Treat context retrieval as a governed access path, not a passive read function. Define which assistants can access which metadata, lineage, and policy objects, log every retrieval, and require ownership for each integration. The goal is to prevent AI systems from consuming context that has not been authorised, validated, or classified for that use.
Why This Matters for Security Teams
Model Context Protocol changes the question from “what can the assistant do” to “what context can it retrieve, and why.” That matters because MCP turns enterprise metadata, lineage, policies, and operational records into a live access path. If that path is treated like passive read-only enrichment, teams can accidentally expose secrets, sensitive classifications, or internal decision logic to assistants that were never authorised for that purpose.
Governance also needs to account for the fact that assistant context requests are often distributed across multiple integrations, not a single front door. Current guidance suggests treating each retrieval as an access decision, not just an instrumentation event. NHI Management Group’s Top 10 NHI Issues and the Ultimate Guide to NHIs for Regulatory and Audit Perspectives both reinforce that retrieval paths need ownership, classification, and traceability, not informal trust. The control objective is simple: if an assistant can ask for it, the organisation must be able to explain why it was allowed.
In practice, many security teams discover context overexposure only after an assistant has already surfaced policy text, lineage notes, or customer data into a chat or workflow the business assumed was harmless.
How It Works in Practice
Governance for MCP-backed assistants starts with inventory. Security teams should identify every assistant, tool, connector, and context source, then classify what each retrieval path can return. The right control model is closer to authorisation at runtime than to static app permissions. That means the assistant identity, the user request, the data classification, the tool being called, and the business purpose should all be evaluated before context is released.
For implementation, organisations should anchor the assistant to a workload identity, issue short-lived access where possible, and log every retrieval with enough detail to support review. The OWASP Top 10 for Agentic Applications 2026 and NIST Cybersecurity Framework 2.0 both align with the idea that access should be governed, monitored, and reviewed rather than assumed. For MCP specifically, the Ultimate Guide to NHIs on lifecycle processes is useful for mapping how ownership, provisioning, rotation, and revocation should work across assistant integrations.
- Define approved context types for each assistant, such as metadata, ticket history, or policy documents.
- Block direct access to secrets, raw credentials, and unrestricted internal knowledge stores unless there is a documented exception.
- Require per-integration ownership so someone is accountable for each connector and retrieval route.
- Log the request, response class, policy decision, and downstream use of retrieved context.
- Review high-risk retrievals with the same discipline used for privileged system access.
Where this guidance breaks down is in environments with loosely controlled plugin sprawl, because assistant-managed connectors often bypass the same approval and logging paths that govern human-facing systems.
Common Variations and Edge Cases
Tighter context controls often increase operational overhead, requiring organisations to balance agility against the risk of overexposure. That tradeoff becomes more visible in fast-moving engineering, support, and analytics workflows where assistants need broad but not unrestricted access. Best practice is evolving here, and there is no universal standard for how much context an assistant should be allowed to infer versus retrieve explicitly.
One common edge case is layered retrieval, where an assistant first pulls metadata, then uses that metadata to query a second system that holds more sensitive records. Another is shared assistants serving multiple business units, where one team’s approved context can become another team’s leakage path. The AI Agents: The New Attack Surface report is especially relevant here because it shows how often agent behaviour exceeds intended scope, while the OWASP Agentic Applications Top 10 highlights the need to control tool chaining and data exfiltration risk. Organisations should also treat any assistant that can retrieve policy objects as potentially able to infer sensitive controls logic, even if no secrets are directly exposed.
Where a mature access review process is missing, MCP governance tends to fail first in shared service teams because context access accumulates faster than ownership and audit practice can keep up.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Agent tool and context access must be constrained to prevent unsafe retrieval paths. |
| CSA MAESTRO | GOV-02 | Governance of agentic workflows requires ownership and policy controls over context access. |
| NIST AI RMF | AI RMF addresses accountability, monitoring, and risk management for assistant context use. |
Restrict tool-enabled retrievals to approved purposes and evaluate each request before releasing context.
