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

Veladon vs Prompt Security

GenAI security gateway with dual browser-extension + API-layer coverage — strong Israeli-cybersec pedigree and a security-engineer-centric admin experience.

Prompt Security price band
Enterprise quote · estimated $35–90k ACV at 1,000–1,500 emp
Veladon price band
$22–32k ACV at 1,000 emp · evidence packs bundled · no services add-on
Prompt Security best fit
Security teams that want one vendor covering both employee-side shadow AI and developer-side LLM API calls, and that have a security engineer in-house who enjoys a powerful-but-verbose policy editor.
Weak against Veladon
Prompt Security spreads across three surfaces (browser, API, SaaS) and each gets less depth than a purpose-built tool. Evidence packs are DIY — you export logs and your GRC team builds the EU AI Act / ISO 42001 / NIST AI RMF artifact manually. The policy editor is powerful but requires a security engineer to operate; a Compliance Officer at a 500–2,500 employee mid-market often cannot drive it without SecEng hand-holding.
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Head-to-head · 10 dimensions

Veladon vs Prompt Security: 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.

DimensionPrompt SecurityVeladon
Primary surface coverageBrowser extension + API gateway + SaaS connectors — three surfaces in one platformBrowser extension + SaaS connectors, tuned specifically for employee shadow-AI (not developer-side LLM app traffic)
Latency budget (<50ms redaction)Browser extension meets <50ms; API gateway adds 20–60ms depending on policy complexity and tenant regionHard-coded <50ms P95 browser redaction; no server round-trip; policy evaluation entirely client-side
Deployment modeBrowser extension via MDM + API gateway via reverse proxy or SDK injection + SaaS connectors via OAuthBrowser extension via MDM (Intune/Jamf/Kandji/Chrome Enterprise managed policy) + SaaS connectors via OAuth. No proxy, no SDK, no network changes
EU AI Act Article 26 audit evidenceRaw logs exportable; customer or services team assembles the Article 26 pack manually — typically 40–120 hours per quarterPre-assembled quarterly pack with Article 26(1)/(2)/(4)/(5), Article 50, Annex IV references inline; one-click JSON + signed PDF export
ISO 42001 A.6.2.3 coverageCoverage present in raw logs; Annex A indexing via customer configurationA.6.2.3 control ID inline on every usage-log line; Annex A indexing by default
NIST AI RMF MP-4 mappingMAP 3 (context) and MAP 4 (risks/benefits) evidenced via raw logs; GOVERN / MANAGE index is customer-builtGOVERN / MAP 3 / MAP 4 / MEASURE 2.8 / MANAGE 1 / MANAGE 4 crosswalk pre-built, with GenAI Profile (NIST AI 600-1) metadata per prompt
Time-to-first-policy30–60 days depending on surface scope (browser only is quicker; browser + API + SaaS is longer)5–10 business days to first production policy live
Policy editor audienceSecurity engineer — YAML-style rule syntax and regex-heavy, expressive but steepCompliance Officer / Head of GRC — plain-English policy builder with regex power-user escape hatch
Price at 1,000 employees (one year)$45–70k ACV typical for the full three-surface bundle; $25–40k for browser-only$22–32k ACV including evidence pack; no per-surface SKU
Shadow-AI discovery — CompBrowser + DesktopApp + OAuthBrowser + OAuth strong; desktop-app (ChatGPT native macOS / Win) detection via API-gateway signals when traffic routes through itBrowser + OAuth + native desktop-app detection via loopback listener + URL probes; AI-system inventory treats desktop apps as first-class entries

Honest category positioning

When Prompt Security is the right choice over Veladon.

If you need one vendor covering both the employee-side shadow-AI surface (someone pasting into ChatGPT) and the developer-side LLM API surface (your own product calling OpenAI / Anthropic / Google APIs in production), Prompt Security's dual-surface coverage is a genuine advantage. Veladon does not cover the developer-side LLM app runtime surface — for that we consider Lakera Guard the better fit.

If you have a dedicated security engineer who enjoys a powerful, verbose policy editor and wants the expressiveness to write custom detection rules in a YAML-like syntax, Prompt Security's policy surface rewards that investment. A security team that has already stood up a full SOAR pipeline will find Prompt Security's integrations natural.

If your AI risk is dominated by API-layer threats (prompt injection, jailbreak, model theft) from customer-facing LLM apps rather than employee-side data leakage to public LLMs, Prompt Security's gateway module is purpose-built for that threat model. Veladon is narrower — employee-side DLP only.

Where Veladon decisively fits

When Veladon is the right choice over Prompt Security.

If your real AI risk is employee-side — a finance analyst pasting a customer SSN into Claude, a sales rep pasting a CRM export into ChatGPT, a paralegal pasting a privileged document into Gemini — Veladon covers that specific risk with a tighter product surface and a tighter price. You are not paying for API-gateway and SaaS-connector modules you do not need.

