CUE for Safety-Critical Infrastructure Validation

אם ירצה ה׳

Configuration errors cause over 50% of production outages. CUE stops these errors before deployment through mathematically rigorous constraint validation that traditional tools cannot match. For safety-critical systems—where misconfiguration can harm people—CUE provides executable safety requirements with formal verification properties that support certification to ISO 26262, IEC 61508, and DO-178C.

Implementation Essentials

Business Outcomes

• Reduced incident frequency: Configuration errors caught pre-deployment
• Accelerated compliance: Automated evidence generation for safety certification
• Competitive differentiation: Demonstrable safety engineering excellence
• Lower certification cost: Formal verification properties reduce manual review

2. The Problem: When Infrastructure Configuration Becomes Safety-Critical

2.1 From Convenience to Consequence

Infrastructure as Code began as a convenience—version-controlled, repeatable deployments. For most organizations, it remains there: configuration errors cause downtime, financial loss, operational friction. But for safety-critical systems—autonomous vehicles, medical devices, industrial control—the same configuration errors can cause harm.

The YAML that configures a Kubernetes deployment for a recommendation engine and the YAML that configures a Kubernetes deployment for a surgical robot use identical syntax. The difference is not in the format but in the consequences of misconfiguration: resource starvation that degrades recommendations versus resource starvation that interrupts critical monitoring.

2.2 The YAML Validation Gap

YAML's design priorities—human readability, flexibility, minimal syntax—directly conflict with safety assurance requirements. Consider:

replicas: 3
resources:
  limits:
    memory: "128Mi"  # Is this sufficient? Safe? Tested?
  requests:
    memory: "64Mi"   # Must be ≤ limits, but who checks?

Traditional validation answers: syntactically valid? (yes, well-formed YAML). Schema valid? (maybe, if JSON Schema defines the fields). Safe? (unknown—safety is outside validation scope).

2.3 Why Traditional Tools Fall Short

What safety-critical validation requires: mathematical guarantees that constraints hold, regardless of how configurations are composed or ordered.

3. CUE: A Technical Primer for Security Engineers

3.1 What Makes CUE Different

CUE is not a better schema language. It is a constraint programming language for configuration, with three distinctive properties:

Order-independence: A & B equals B & A. Always. No override surprises.

Monotonicity: Adding constraints can only make results more specific or fail. Never less specific.

Explicit failure: Incompatible constraints produce _|_ (bottom), with precise error location. Never silent override.

These properties emerge from lattice-based semantics, not implementation choice. They are provable and portable across CUE implementations.

3.2 Constraint Unification in Practice

Basic CUE validates what JSON Schema validates, then goes further:

// Schema: what must be true
#Deployment: {
    replicas: int & >0 & <100      // Type + bounds
    image: =~"^registry.company.io/"  // Pattern
    resources: {
        requests: memory: string
        limits: memory: string
        // Cross-field: limits ≥ requests
        limits: memory: >=requests.memory
    }
    // Cross-field: replicas affects other constraints
    if replicas > 1 {
        strategy: type: "RollingUpdate"
    }
}

// Data: what is configured
myApp: #Deployment & {
    replicas: 3
    image: "registry.company.io/app:v1.2.3"
    resources: {
        requests: memory: "64Mi"
        limits: memory: "128Mi"  // Valid: 128Mi ≥ 64Mi
    }
}

The & operator is unification, not Boolean AND. It produces the most specific value satisfying both operands, or _|_ if none exists.

3.3 YAML Integration at Scale

CUE's cue vet command validates existing YAML without conversion:

# Validate all YAML in directory against schema
cue vet -c deployment.cue -d '#Deployment' k8s/*.yaml

# Concrete (-c) requires all fields have values
# Violations: precise file:line:column in both schema and data

For CI/CD:

# .github/workflows/validate.yml
- name: CUE Validation
  run: cue vet -c ./schemas ./deployments
  # Non-zero exit on violation blocks deployment

4. Safety Engineering with CUE: Three Concrete Patterns

4.1 Pattern 1: SIL-Aware Kubernetes Resource Validation

Safety Integrity Levels require demonstrable implementation of safety mechanisms. CUE encodes this structurally:

// Base: ASIL-agnostic safety fundamentals
#SafetyBase: {
    runAsNonRoot: true
    readOnlyRootFilesystem: true
    allowPrivilegeEscalation: false
    seccompProfile: type: "RuntimeDefault"
}

// ASIL D: highest integrity, most constraints
#ASIL_D: #SafetyBase & {
    replicas: >=2  // Redundancy required
    // Anti-affinity: distribute across failure domains
    affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: [{
        labelSelector: matchLabels: app: string
        topologyKey: "topology.kubernetes.io/zone"
    }]
    // Health monitoring with tight thresholds
    livenessProbe: {
        initialDelaySeconds: <=10
        periodSeconds: <=5
        failureThreshold: <=3
    }
    // Resource guarantees for predictable performance
    resources: {
        requests: cpu: string
        limits: cpu: requests.cpu  // Guaranteed QoS: requests == limits
    }
}

// Apply to critical workload
criticalService: #ASIL_D & {
    replicas: 3
    // ... other configuration
}

Validation guarantees: Any deployment claiming ASIL_D compliance must satisfy all structural requirements. Missing anti-affinity, excessive probe thresholds, or burstable QoS are caught before deployment, not in safety audit.

