For your Python-based AI Network Troubleshooting Platform, I recommend implementing Data Governance as a centralized governance and policy enforcement layer that controls how data is collected, accessed, stored, processed, shared with AI agents, and retained.
Your application is more complex than a traditional chatbot because it combines network device access, LLM reasoning, MCP servers, external tools, user management, and custom agents. Data governance therefore needs to cover both traditional application data and agentic AI execution.
The key architectural principle should be:
Every user, agent, MCP server, and tool must operate within explicitly defined data access permissions, with auditable enforcement at every trust boundary.
1. Proposed data governance architecture
You do not necessarily need another container for every governance capability. Start with a reusable Python governance package shared by the API and AI core, then introduce a dedicated policy service when independent deployment or scaling becomes necessary.
2. Ten governance capabilities to implement
Capability | Purpose | Recommended implementation |
|---|---|---|
1. Data classification | Identify sensitivity of device data, prompts, credentials, logs | Classification labels and metadata |
2. Identity management | Authenticate users and service identities | OIDC/JWT, service identities |
3. Authorization | Control which data and actions are accessible | RBAC + ABAC |
4. Data isolation | Prevent cross-user, tenant, or project access | PostgreSQL RLS and tenant scoping |
5. AI governance | Control what agents and LLMs can access | Agent policy enforcement |
6. MCP governance | Restrict tool discovery, invocation, and outputs | MCP tool gateway |
7. Data protection | Protect sensitive information | Encryption, masking, secret management |
8. Data lineage | Track data from source to answer | Trace IDs and provenance records |
9. Audit and monitoring | Record sensitive actions and policy decisions | Append-only audit events and alerts |
10. Lifecycle management | Govern retention, deletion, and archival | Retention policies and cleanup jobs |
3. Data classification for network troubleshooting
Your application should classify network information before storing it or passing it to agents.
Classification | Example | Default handling |
|---|---|---|
Public | Product documentation, generic troubleshooting guides | Accessible to authorized application users |
Internal | Device inventory, interface status, topology | Organization-scoped access |
Confidential | Configuration files, internal IP addresses, routing details | Restricted roles, selective masking |
Restricted | Passwords, SNMP community strings, private keys, TACACS secrets | Never expose to LLM context; secure secret storage |
Classification should attach to individual data objects, not just database tables.
For example:
{
"resource_type": "device_configuration",
"device_id": "router-101",
"tenant_id": "tenant-A",
"classification": "confidential",
"owner": "network-operations",
"allowed_purposes": ["troubleshooting"],
"retention_days": 30
}The 30-day value is illustrative; production retention should be based on your organization's policy.
4. Authorization: RBAC combined with ABAC
For your application, Admin and User roles alone will not be sufficient.
I suggest starting with these roles:
Role | Permissions |
|---|---|
Platform Admin | Manage platform configuration and governance |
Organization Admin | Manage users, devices, and policies within an organization |
Network Operator | Troubleshoot assigned devices and run approved commands |
AI Agent Developer | Create and configure custom agents |
Standard User | Run permitted troubleshooting conversations |
Auditor | Read governance logs and policy evidence |
RBAC determines broad permissions, while ABAC evaluates additional attributes such as device ownership, environment, classification, and action risk.
Example:
A Network Operator asks an agent to execute show running-config on a production router.
The policy engine evaluates:
{
"subject": {
"role": "network_operator",
"tenant_id": "tenant-A"
},
"resource": {
"type": "network_device",
"device_id": "router-101",
"tenant_id": "tenant-A",
"environment": "production"
},
"action": "read_configuration",
"context": {
"purpose": "troubleshooting",
"approval": false
}
}The decision might be allow_with_redaction if the operator has configuration-read permission. Sensitive credentials are removed before the result enters the LLM context.
A configuration-changing command should require a separate permission and potentially human approval.
Important: User authorization must also constrain agents. An agent should never acquire greater access merely because its MCP server or device connector uses a privileged service account.
5. MCP server and custom agent governance
This is one of the most important areas in your architecture.
User Request
AI Agent
Policy Check
Network Device
MCP Tool
Allowed Action
For custom agents, introduce an Agent Registry with versioned definitions and approval states.
Each agent should declare its allowed tools, permitted data classifications, resource scope, execution limits, and whether it can perform write operations.
