AI Governance & MCP Integration
Governed AI Access
Mission impact
AI capabilities that operate within defined organizational boundaries are capabilities your team can actually rely on — not systems that require constant supervision to prevent unauthorized data access or unreviewed content modification. By implementing governance at the connector and policy layer, we ensure that AI augments your operational capacity without creating new categories of insider-threat risk or compliance exposure.
AI systems that can read and write production content infrastructure without defined governance boundaries are an operational risk, not a capability advantage. Wilkes & Liberty designs and implements governed AI access frameworks that establish precise boundaries around what AI agents may read, what they may modify, and what they may never touch — with a complete audit record of every interaction. The implementation is built around the Model Context Protocol (MCP): structured, policy-governed connectors between AI systems and your content, data, and operational platforms.
Governed Access, Not Prompted Restraint
Field-level redaction prevents AI systems from reading or surfacing sensitive content categories — classified fields, draft content awaiting review, access-controlled records — regardless of the query. Allow/deny policy defines which tools and operations each AI agent class may invoke, enforced at the connector layer rather than through prompt-level instructions that can be overridden. Every interaction against governed systems lands in a tamper-evident audit record with enough detail to support audit, investigation, and policy refinement.
We build and maintain this governance tooling as published open-source software and run it against our own production systems — the enforcement model we sell is the one we depend on. Agents are non-person entities, and we treat each one as an untrusted principal; our Zero-Trust Architecture practice describes that posture in full, and the build side of the discipline lives in Agentic AI Development.
The Product Behind the Practice
This service is the implementation and integration complement to Sentinel, our enterprise AI-governance platform — whose Sentinel for Drupal instance (the `mcp_sentinel` module and the Drupal MCP Connector) ships and runs in production today. The same governance-layer design applies to content systems and AI toolchains you already operate — you do not need our platform to need this discipline.
Make AI Answerable
The organizations that benefit from AI are the ones that can state, with evidence, what their AI systems may touch, what they may never touch, and what they did last Tuesday. Contact us to scope a governance layer for AI you already run, or for AI you are about to deploy.
Key capabilities
MCP connector design and deployment
Purpose-built Model Context Protocol connectors that expose only the tools and data fields explicitly authorized by organizational policy, with no residual access surface.
Mission benefit: AI agents operate on the data your policy permits, not on everything they can technically reach.Field-level redaction policy engineering
Redaction rules that prevent AI systems from reading, surfacing, or operating on defined field categories — draft content, access-controlled records, sensitive metadata — enforced at the connector layer.
Mission benefit: Sensitive content categories are protected from AI access without requiring per-query human review.Allow/deny policy framework implementation
Structured allow/deny policies governing which operations each AI agent class may invoke, with policy definitions maintained in version-controlled configuration rather than prompt instructions.
Mission benefit: AI authorization is governed by auditable policy, not by instructions the AI itself could disregard.Audit logging and interaction traceability
Complete structured logs of every AI interaction against governed systems — tool invocations, data reads, content modifications — retained in customer-controlled storage with query capability for audit and investigation.
Mission benefit: Every AI action against production systems is accountable and reviewable.Governance framework review and policy design
Working sessions to define the organizational policy that governs AI access: what AI may read, what it may write, what escalation paths exist for ambiguous requests, and how policy evolves as AI toolchains expand.
Mission benefit: Governance decisions are made deliberately by your organization, not inherited from vendor defaults.
Defense & government relevance
Designed for organizations handling sensitive or access-controlled content: field-level redaction prevents AI systems from reading CUI or access-restricted fields; audit trails provide the documentation required for compliance review; allow/deny policy frameworks can be scoped to individual AI agent classes with the precision federal information security requirements demand. Fully operable in customer-controlled, on-premises environments with no dependency on external AI infrastructure.