4.2 Pattern 2: Automated HAZOP Guide Word Enforcement

HAZOP guide words systematically identify hazardous deviations. CUE constraints can encode preventive patterns:

Example—preventing "NO [safety monitoring]":

import ( 
            "list"
            "strings"
)

#MonitoredWorkload: {
    // The ! means: must be specified, no default
    metricsPort: int & >1024 & <65536
    healthEndpoint: string & =~"^/health"
    alertingRules: [...string] & list.MinItems(1)
    
    // Derived: monitoring must be reachable
    _monitoringValid: ports.containerPort == metricsPort
}

4.3 Pattern 3: Fault Tree Cut Set Prevention in Terraform

Note: This pattern is architectural; specific implementations require HCL-to-CUE conversion.

Fault Tree Analysis identifies minimal cut sets—smallest combinations of failures causing system hazard. CUE can enforce structural prevention: diversity requirements that eliminate common cause failures.

import ( 
           "list"
           "strings"
)

// For safety-critical redundancy: diverse implementations required
#DiverseRedundancy: {
    channels: [...#Channel] & list.MinItems(2)
    // All channels must have distinct implementations
    _implementations: [for c in channels {c.implementation}]
    _unique: list.Unique(_implementations) & {
        len(this) == len(channels)  // No duplicates
    }
}

#Channel: {
    implementation: string  // e.g., "vendorA-v1.2", "vendorB-v3.4"
    nodeSelector: topology.kubernetes.io/zone: string
    // Zones must differ across channels
}

5. Implementation in Production Environments

5.1 Pipeline Integration Architecture

┌─────────────────┐     ┌─────────────┐     ┌─────────────────┐
│  Developer IDE  │────→│  Pre-commit │────→│   PR Validation │
│  (CUE LSP)      │     │  (cue vet)  │     │  (full schemas) │
└─────────────────┘     └─────────────┘     └─────────────────┘
                                                    │
┌─────────────────┐     ┌─────────────┐            │
│  Production     │←────│  Deployment │←───────────┘
│  (monitored)    │     │  Gate       │
└─────────────────┘     └─────────────┘
        │
        ↓
┌─────────────────┐
│  Incident:      │
│  CUE validation │
│  evidence for   │
│  safety case    │
└─────────────────┘

5.2 Constraint Library Organization

cue.mod/
├── pkg/
│   ├── chokmah.io/safety/v1/      # Base safety patterns
│   │   ├── asil.cue               # ASIL-A through D
│   │   ├── sil.cue                # SIL 1-4 mappings
│   │   └── redundancy.cue         # N-modular, voting
│   ├── chokmah.io/security/v1/    # Security controls
│   │   ├── podsecurity.cue        # PSS implementation
│   │   ├── network.cue            # Zero-trust patterns
│   │   └── rbac.cue               # Least-privilege
│   └── chokmah.io/compliance/v1/  # Framework mappings
│       ├── iso26262.cue           # Automotive
│       ├── iec61508.cue           # Generic functional safety
│       └── nist80053.cue          # US government
└── usr/                           # Application constraints
    └── myapp/
        └── deployment.cue         # Uses chokmah.io/safety/v1

5.3 Measuring Safety Outcomes

6. Getting Started: A 30-Day Adoption Plan

6.1 Week 1-2: Pilot Selection and Team Enablement

• Select pilot: Single application, Kubernetes-native, existing configuration error pain
• Assemble team: 2-3 engineers with Go experience, safety engineering liaison
• Training: CUE fundamentals (https://cuelang.org/docs/tutorials/), constraint-based thinking workshop
• Initial schema: Port existing JSON Schema or develop from safety requirements

6.2 Week 3-4: Production Constraint Deployment

• CI integration: cue vet in PR checks, blocking on violation
• Developer experience: IDE plugins, pre-commit hooks
• Constraint refinement: Based on initial feedback, false positive elimination
• Documentation: Constraint purpose, safety rationale, example violations

6.3 Month 2+: Scaling and Optimization

• Expand coverage: Additional resource types, cross-resource constraints
• Organizational rollout: Training additional teams, constraint library governance
• Advanced patterns: Template validation, differential analysis, safety case integration
• Community contribution: Share patterns, engage with CUE safety-critical use case development

7. Resources and Expert Engagement

7.1 Open Source Tools and Libraries