TapPass vs Protect AI.
Protect AI secures the ML supply chain and model scanning. TapPass governs AI agents at runtime, scanning every LLM call as it happens.
TapPass vs Protect AI
| Capability | TapPass | Protect AI |
|---|---|---|
| ML model scanning | ✗ Not the focus | ✓ Guardian vulnerability scanning |
| ML supply chain security | ✗ Not the focus | ✓ Provenance & dependency scanning |
| Runtime prompt governance | ✓ Comprehensive runtime pipeline | ◐ Layer (LLM firewall) |
| PII detection & redaction | ✓ Comprehensive, with tokenisation | ◐ Basic via Layer |
| Data classification | ✓ Multi-level with routing policies | ✗ Not available |
| Tamper-evident audit trail | ✓ Cryptographically chained | ✗ Not available |
| Tool / function call governance | ✓ Permissions, scanning, zones | ✗ Not available |
| Human approval gates | ✓ Real-time workflows | ✗ Not available |
| Agent observability | ✓ Health scores, pacts, canaries | ✗ Not available |
| EU AI Act compliance | ✓ Art. 9, 12, 13, 14 | ◐ Risk assessment reports |
| Self-hosted | ✓ Docker + Helm | ✓ Self-hosted available |
| EU-headquartered | ✓ Belgium 🇧🇪 | ✗ US-based |
Supply chain security vs. runtime governance
Protect AI: ML supply chain
Protect AI secures models before deployment: scanning, dependency checks, provenance. It's about ensuring the model itself is safe to use.
TapPass: runtime governance
TapPass governs what happens when agents use models. Every prompt scanned, every response checked, every interaction audited, all in real-time.
Complementary, not competitive
Use Protect AI for your model supply chain. Use TapPass for runtime governance. Together they cover both the model layer and the interaction layer.
Secure models need governed agents.
This comparison is about scope, not quality. Detection tools do detection well. TapPass is the layer that decides what an agent may do, holds the calls that need a person, and leaves a record you can hand to an auditor. If you already run Protect AI, TapPass sits in front of it rather than replacing it.