Runtime controls that inspect prompts and responses in real time to block prompt injection, unsafe content, and data leakage before they reach users or systems.
A national AI strategy under MCIT with PDPPL data protection. Guardrails deployed in Qatar must also work in Arabic, not just English.
A national AI strategy under MCIT with PDPPL data protection.
Language coverage matters: primary business languages — Arabic, English. Ask vendors for measured accuracy per language, not a supported-language list.
Safeguards and guard models are configured for the use case — and updated or fine-tuned for your domain when needed.
Every command, tool call, and data access is observable in one place — across models, frameworks, and MCP servers.
Policies apply inline as agents act — block, redact, or require approval before the action executes, not after.
A preserved audit trail of every agent action — evidence you can hand to auditors, regulators, and incident responders.
PII and secrets detected and redacted in tool-call payloads and MCP traffic before data leaves your org.
GuardionAI builds its own guard models and publishes the benchmarks — the numbers below come from the public guard models guide. Independent alternatives are listed right after this section.
Guard models are natively trained on 8 languages (English, Spanish, Italian, Portuguese, Russian, Chinese, Hindi, Arabic) — including Arabic — extended to 100+ languages, and evaluated across 1,000+ languages — including historical and dead languages — with graceful post-training decay.
Precision 0.98 • FPR 0.02
Recall 0.99 • aligned with the NVIDIA Aegis (Nemotron) content-safety taxonomy
Precision 1.00 • FPR 0.004
PII groups
Person name · Contact · Address · Government ID · Payment · Digital ID · Company
Secrets
AWS access & secret keys · GitHub tokens · Google API keys · Slack tokens · Stripe keys · Private keys & JWTs · npm tokens · Seed phrases · Generic API keys, secrets & passwords
Policy engine decisions return in under 130ms — 20× faster than cloud provider guardrails. Benchmarks: sensitivity L1–L4, methodology in the docs.
Request a demoIndependently listed from the Runtime Guardrails & AI Firewall category of the index — each with its own analysis page.
A focused runtime security layer protecting against prompt injection, PII leakage, and hallucinations via API. Acquired by Check Point in late 2025 (~$300M reported); Lakera Guard and Lakera Red remain live products and anchor Check Point's Center of Excellence for AI Security.
Microsoft's cloud service for detecting harmful content, with Prompt Shields as its real-time API for blocking jailbreaks and indirect prompt injection from documents. By 2026 it also spans groundedness detection, protected-material detection, and a task-adherence API for agent tool misuse, feeding runtime signals into Microsoft Defender for AI.
Google Cloud's GA service for screening LLM prompts and responses for prompt injection, jailbreaks, harmful content, malicious URLs, and sensitive data leakage. Model-agnostic (works with Gemini, OpenAI, Anthropic, Llama over REST) and integrated with Apigee, Vertex AI, Agent Gateway, and Security Command Center, with org-wide floor settings for baseline enforcement.
GuardionAI covers the full ai guardrails stack (prompt defense F1 0.95, moderation F1 0.98, PII/DLP F1 0.97), and strong alternatives include Lakera (Check Point), Azure AI Content Safety (Prompt Shields), Google Cloud Model Armor — compared in detail on this page.
Runtime controls that inspect prompts and responses in real time to block prompt injection, unsafe content, and data leakage before they reach users or systems.
A national AI strategy under MCIT with PDPPL data protection.
Yes — Guardion's guard models are natively trained on Arabic (one of 8 training languages), extended to 100+ languages, and evaluated across 1,000+ languages — including historical and dead languages — with graceful post-training decay. Verify language coverage explicitly when evaluating any vendor.
Published benchmarks report precision/recall/F1 and false-positive rate per model family. Guardion publishes its methodology and per-sensitivity results (L1–L4) in the guard models guide: https://guardion.ai/docs/guides/guard-models.