PrimeSwarm's Governance Stack

The Problem with Monitoring

Most AI safety frameworks operate on a monitoring paradigm: observe agent behavior, flag anomalies, intervene after the fact. This is fundamentally insufficient for autonomous systems that execute at machine speed.

Mathematical Constraint

PrimeSwarm takes a different approach. We enforce safety through mathematical constraints that make unsafe actions physically incapable of forming — not just flagged after execution.

The 10 Layers

1. PII Sanitization

SSNs, account numbers, and NPI are stripped from context before agent interaction, replaced with cryptographic hashes.

2. Adversarial Defense

TPNN spatial constraints prevent prompt injection attacks from forming valid output vectors.

3. Fairness Gates

Automatic Adverse Impact Ratio calculation using the Four-Fifths Rule. Swarms trigger Pre-Effect Refusal if bias is mathematically detected.

4. Human-in-the-Loop Approval

Swarms with convergence scores below 98% halt and enter a secure queue for cryptographic sign-off.

5. Cryptographic Audit Receipts

HelixDB captures the exact vector state and topology at execution, creating immutable receipts.

6. SIEM Integration

Real-time security event forwarding to Splunk and Datadog.

7. Zero-Trust JWT Authentication

Every API call authenticated with JWT tokens and RBAC.

8. GDPR Retention

Session data with configurable retention policies.

9. Hardened Sandboxing

Docker with 6 security layers plus WASM backend for code execution.

10. MCP Tool Protocol

Pluggable external tools via JSON-RPC 2.0 with declarative configuration.

Competitive Analysis

No competing framework — LangChain, AutoGPT, CrewAI, OpenAI Assistants, or TrueForge — offers more than 3 of these 10 capabilities. PrimeSwarm offers all 10.