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Runlayer vs WitnessAI

Enterprise MCP gateway vs. The AI firewall — compared on 12 governance & compliance criteria for enterprises putting AI agents into production.

Strong / documented Partial / indirect Absent / not publicly documented

Runlayer

Enterprise MCP gateway

Runlayer is an enterprise MCP gateway that routes every MCP request through a governed proxy — deterministic access policy (PBAC), agent identity and token brokering, shadow-MCP discovery (Watch), and an ML-based threat scanner (Guard). MCP is its entire center of gravity.

WitnessAI

AI security & governance (AI firewall)

WitnessAI is an inline "AI firewall" — agentless network-level discovery (Observe), an intent-based policy engine (Control), and inline protection with PII tokenization and prompt-injection blocking (Protect), enforcing at the tool-call and MCP-server level.

How they compare

Twelve criteria that decide whether an enterprise can prove — not just hope — that its AI agents stay inside policy.

Criterion Runlayer WitnessAI
Deterministic, rule-based authorization
Pre-execution enforcement
Segregation of Duties enforcement
Excessive-agency prevention / least privilege
Design-time action-surface mapping
Compliance-classified tools catalog
Full lifecycle coverage (design-time + runtime)
Named-approver human-in-the-loop routing
SOX / GDPR / financial-GRC control mapping & evidence
AI-specific standards (ISO 42001, EU AI Act, NIST AI RMF, OWASP LLM)
Immutable / tamper-evident audit ledger
GRC / internal-audit / IT-governance buyer fit

Where Runlayer is strong

  • Deep MCP focus — an 18,000+ server catalog across 300+ AI clients
  • Deterministic PBAC access control with least-privilege intersection of agent/user/server policies
  • Shadow-MCP discovery (Watch) and agent identity, including a 1Password partnership
  • SOC 2 Type II, HIPAA and GDPR, with tier-1 backing and MCP-protocol credibility

Where WitnessAI is strong

  • Best-in-class agentless AI observability — network-level shadow-AI discovery, no endpoint agents
  • Identity-based policy that attributes every agent action back to a human identity
  • Strong inline data protection — PII/PCI/PHI tokenization before data reaches a model
  • Real inline pre-execution blocking, with heavyweight backing and fast enterprise traction

What both leave to you: governance

Runlayer and WitnessAI secure how agents operate — but neither enforces Segregation of Duties, maps an agent's action surface at design time, or produces SOX/GDPR-grade compliance evidence. That is the layer LangGuard adds.

  • Deterministic, rule-based authorization on every action — reproducible and auditable, not probabilistic
  • Segregation-of-Duties enforcement built in — the only vendor in this set to ship it
  • Design-time action-surface mapping plus a compliance-classified tools catalog (SoD, SOX, GDPR, ISO 42001)
  • Named-approver human-in-the-loop routing and an immutable, tamper-evident audit ledger
  • Built for GRC, internal audit and IT governance — with SOX/GDPR control evidence

The bottom line

Runlayer and WitnessAI are both strong runtime security tools. If your requirement is deterministic authorization, Segregation of Duties, design-time action-surface mapping, and audit-grade compliance evidence, LangGuard governs what agents are allowed to do — before they do it — and works alongside either.

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