Topic starter
10/06/2026 12:13 am
TL;DR: AI can help normalize tokens, variants, and usage guidance when it operates on real file context rather than abstract prompts, according to Lasso Security. The governance lesson is that contextual access must be observable and bounded, or the same tooling that improves consistency can expose product architecture and expand trust assumptions.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Building a Scalable Design System with AI & Figma MCP”.
Key questions
Q: How should security teams govern AI agent access to design files in MCP-based workflows?
A: Security teams should treat design systems as sensitive intellectual property and place MCP access behind policy enforcement.
Q: Why does contextual AI increase governance risk in design systems?
A: Because the model is no longer working from synthetic prompts.
Q: What are the signs that a design system needs tighter token governance?
A: Look for redundant colour values, spacing increments that no longer follow a base rhythm, radius values that do not align to the scale, and inconsistent naming that makes implementation ambiguous.
Practitioner guidance
- Define MCP access boundaries for design tools Limit which Figma files, branches, and component libraries an AI assistant can inspect, and separate exploratory read access from any workflow that can modify source design assets.
- Treat tokens as governed foundations Standardise semantic colour, spacing, radius, elevation, and surface tokens before allowing AI-driven refactoring so that the model reinforces a stable system instead of amplifying drift.
- Constrain contextual exposure in live environments Route AI access through an observable gateway, log file-level interactions, and review what structural metadata the tool can infer from component hierarchy and naming conventions.
Bottom line: AI-assisted design systems create a governance problem as soon as the tool is allowed to inspect real file context rather than synthetic prompts.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 20 hours ago by NHI Mgmt Group
11/06/2026 1:55 am
Context-aware AI is becoming a governance problem, not just a design-system convenience. Once an AI tool can inspect live file structures, the question changes from output quality to access scope. The control surface now includes component hierarchies, naming systems, and token relationships that can reveal product architecture. Practitioners should treat this as a contextual access design issue, not a content-generation feature.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- GitGuardian & CyberArk also reported that organisations maintain an average of 6 distinct secrets manager instances, a fragmentation pattern that often weakens centralised control.
A question worth separating out:
Q: When should security teams treat AI design tooling as an identity governance issue?
A: When the tool can read, extend, or refactor live production context without a human reviewing each access decision. At that point, you are governing a non-human access path with real entitlement scope, review requirements, and audit needs. The question becomes who can see what, for how long, and under which approval model.
👉 Read our full editorial: AI-assisted design systems need context-aware governance
12/06/2026 3:30 am
Context-aware AI governance is now an access problem, not just a productivity problem. When an AI system can inspect live design files, the governance question is no longer whether it can help teams work faster. The real question is what file context it is allowed to see, how that access is observed, and whether the resulting visibility is proportionate to the task. Practitioners should treat MCP connections as governed pathways into proprietary architecture, not as harmless assistants.
A few things that frame the scale:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: What should teams do when AI starts consolidating component variants?
A: Require explicit rules for legitimate states before consolidation begins. If loading, disabled, or alternate behaviours are not defined clearly, the AI will compress structure in ways that may hide real product differences. The right control is a state model that governs when a variant is actually justified.
👉 Read our full editorial: AI-assisted design systems need context-aware governance
This post was modified 20 hours ago by NHI Mgmt Group