If your Compliance Officer or Head of GRC is the primary operator of the policy editor (not a security engineer), Veladon is the better fit. Our policy editor is plain-English with regex as an escape hatch; Prompt Security's is regex-first with natural-language as the escape hatch. Same capability, different default mode — and the default matters at 500–2,500 employees where a dedicated SecEng rarely owns the tool.

If your next external audit names EU AI Act Article 26, ISO 42001 Annex A, or NIST AI RMF in the scope and you need pre-assembled evidence packs rather than raw logs + 40–120 hours of GRC work per quarter, Veladon bundles the pack into the base plan. Prompt Security's audit story requires customer-side assembly, which is feasible but labor-intensive.

Migration from Prompt Security → Veladon

How to migrate without losing audit-trail continuity.

Migrating from Prompt Security browser extension to Veladon is a 10-day exercise. Export your Prompt Security rule catalog (YAML), run the Veladon rule-translator (we supply one for the 50 most common Prompt Security patterns), push Veladon via your existing MDM rollout ring, shadow-mode for 3 business days to baseline redaction-rate parity, cut over, and export the last 90 days of Prompt Security logs into the Veladon evidence index. If you are keeping the Prompt Security API gateway for developer-side coverage, the two tools coexist cleanly — Veladon covers employee-side, Prompt Security covers API-side, and you consolidate evidence into one crosswalk.

Questions CISOs ask during a Prompt Security evaluation

Common questions about Veladon vs Prompt Security.

Does Veladon cover developer-side LLM API traffic the way Prompt Security does?

No. Veladon is purpose-built for employee-side shadow-AI coverage — browser redaction and SaaS-connector OAuth telemetry. Developer-side LLM API gateway functionality (inspecting your own product's outbound traffic to OpenAI / Anthropic / Google APIs for prompt injection, jailbreak, model theft) is not in scope. For that, we consider Lakera Guard the best-in-class point solution. Prompt Security's dual coverage is a legitimate advantage if you need both in one vendor.

Is Prompt Security's policy editor usable by a Compliance Officer without security-engineering support?

In practice, no. Prompt Security's policy editor is powerful but security-engineer-centric — YAML-like rule syntax, regex-heavy expressions, and a SIEM-style event viewer. A Compliance Officer at a 500–2,500 employee mid-market typically cannot operate it day-to-day without a security engineer available. Veladon's policy editor is plain-English with regex as an escape hatch; the same capability but a default mode tuned for the non-engineer.

How much manual work is it to assemble an EU AI Act Article 26 evidence pack from Prompt Security logs?

Typical estimate: 40–120 hours per quarter for a 500–2,500 employee mid-market. The work includes exporting raw logs, mapping event types to Article 26(1) usage-log requirements, evidencing Article 26(2) human oversight per use case, producing Article 26(4) data-governance alignment, assembling the AI system inventory under Article 6, and formatting the pack as JSON + signed PDF. Veladon ships all of this pre-assembled in the base plan, cutting the quarterly GRC hours to near zero.

Can Veladon + Prompt Security coexist in the same environment?

Yes, and this is a reasonable stack. Veladon owns the employee-side browser redaction + SaaS-connector telemetry (the shadow-AI surface), Prompt Security owns the developer-side API gateway (the runtime LLM-app surface). Both write to a unified evidence index via webhook, and the quarterly pack crosswalks events from both tools. This stack covers a broader threat surface than either alone — employee-side DLP plus API-side guardrails.

What's the total cost of ownership (TCO) over 3 years for Veladon vs Prompt Security at 1,200 employees?

Veladon 3-year TCO at 1,200 emp: $75–125k (base plan + bundled evidence packs). Prompt Security 3-year TCO at 1,200 emp: $135–210k for the full three-surface bundle, or $75–120k for browser-only plus $40–90k of GRC hours per year assembling the evidence pack manually. If employee-side DLP is the primary use case, Veladon's TCO is lower and the GRC time savings are substantial.

Does Prompt Security work for NY DFS 23 NYCRR 500 AI circular letter evidence?

Prompt Security's raw logs cover the 500.2 monitoring and 500.11 third-party governance requirements after customer-side mapping. Veladon's evidence pack is pre-indexed to 500.2, 500.3, 500.9, 500.11, 500.14, 500.15 and the October 2024 AI circular letter. For a NY-chartered bank, insurer, or money transmitter facing a NYDFS examination in 2026, the pre-indexed pack saves the CISO 40–80 hours of quarterly preparation and reduces the risk of an examination finding on 500.9 (risk assessment) or 500.11 (third-party AI governance).

Related reading on Veladon

Framework pages

Long-form analysis

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