Stop Building Apps, Start Engineering Control Planes

Most organizations think more apps means more productivity. They’re wrong. More apps mean more governance surface area — more connectors, more owners, more permissions, more data pathways, and more tickets when something breaks. Governance-by-humans doesn’t scale. Control planes scale trust. This episode breaks down a single operating model shift — from building apps to engineering control planes — that consistently reduces governance-related support tickets by ~40%. This channel does control, not crafts. 1. The Foundational Misunderstanding: “An App Is the Solution” An app is not the solution. An app is a veneer over:

• Identity decisions
• Connector pathways
• Environment boundaries
• Lifecycle events
• Authorization graphs

What gets demoed isn’t what gets audited. Governance doesn’t live in the canvas. It lives in the control plane: identity policy, Conditional Access, connector permissions, DLP, environment strategy, inventory, and lifecycle enforcement. App-first models create probabilistic systems.
Control planes create deterministic ones. If the original maker quits today and the system can’t be safely maintained or retired, you didn’t build a solution — you built a hostage situation. 2. App Sprawl Autopsy App sprawl isn’t aesthetic. It’s measurable. Symptoms:

• 3,000+ apps no one can explain
• Orphaned ownership
• Default environment gravity
• Connector creep
• Governance tickets as leading indicators

The root cause: governance that depends on human review. Approval boards don’t enforce policy.
They manufacture precedent. Exceptions accumulate. Drift becomes normal. Audits require heroics. Governance becomes theater. 3. The Hidden Bill App-first estates create recurring operational debt:

• 📩 Support friction
• 📑 Audit evidence scavenger hunts
• 🚨 Incident archaeology
• 💸 License and capacity waste

The executive translation: You can invest once in a control plane.
Or you can pay ambiguity tax forever. 4. What a Control Plane Actually Is A control plane decides:

• What can exist
• Who can create it
• What must be true at creation time
• What happens when rules drift

Outputs:

1. Identity outcomes
2. Policy outcomes
3. Lifecycle outcomes
4. Observability outcomes

If enforcement requires memory instead of automation, it’s not control. 5. Microsoft Already Has the Control Plane Components You’re just not using them intentionally.

• Entra = distributed decision engine
• Conditional Access = policy compiler
• Microsoft Graph = lifecycle orchestration bus
• Purview DLP = boundary enforcement layer
• Power Platform admin features = scale controls

The tools exist. Intent usually doesn’t. Case Study 1: Power App Explosion Problem: 3,000+ undefined apps.
Solution: Governance through Graph + lifecycle at birth. Changes:

• Enforced ownership continuity
• Zoned environments (green/yellow/red)
• Connector governance gates
• Automated retirement
• Continuous inventory

Results:

• 41% reduction in governance-related tickets
• 60% faster audit evidence production
• 28% reduction in unused assets

System behavior changed. Case Study 2: Azure Policy Chaos Problem: RBAC drift, orphaned service principals, inconsistent tagging.
Solution: Identity-first guardrails + blueprinted provisioning. Changes:

• Workload identity standards
• Expiring privileged roles
• Subscription creation templates
• Drift as telemetry
• Enforced tagging at birth

Results:

• 35% drop in misconfigurations
• 22% reduced cloud spend
• Zero major audit findings

Govern the principals. Not the resources. Case Study 3: Copilot & Shadow AI Blocking AI creates shadow AI. So they built an agent control plane:

• Prompt-level DLP
• Label-aware exclusions
• Agent identity governance
• Tool-scoped permissions
• Lifecycle + quarantine
• Monitoring for drift & defects

Results:

• Full rollout in 90 days
• Zero confirmed sensitive data leakage events
• 2.3× forecasted adoption

Not “safe AI.”
Governable AI. Executive Objection: “Governance Slows Innovation” Manual review slows innovation. Control planes accelerate it. App-first scaling looks fast early.
Then ambiguity compounds.
Tickets rise. Trust erodes. Innovation slows anyway. Control planes remove human bottlenecks from the hot path. The Operating Model Self-service with enforced guardrails:

• Zoning (green/yellow/red)
• Hub-and-spoke or federated on purpose
• Engineered exception workflows
• Standardized templates
• Incentives for reuse and deprecation

And one executive truth serum: 🎯 Governance-related support ticket volume. If that number drops ~40%, your control plane is real. If it doesn’t, you’re performing governance. Failure Modes Control planes rot when:

• Automation is over-privileged
• Policies pile without refactoring
• Labels are fantasy
• Orphaned identities persist
• Telemetry doesn’t exist

Governance must be enforceable, observable, and lifecycle-driven. Otherwise it’s theater. Conclusion Stop scaling apps.
Scale a programmable control plane. If this episode helped reframe your tenant, leave a review so more operators find it. Connect with Mirko Peters on LinkedIn for deeper control plane patterns.

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If this clashes with how you’ve seen it play out, I’m always curious. I use LinkedIn for the back-and-forth.