Lakera is one of the few AI-native security platforms that combines workforce AI security, AI agent security, and AI red-teaming under one roof. The Gandalf prompt injection challenge — and the more recent Gandalf: Agent Breaker environment — give the company unusual depth in adversarial research that translates back into the product. For buyers who want runtime defense and red-teaming from the same vendor, Lakera is the strongest single answer in the category.
Coverage is broader than a pure DLP and the API-first architecture is a fit for organizations with engineering teams building custom LLM apps and AI agents.
This review is based on vendor demos, public documentation, customer feedback, and framework analysis. Lab testing is currently pending.
This review is currently based on:
Lab testing has not yet been completed because access is still pending.
Every reviewed product receives a score from 1 to 10 on each of seven dimensions. The dimensions and their weights are:
Coverage breadth
Detection accuracy
Deployment friction
Policy & control depth
Framework alignment
Support & documentation
20%
20%
15%
15%
10%
10%
10%
9
9
7
8
9
5
9
Mature prompt injection and jailbreak detection backed by Gandalf-driven adversarial research; sub-50ms runtime latency per vendor.
API-first; cloud-native enterprise integrations are documented. Pure SaaS rollouts are fast; custom LLM app integration requires engineering work.
Context-aware data protection and granular policy primitives across the runtime, agent, and red-team modules.
Strong OWASP LLM Top 10 coverage; reasonable NIST AI RMF mapping.
Quote-based for enterprise; some self-serve tiers exist for individual modules.
Score
9
What it measures
Score
9
What it measures
Mature prompt injection and jailbreak detection backed by Gandalf-driven adversarial research; sub-50ms runtime latency per vendor.
Score
7
What it measures
API-first; cloud-native enterprise integrations are documented. Pure SaaS rollouts are fast; custom LLM app integration requires engineering work.
Score
8
What it measures
Context-aware data protection and granular policy primitives across the runtime, agent, and red-team modules.
Score
9
What it measures
Strong OWASP LLM Top 10 coverage; reasonable NIST AI RMF mapping.
Score
5
What it measures
Quote-based for enterprise; some self-serve tiers exist for individual modules.
Score
9
Public documentation is among the deepest in the category; the Gandalf community is a credibility multiplier.
Most competitors do one or the other. Lakera does both, & the feedback loop between adversarial research and detection improvements shows up in the product.
Discovery is not a separate SKU. New AI tools surfacing in the org show up in the same console used to enforce runtime policy.
Per vendor; appropriate for in-line enforcement on customer-facing LLM apps.
The original Gandalf prompt injection challenge and the newer Gandalf: Agent Breaker show a rare public commitment to adversarial testing and continuous product learning.
Maps cleanly onto OWASP LLM01 (prompt injection), LLM07 (system prompt leakage), LLM02 (sensitive information disclosure), & elements of the new owasp Top 10 Applications 2026.
This site distinguishes between two testing tracks. Both are honest about their depth. The lab program operates under the following commitments:
Enterprise pricing is quote-based.
Less of a weakness, more a category constraint but buyers expecting a zero-deployment workforce DLP should not be running through Lakera’s full stack on day one.
Published ISO 42001 mapping, named-CSM tier thresholds, & customer-attested benchmark numbers are not publicly available at the depth a top-tier review would prefer.
Mid-to-large enterprises with engineering teams building or operating custom LLM applications and AI agents, plus a workforce that uses ChatGPT, Claude, Gemini, Perplexity, and embedded SaaS AI. Buyers who want runtime defense and red-teaming from one vendor.
Mixed. Self-serve tiers exist for some modules; enterprise is quote-based. Improving on this would lift the score.
HiddenLayer is the closest enterprise alternative on AI lifecycle and supply chain. Lasso Security is a closer match on guardrails-layer GenAI security. Witness AI is the network-layer alternative.
If Lakera grants lab access, we would run the following scenarios. This list serves both as transparency about how a Lab Tested review of Lakera would be scored, and as a public roadmap that pressures vendors toward participation:
The standard 150-prompt sensitive data set across the workforce module to evaluate detection accuracy in the inspected path.
The standard 150-prompt sensitive data set across the workforce module to evaluate detection accuracy in the inspected path.
Verify policy enforcement on a representative agent runtime with at least one MCP tool integration.
Block, warn, redact, allow behaviors against the configured workforce and runtime policies.
Verify what is logged, what is not, & retention behavior across workforce, agent, and red-team modules.
The standard 150-prompt sensitive data set across the workforce module to evaluate detection accuracy in the inspected path.
Measure runtime latency on standard prompt sizes against the vendor's sub-50ms claim
Lakera’s three-module architecture (Workforce, Agent, Red Teaming) lets buyers start narrow and expand. The most common adoption pattern we have seen in customer references is to start with the AI Red Teaming module — adversarial testing of an existing LLM application before launch — and add Workforce and Agent modules as the AI portfolio grows.
The advantage of this sequence is that the red-teaming engagement produces evidence usable in NIST AI RMF Measure activities and ISO 42001 lifecycle artifacts; that evidence is a wedge for the broader rollout.
For organizations that prefer a workforce-first deployment, Lakera’s shadow AI discovery is integrated into the same console as the runtime policy, which avoids the “two consoles for one problem” pattern common with stitched-together discovery and enforcement vendors.
API-first means engineering work for custom LLM applications. The integration is well-documented; references describe a few engineer-weeks for a typical deployment on a single application, with subsequent applications adopting the same pattern in days.
The MCP and agent integrations are more complex and depend on the
agent framework in use; LangChain, LangGraph, and direct MCP integrations are documented.
TCO depends on which modules are licensed and the seat count for the workforce module. Buyers comparing TCO with broader-platform competitors should normalize for the value of the red-teaming module many enterprises pay an external red-team firm for periodic engagements
and the in-platform red-teaming module replaces a portion of that spend. We saw one customer reference attribute roughly 15% of total Lakera value to the displaced external red-team budget.
