enclawed: A Configurable, Sector-Neutral Hardening Framework for Single-User AI Assistant Gateways
Alfredo Metere

TL;DR
enclawed is a configurable security framework for AI assistant gateways, enhancing trust, security, and auditability in regulated industries through strict policies, testing, and formal verification.
Contribution
It introduces a flexible, sector-neutral hardening framework with multiple deployment modes, comprehensive testing, and formal verification primitives for AI gateways.
Findings
Supports attestable peer trust and tamper-evident audit trails.
Includes extensive testing with 356-case suite covering security threats.
Provides formal verification primitives for secure model loading.
Abstract
We present enclawed, a hard-fork hardening framework built on the OpenClaw AI assistant gateway. enclawed targets deployments that need attestable peer trust, deny-by-default external connectivity, signed-module loading, and a tamper-evident audit trail -- typically regulated industries (financial services, healthcare, defense, government). The framework ships in two flavors: an open flavor preserving OpenClaw compatibility while emitting audit, classification, and data-loss-prevention (DLP) signals, and an enclaved flavor activating strict allowlists, FIPS cryptographic-module assertion, mandatory manifest signature verification, and high-assurance peer attestation for the Model Context Protocol. The classification ladder is data-driven: deployers pick from five built-in presets or supply their own JSON. We ship a 356-case test suite (261 unit + 95 adversarial pen-tests)…
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