Comparison · AI Governance DLP · 500–2,500 employee mid-market

Veladon vs Lakera Guard

Runtime LLM firewall for customer-facing AI applications — best-in-class prompt-injection and jailbreak detection, now backed by Check Point post-acquisition.

Lakera Guard price band
Varies post-Check-Point acquisition · estimated $40–200k ACV depending on throughput tier
Veladon price band
$22–32k ACV at 1,000 emp · evidence packs bundled · no services add-on
Lakera Guard best fit
Product and engineering teams shipping customer-facing LLM applications (chatbots, agents, copilots) who need runtime input/output guardrails at the API layer — a different threat model and buyer from employee-side shadow AI.
Weak against Veladon
Lakera Guard protects your AI applications from bad users; it does not protect your employees from leaking data into public LLMs. The product, buyer (platform engineer, not CISO), and audit trail (runtime app metrics, not compliance evidence) are all different. For an employee-side AI DLP use case, Lakera Guard is the wrong tool.
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Head-to-head · 10 dimensions

Veladon vs Lakera Guard: dimension-by-dimension.

The dimensions auditors, CISOs, and Compliance Officers ask about when they evaluate an AI-governance DLP against an incumbent. Read horizontally to compare behavior on the same axis.

DimensionLakera GuardVeladon
Protection directionInbound to your AI app — filters malicious user input (prompt injection, jailbreak) and flags risky outputs your LLM generates for customersOutbound from your employees — redacts PII / PHI / secrets in prompts your staff sends to ChatGPT / Claude / Gemini before the request leaves the browser
Deployment modeSDK or reverse-proxy inline with your LLM calls in production — integrates with the app at the API layerBrowser extension via MDM + SaaS connectors via OAuth — integrates with the employee at the surface layer. No code changes
Primary buyerVP Engineering or Platform Lead (sometimes Head of AI) — the owner of the LLM-powered productCISO + Compliance Officer — the owners of the employee-facing shadow-AI risk
Audit trailRuntime app telemetry — request rate, injection attempt counters, jailbreak attempt counters, fairness metricsCompliance evidence — per-prompt event log with EU AI Act Article 26 / ISO 42001 Annex A / NIST AI RMF mapping, ready for quarterly regulator submission
EU AI Act Article 26(1) evidenceDoes not produce Article 26(1) deployer evidence — Lakera's audit trail is Article 14-adjacent (human oversight on high-risk AI system outputs) for your own AI app, not deployer evidence for employee AI useArticle 26(1) usage log + Article 26(2) human oversight + Article 26(4) data-governance alignment + Article 50 transparency + Annex IV technical documentation — the deployer evidence stack
ISO 42001 Annex A mappingA.9 (AI system performance monitoring) and A.8.3 (human oversight) for your own AI-app outputs — evidenced via runtime metricsA.4 (lifecycle) + A.6.2.3 (usage monitoring) + A.8.3 (human oversight) + A.9 (performance) + A.10 (third-party AI governance) — evidenced for employee use of third-party public LLMs
NIST AI RMF MP-4 / MEASURE coverageMEASURE 2 runtime metrics (robustness, adversarial) for your own AI appMEASURE 2.8 test / eval / V&V evidence for employee shadow-AI use; GenAI Profile per-prompt metadata
Time-to-first-policySDK integration: 2–4 weeks engineering work per LLM app; gateway mode: 1–2 weeksBrowser extension via MDM: 5–10 business days; no engineering work
Threat model primaryAdversarial users attacking your AI app — prompt injection, jailbreak, model theft, denial-of-wallet, data exfiltration via unintended outputsEmployees leaking data to public LLMs — PII / PHI / payment / source code / customer identifiers / secrets in outbound prompts
Coexistence with the other toolComplementary — Lakera Guard owns the runtime app surface, Veladon owns the employee surface. Teams adopting both unify the audit trail via webhook bridgeComplementary — Veladon owns the employee-side DLP, Lakera Guard owns the product-side AI firewall. Different OSI layer, different buyer, different control surface

Honest category positioning

When Lakera Guard is the right choice over Veladon.

If your primary threat model is adversarial users attacking your customer-facing AI app (chatbot, agent, copilot) — prompt injection, jailbreak, model theft, denial-of-wallet via token-burn, data exfiltration via unintended outputs — Lakera Guard is the right tool. It sits inline with your LLM calls and filters both directions at the API layer. Veladon does not cover this threat model; our surface is the employee browser, not the production app runtime.

If your buying committee is VP Engineering + Platform Lead + Head of AI rather than CISO + Compliance Officer, Lakera Guard's sales motion and integration patterns are engineered for that committee. The audit trail they produce (runtime telemetry, robustness metrics, red-team evidence) is what that buyer wants. Veladon's audit trail (compliance evidence packs pre-mapped to EU AI Act / ISO 42001 / NIST AI RMF) speaks to a different buyer.

If your organization already has employee-side DLP covered by another tool (or genuinely has negligible employee shadow-AI exposure) and the gap is on your own LLM-powered product, Lakera Guard is the focused point solution. Post-Check-Point acquisition the roadmap and distribution reach are deep.