Example:
agent:
name: network-diagnostics
version: "1.0"
owner: network-operations
status: approved
permissions:
allowed_tools:
- get_interface_status
- get_device_logs
- get_routing_table
denied_tools:
- reload_device
- change_configuration
data_access:
maximum_classification: confidential
tenant_scope: current_user
execution:
max_tool_calls: 10
require_approval_for_writes: trueThese definitions must be enforced server-side, not merely inserted into an agent's system prompt.
For MCP specifically, enforce tool allowlists, trusted server registration, input validation, output sanitization, tool-call limits, and explicit approval for high-risk operations. Treat MCP responses as untrusted data because malicious or compromised device output could contain prompt-injection instructions.
6. PostgreSQL data governance model
You can extend your existing PostgreSQL database rather than introducing a separate governance database initially.
I recommend these logical table groups:
Group | Suggested tables |
|---|---|
Identity |
|
Organizations |
|
Network inventory |
|
Agent registry |
|
MCP registry |
|
Data governance |
|
Policies |
|
Audit |
|
AI lineage |
|
Use PostgreSQL Row-Level Security to enforce tenant boundaries, with carefully configured application roles that cannot bypass RLS.
For sensitive operational records, consider separating application transaction data from immutable or tamper-evident audit storage.
7. Data lineage and AI observability
A traditional audit log recording only username and timestamp is insufficient for an agentic troubleshooting application.
For each troubleshooting request, you should be able to reconstruct:
Example execution trace
User: Network Operator submits a BGP troubleshooting question.
Agent: Routing Diagnostics Agent v1.2 is selected.
Policy: User and agent are authorized for router-101.
Tool: MCP invokes
get_bgp_neighbors.Data: Device response is classified and sanitized.
LLM: Model generates a diagnosis using approved context.
Audit: Trace, policy decisions, tool calls, and source references are recorded.
Capture correlation IDs across NGINX, API, AI core, and MCP tools. Record model and prompt versions, tool versions, device identifiers, policy decisions, latency, and outcome status.
Avoid indiscriminately logging raw prompts or full device outputs because those may contain credentials or sensitive infrastructure information.
8. Recommended Python technology stack
Requirement | Technology |
|---|---|
API enforcement | FastAPI dependencies and middleware |
Policy engine | Open Policy Agent (OPA) or Casbin |
Identity | OIDC provider, JWT validation, TACACS+ integration where appropriate |
Database isolation | PostgreSQL RLS |
Secret management | HashiCorp Vault or cloud secret manager |
AI observability | OpenTelemetry and Langfuse |
Metrics | Prometheus and Grafana |
Structured logging | Python structlog |
Data validation | Pydantic |
Encryption | TLS and encrypted database/storage volumes |
For your current containerized architecture, Casbin may be simpler for initial embedded authorization, while OPA becomes attractive when you need consistent policy decisions across independent containers and MCP services.
Neither tool alone supplies classification, retention, audit, or lineage. Those must be designed separately.
9. Suggested implementation sequence
Implementation checklist
Phase 1 — Foundation
Phase 2 — Agent and MCP controls
Phase 3 — Protection and lineage
Phase 4 — Enterprise governance
10. My recommendation for your first version
I would prioritize five mandatory controls before expanding the platform:
Central authorization: RBAC + ABAC for users, agents, devices, and MCP tools.
Tenant isolation: PostgreSQL RLS and tenant-aware access checks at every boundary.
MCP and agent guardrails: Strict tool allowlists, approval for writes, and execution limits.
Data protection: Classification, credential redaction, encryption, and secrets management.
End-to-end auditability: Correlate user requests, agent reasoning steps, tool executions, policy decisions, and network data sources.
This provides a practical governance foundation aligned with concepts from NIST AI RMF, NIST SP 800-53, and ISO/IEC 42001, although implementing these controls alone does not establish compliance.
The next architectural decision is whether you want data governance embedded within the existing four containers or a separate centralized Governance Service with its own APIs and policy engine.
For your platform, I would lean toward a hybrid: centralized policy definitions and decisions, but distributed enforcement within FastAPI, the AI core, and the MCP gateway. This avoids governance becoming a single bypassable checkpoint while preserving a consistent policy model.
I can next detail the production-ready database schema, Python package structure, FastAPI middleware, authorization policies, MCP governance gateway, and Docker Compose changes for this architecture.
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