost estimate
- • Enterprise onboarding and policy-tuning engineering time
- • Potential per-request or per-seat scaling costs not disclosed publicly
Viability Score
How well maintained and how widely used is Lakera? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Real-time prompt injection detection
- Indirect prompt injection blocking through connected tools and MCP servers
- Data leakage protection in AI prompts and responses
- Shadow AI discovery across apps and browsers
- AI agent and MCP-connected tool discovery
- Risk posture assessment per agent (tools, data access, autonomy)
- Context-aware data protection inside prompts
- Granular policy controls by user, app, and action
- Centralized policy management without code changes
- Risk-based AI red teaming with direct and indirect attack simulations
- Collaborative remediation guidance from red team findings
- Multimodal detection across text, audio, and image inputs
- 100+ language support for multilingual attacks
- Sub-50ms runtime latency
- Cloud-native, API-first architecture
About Lakera
Lakera is an AI-native security platform for enterprises running generative AI in production. It covers three jobs: Workforce AI Security, which discovers shadow AI usage across apps and browsers and applies context-aware protection inside prompts; AI Agent Security, which discovers AI agents and MCP-connected tools across your environment, assesses each one's data access and autonomy, and enforces runtime policy on what agents can call and do; and AI Red Teaming, which runs risk-based direct and indirect attack simulations with remediation guidance. Detection covers text, audio, and image inputs across 100+ languages, and Lakera reports sub-50ms runtime latency with a 0.01% production false positive rate — it says 1M+ hackers' worth of attack data from its Gandalf community feeds the detection. It is cloud-native and API-first, and it works alongside general-purpose security vendors rather than replacing them: Snyk is a partner on agent security research, and Lakera is cited in OWASP's LLM and GenAI Security Landscape Guide 2025. Dropbox and a regulated banking deployment are named as customers.
Behind the Verdict
Lakera's pitch is narrow and specific: control what AI does, not just what it can access. That framing matters because the failure modes it targets — prompt injection, indirect injection through connected tools, data leaking out through prompts and responses, an agent taking an unauthorized action — are not addressed by the access-control and DLP tools most enterprises already own. Strengths. The published performance numbers are unusually concrete for this category: sub-50ms runtime latency, a 0.01% production false positive rate, 100+ languages, and 1M+ secured transactions per app per day. False positives are the real killer in runtime security, so a vendor putting a specific number on it is worth noting. Coverage spans text, audio, and image inputs, so audio bots and multimodal apps are in scope rather than being an afterthought. Policy is centralized and changed without touching application code, which keeps the security team in control instead of queueing engineering work. The discovery side is genuinely differentiated: AI Agent Security finds agents and MCP-connected tools that teams did not register, then assesses each on configuration, tools, data access, and autonomy before enforcing at runtime. Weaknesses and caveats. Deployment is cloud-based, and browser extensions do the work of discovering shadow AI usage — that is real endpoint rollout work, not a switch you flip. The third-party credentials and hosting footprint all sit with one vendor, so you are adding a dependency on the critical path of every AI request. It is also a point solution: it secures the AI layer but does not replace your existing network, endpoint, or cloud security stack, so expect to run it alongside them. Single-vendor reliance also means if Lakera's own service degrades, AI features behind it feel it. Where it fits. Enterprises and regulated organizations — banking, healthcare, financial services — with customer-facing agents, RAG/document applications, GenAI gateways, or a workforce already using ChatGPT, Copilot, and Gemini. Pairs naturally with an existing security vendor: Snyk is a partner on AI agent security research, and Lakera's OWASP and Gartner TRiSM credentials make it easier to defend the line item internally. Where it doesn't. Teams with no GenAI in production should not buy a runtime guardrail. Small teams looking for a lightweight drop-in should weigh open-source guardrails first, and anyone who needs to route AI traffic through infrastructure they fully control should factor in the cloud dependency before committing.
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Real-world workflow fit
Concrete scenarios for the personas Lakera actually fits — and what changes day-one when you adopt it.
Deploy the AI Agent Security layer in front of the assistant's model calls, assess each agent's data access and autonomy, then enforce runtime policy that blocks prompt injection, jailbreaks, and sensitive data in prompts before they reach the model.
Outcome: Attacks are blocked before the model sees them, and the security team has one policy console instead of code changes in the application.
Roll out browser extensions to discover which AI apps employees use, then apply context-aware protection inside prompts so customer data pasted into ChatGPT, Copilot, or Gemini is caught at the point of use.
Outcome: Shadow AI usage becomes a visible, policy-governed inventory instead of an unknown risk.
Run Lakera's risk-based red teaming with direct and indirect attack simulations against the agent's tools and MCP connections, then work through the remediation guidance before going live.
Outcome: Known failure modes are found and fixed pre-launch, and the same policies carry into runtime protection after launch.
Use Cases
- Blocking prompt injection and jailbreaks against customer-facing conversational agents before they reach the model
- Stopping sensitive data exposure in RAG and document applications
- Discovering and governing employee use of ChatGPT, Copilot, and Gemini
- Running risk-based red team simulations on AI features before they launch
- Discovering unregistered AI agents and MCP-connected tools across the enterprise
- Containing unsafe or unauthorized agent actions at runtime
- Enforcing AI compliance policy in regulated banking and healthcare deployments
- Preventing toxic or non-compliant output from customer support bots
Limitations
- Lakera covers the AI layer specifically — it is runtime protection for AI applications, agents, and workforce AI usage, not a replacement for your network, endpoint, or cloud security stack.
- Shadow AI discovery runs through browser extensions, so endpoint rollout and management are part of the project.
- Deployment is cloud-based and the platform is API-first, so every AI request depends on Lakera being reachable and healthy; teams that require the decision to be made on infrastructure they control should weigh that dependency carefully.
- Named performance figures — sub-50ms runtime latency and a 0.01% production false positive rate — are vendor-published.
as of 2026-09-29
Verification history
We have re-verified Lakera 21 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
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- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 21 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Lakera's pricing actually pencils out — and where peers do it cheaper.
Lakera prices as enterprise infrastructure sitting on the request path of production AI, which places it alongside other enterprise security platforms rather than against low-cost open-source guardrails. The credible comparison is a general-purpose security vendor that also advertises AI protection — Lakera's case is depth on GenAI-specific threats in exchange for being a single-purpose layer you add rather than fold into an existing contract.
Setup time & first value
How long it actually takes to get something useful out of Lakera — broken out by persona, not the marketing-page minute.
Enterprise security teams: expect a discovery sprint first (agent landscape, MCP connections, existing AI usage) before enforcement goes live. Workforce AI Security adds endpoint browser-extension rollout time on top. Lakera states no changes to models or prompts are required for the runtime layer and describes deployment in minutes for the agent security layer once the environment is connected.
Switching to or from Lakera
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From open-source guardrails: replace custom prompt-injection filters with the runtime layer while keeping your existing app architecture, since Lakera needs no model or prompt changes.
- →From no runtime protection: start with agent and MCP discovery to inventory what exists, then turn on enforcement per agent as risk is assessed.
- →From a general-purpose security vendor's AI add-on: run Lakera alongside it for GenAI-specific threats rather than ripping out the existing stack.
- ↗To a general-purpose security platform: if your AI risk is low and you want one vendor, general platforms cover adjacent ground, though Lakera's depth is specific to GenAI threats.
- ↗To open-source guardrails: possible for teams with the engineering capacity to build and maintain their own detection, at the cost of continuous threat-intel updates.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Lakera”, and we withheld 5: 5 could not be judged, because “Lakera” is a single word that other videos use for other things. Showing the 1 we can prove is about Lakera.
Official links
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