Where Veladon decisively fits

When Veladon is the right choice over Lakera Guard.

If your real AI risk is employees pasting customer data into ChatGPT and Claude — not adversarial users attacking your own AI app — Veladon is the correct tool and Lakera Guard is the wrong one. The threat models do not overlap. At 500–2,500 employees in financial services, healthcare, SaaS, legal, or insurance, employee-side leakage is almost always the larger threat by volume and by audit-exposure surface.

If your audit committee is asking for EU AI Act Article 26 deployer evidence, ISO 42001 Annex A coverage, or NIST AI RMF GOVERN / MAP / MEASURE / MANAGE evidence, Lakera Guard's runtime telemetry does not answer that ask. Veladon's quarterly evidence pack does, by default, with clause-level indexing. The two artifact types are complementary — Lakera Guard evidences your AI app's safety, Veladon evidences your employees' AI use.

If you have multiple AI-risk surfaces (employees + your own AI app) and want to evaluate both, the stack is Veladon + Lakera Guard. Neither replaces the other. A common mid-market pattern in 2026 is Veladon for the shadow-AI surface and Lakera Guard for the in-production chatbot — unified via webhook into one compliance index.

Migration from Lakera Guard → Veladon

How to migrate without losing audit-trail continuity.

There is typically no migration from Lakera Guard to Veladon because the tools solve different problems. If you adopted Lakera Guard for employee-side AI DLP by mistake (it happens — the category is new and vendor positioning overlaps), the migration is straightforward: Lakera Guard's runtime gateway gets decommissioned from the employee-side use cases (keep it for your AI app if that exists), Veladon is deployed via MDM for browser-side coverage, and the last 90 days of Lakera employee-side logs (where captured) import into the Veladon evidence index. Typical timeline: 10 business days.

Questions CISOs ask during a Lakera Guard evaluation

Common questions about Veladon vs Lakera Guard.

Does Lakera Guard protect employees who paste customer data into ChatGPT?

No. Lakera Guard is a runtime guardrail for customer-facing LLM applications — it filters traffic to and from your own AI app's LLM API. It does not sit between your employees and public LLMs (ChatGPT, Claude, Gemini), and it does not redact PII / PHI / payment data from employee-side prompts. If your primary risk is employees leaking data to public LLMs, Lakera Guard is the wrong tool. Veladon is purpose-built for that employee-side surface.

Can I use Lakera Guard for EU AI Act Article 26 deployer evidence?

Partially. Lakera Guard's runtime telemetry for your own AI app contributes to Article 14 (human oversight) and Article 9 (risk management) evidence if you are a provider or deployer of a high-risk AI system you operate. It does not produce Article 26 deployer evidence for your employees' use of third-party public LLMs — that is a different surface. For the full deployer evidence stack (Article 26(1) / (2) / (4) / (5), Article 50, Annex IV), Veladon is the fit.

What's the difference between an LLM firewall and an AI DLP?

An LLM firewall (Lakera Guard, NeMo Guardrails, Guardrails AI) sits inline with LLM API calls in production and filters inputs and outputs at the application layer. It protects the AI app from adversarial users and protects users from unsafe outputs. An AI DLP (Veladon, Harmonic, Prompt Security browser extension) sits at the employee surface and redacts sensitive data in outbound prompts to public LLMs. Different OSI layer, different threat model, different buyer. The two coexist in a mature stack.

Did Check Point's acquisition of Lakera change the positioning?

Post-acquisition (2025), Lakera Guard is being integrated into Check Point's enterprise security distribution. The LLM-firewall positioning has not shifted — it remains focused on runtime guardrails for customer-facing AI applications. What has shifted is the packaging (bundled into Check Point enterprise agreements) and the sales motion (Check Point channel + direct). For a mid-market that already has a Check Point relationship, the bundle math can be favorable; for others, the standalone Lakera pricing still applies.

Can Veladon + Lakera Guard + Prompt Security all coexist?

Yes, and this is a reasonable enterprise stack for an organization with both employee shadow-AI exposure and customer-facing AI apps in production. Veladon owns the employee-browser surface, Lakera Guard owns the runtime AI-app guardrail, and Prompt Security's API gateway module covers developer-side LLM API traffic (or is replaced by Lakera Guard for that purpose). All three feed a unified compliance-evidence index, and the quarterly pack crosswalks events across all three into one EU AI Act / ISO 42001 / NIST AI RMF artifact.

Which tool is right for a 1,500-employee regulated SaaS with both employee shadow AI and a customer-facing AI copilot?

Both, but not substitutes. Veladon covers the employee-side shadow-AI risk (your staff pasting customer data into ChatGPT / Claude / Gemini) and produces EU AI Act Article 26 deployer evidence. Lakera Guard covers the runtime AI-copilot risk (prompt injection, jailbreak, unsafe outputs) and produces runtime safety telemetry. Neither tool substitutes for the other; at 1,500 employees with both surfaces live, you typically run both and unify the evidence index via webhook. Combined 3-year TCO at 1,500 emp typically lands at $180–280k.

Related reading on Veladon

Framework pages

Long-form analysis

Other Veladon comparisons

Evaluate the rest of the category.

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