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.
Most AI pilots die at the system of record. Models can draft and plan; what fails is safe access to production content — legacy permissions, compliance review, ops handoff, and an audit trail leadership will accept. Wilkes & Liberty is a company of forward-deployed engineers for that last mile: we sit with the real workflows, wire governed agent access into the platforms you already run, and leave policy, redaction, and evidence behind — not another slide deck about AI strategy.
The failure mode we close
Connecting an agent with create/update/delete rights through a generic API or MCP bridge is an act of faith. Without server-side policy, field-level redaction, and attribution, every “productivity win” is also an ungoverned insider. Organizations stall for good reason. This practice implements the control plane those teams require so AI can do real work without expanding blast radius.
One security bar — commercial and regulated
Private-sector and nonprofit clients get the same engineering security bar we design for mission and government environments: fail-closed policy, least privilege, human gates on irreversible actions, and tamper-evident audit. That is not a premium federal package sold separately — it is how we build. Framework-specific federal control mappings and acquisition artifacts live on the Federal Buyers path; the enforcement discipline itself is universal.
What we do on the ground
Discovery first: which systems hold the content that matters, which agent jobs would actually change throughput, and which fields must never leave the boundary. Then integration: Model Context Protocol (MCP) connectors and policy profiles enforced at the system of record — not prompt-level restraint an agent can talk past. Then handoff: runbooks, revocation paths, and ops ownership so the engagement does not become permanent babysitting unless you choose managed ops.
- Field-level redaction — sensitive categories (drafts, access-controlled records, regulated fields) never surface to the agent, regardless of the query.
- Allow/deny tool policy — which operations each agent class may invoke, enforced in the connector and governance layer.
- Tamper-evident audit — every governed action attributable for investigation, policy refinement, and compliance review.
- Human gates on one-way doors — publish, irreversible deletes, and high-risk mutations reserved for accountable people by design.
Inspectable controls, customer-owned operations
We maintain open-source governance components and use them in scoped implementations when they fit the customer’s systems. The code makes core mechanisms inspectable; the engagement adds the customer’s identity, content model, policy decisions, audit requirements, acceptance evidence, and operations handoff.
Not the compliance paper trail — the enforcement layer
This page is about implementing governed AI access (connectors, redaction, policy, audit). Authoring the assessor-facing paper trail lives under Compliance & Security Governance. Building new agent workflows sits under Agentic AI Development; treating agents as untrusted principals is the delivery side of Zero-Trust Architecture.
Related platform and open foundation
Sentinel is the related governance platform. Its current Drupal foundation combines mcp_sentinel for policy, redaction, locks, and audit with drupal-mcp-connector for governed access. We use the same server-authoritative design against the content systems and AI toolchains in scope, with supported components and deployment boundaries confirmed during discovery.
Engagement path
Start with the AI & Sovereignty Readiness Assessment when you need an evidence-based map of AI exposure and control gaps. Move to implementation when the job is wiring governed MCP access into production. Stay on managed ops when you want continuous policy, audit, and connector hygiene.
Open a ticket — same as asking an internal IT team to take a job: structured intake (problem, systems, constraints, desired outcome) lands in our private queue. No sales theater required. Prefer a named discovery offer? Start with the Readiness Assessment.
When AI access becomes production access
Application owners, content-platform teams, security leaders, and AI program owners reach this point when agents or model-powered tools must act on enterprise systems without receiving broad, unreviewed authority. The work belongs before production MCP access is enabled—and immediately when pilots rely on shared credentials, sensitive fields can reach a model, actions are not attributable, or irreversible operations lack an operator gate.
The enforcement boundary
The enforcement boundary sits between AI clients and systems of record, covering identity, connector configuration, tool policy, entity and field access, approval gates, audit evidence, quotas, and operations. Discovery maps actors and requested actions; threat modeling defines trust boundaries; implementation wires scoped access; adversarial tests prove denials and redaction; handoff leaves policy ownership, evidence queries, and incident procedures.
Controls and evidence delivered
- An agent-access and data-flow map with named owners and risk decisions.
- Configured connectors, policy profiles, redaction, approvals, and audit controls.
- Acceptance evidence, operations runbooks, and a governed backlog for expansion.
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.