AI Policy Orchestration — Moving from Passive Flagging to Automated Enforcement
Enterprise AI teams were struggling to govern models and agents at scale. I led the design of an orchestration engine that turns static compliance rules into active, cross-platform enforcements.
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0.5 IMAGEEnterprises deploy hundreds of AI agents and models across fragmented ecosystems like AWS, Azure, and Foundry. Compliance and risk teams had no single place to enforce guardrails, leaving them blind to which models violated documentation or safety policies until after an audit broke down.
- AI policies were disconnected from actual infrastructure — flagging issues without remediation paths.
- Remediation was fully manual: teams were handed text instructions and forced to fix models across disparate cloud consoles.
- No automated mechanism to trigger enterprise risk reviews, assessments, or alerts when non-compliant models went live.
0.1b IMAGEDetailed process breakdown and technical tradeoffs below ↓
Defining "If This, Then That" for AI Governance.
The baseline logic engine.
We started with a foundational goal: allow compliance teams to select attributes and write conditional rules to identify which agents or models failed critical documentation or guardrail thresholds.
- Established condition operators (equals, not blank, contains) for model attributes
- Mapped out seeded guardrails across domains (Health, Loans, Employment)
- Created the primary mechanism to flag models in violation state
1.1 DIAGRAMValidating with TELUS, Dell, and Early Adopters.
The limits of manual checklists.
Testing our initial solution with large customers revealed a major flaw: our system gave them a checklist of remediation steps, but made engineers jump out to third-party tools to manually fix them.
- Tested text-based manual remediation guides with risk teams
- Discovered users found cross-system context switching tedious and error-prone
- Customer feedback proved that teams wanted the platform to push fixes directly into their cloud environments
2.1 IMAGEArchitecting System-Aware Enforcement Cards.
Enforcement at the infrastructure layer.
Instead of treating all models identically, we made policies environment-aware. We designed preset enforcement cards tailored to AWS, Azure Foundry, and internal actions.
- Built preset enforcement cards targeting specific cloud environments (AWS Bedrock, Azure)
- Enabled targeted rules: "If a model is in AWS East, apply this Bedrock Guardrail card"
- Designed automated triggers for instant risk creation and assessment launches
3.1 WIREFRAMEOrchestrating Actions and Mitigating Alert Fatigue.
From detection to executive digest.
We detailed the side drawers for complex rule triggers, configured automated risk evaluations, and designed daily digest reports to give stakeholders quick action items instead of endless notifications.
- Streamlined unresolved violation lists with quick-resolution actions
- Configured automated risk scoring with impact and probability criteria
- Designed automated email digests categorizing critical violations for policy owners
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4.3 IMAGELive across the platforms that matter.
Already running for real customers.
Now live in production, with coverage expanding across the platforms our earliest customers actually run on.
- Rolled out to GA with active enterprise adoption
- Shipped and in active use by Dell, TELUS, and Lumen
- Iterative roadmap expanded based on real-world multi-cloud orchestrations
5.1 IMAGE5.2 IMAGE6.1 BEFORE- Manual, text-only remediation checklists
- Disconnected from underlying cloud infrastructure (Bedrock, Azure)
- Risk teams forced to context-switch across external consoles to resolve issues
Preset Enforcement Cards (AWS Bedrock, Azure Foundry) allowed users to deploy deep technical cloud remediations without dealing with overwhelming API configuration settings.
Balancing notification frequency with actionable urgency. It required multiple rounds of design to separate instant high-severity triggers from consolidated daily and weekly digests.
AI Policy Orchestration evolved from a static rule-flagging concept into an automated enforcement engine. Since launch, it has cut average policy creation time to 2.8 minutes and reached 82% coverage across the platforms our customers actually run on — Dell, TELUS, and Lumen among them.