CISO Policy Routing: Segment Sensitive Data at Scale

# owners: security-platform@company.com, data-governance@company.com
# version: 1.7.3
# regions: [eu-central-1, eu-west-1, us-east-1, us-west-2]
# models:
#   eu: onprem-llm-v4 @ eu-central-1
#   us: managed-llm-gov @ us-east-1 (FedRAMP High)
# logging: snowflake://ai_trust_logs.policy_decisions (per-region accounts)
# SLOs: decision_latency_ms < 150; evidence_write_success > 99.9%

policies:
  - id: hr-eu-pii
    description: EU HR incidents with PII must remain in EU; redact before inference.
    match:
      sources: [servicenow]
      business_units: [HR]
      regions: [eu-central-1, eu-west-1]
      sensitivity: [restricted, pii]
    actions:
      - classify:
          deterministic: true
          labels: [pii, hr, eu]
      - redact:
          fields: [email, phone, national_id]
          method: pattern+dictionary
          required: true
      - route:
          region: eu-central-1
          engine: onprem-llm-v4
      - approve_if:
          min_confidence: 0.92
          approvers: []
      - human_in_loop_if:
          min_confidence: 0.80
          approvers: [dpo@company.com]
          sla_hours: 4
      - fail_closed_if:
          missing_labels: [pii]
          or_confidence_below: 0.80
    logging:
      prompt_logging: hash+store
      response_logging: hash+store
      retention_days: 365
      pii_snapshot: masked

  - id: it-us-internal
    description: US IT tickets without PII can use managed US model.
    match:
      sources: [servicenow, jira]
      business_units: [IT]
      regions: [us-east-1, us-west-2]
      sensitivity: [internal]
    actions:
      - classify:
          deterministic: true
          labels: [it, internal, us]
      - redact:
          fields: []
          method: none
          required: false
      - route:
          region: us-east-1
          engine: managed-llm-gov
      - approve_if:
          min_confidence: 0.70
          approvers: []
    logging:
      prompt_logging: hash+store
      response_logging: hash+store
      retention_days: 180

  - id: finance-secret
    description: Finance content marked secret requires two-person approval; no external models.
    match:
      sources: [jira]
      business_units: [Finance]
      sensitivity: [secret]
    actions:
      - classify:
          deterministic: true
          labels: [finance, secret]
      - route:
          region: us-east-1
          engine: rules-engine-only
      - approval:
          approvers: [ciso@company.com, controller@company.com]
          sla_hours: 8
      - fail_closed_if:
          any_external_call: true
    logging:
      prompt_logging: full
      response_logging: full
      retention_days: 730

observability:
  metrics_namespace: ai_trust
  thresholds:
    decision_latency_ms: 150
    exception_backlog_hours: 4
  alerts:
    on_violation:
      notify: [sec-ops@company.com, legal@company.com]
      severity: high
      pager: true
  weekly_brief:
    owners: [ciso_office@company.com]
    contents: [coverage, exceptions, evidence_latency, kpi_trends]

{
  "title": "CISO Policy Routing: Segment Sensitive Data at Scale",
  "published_date": "2025-12-11",
  "author": {
    "name": "Sarah Chen",
    "role": "Head of Operations Strategy",
    "entity": "DeepSpeed AI"
  },
  "core_concept": "Intelligent Automation Strategy",
  "key_takeaways": [
    "Policy-based routing enforces data residency and sensitivity rules across all automations—no more one-off exceptions.",
    "A 30-day audit → pilot → scale motion proves ROI and control coverage without slowing delivery.",
    "Implement a trust layer: classification, redaction, routing, approvals, and audit logging to Snowflake.",
    "Outcome to cite: 0 cross-border violations and 45% faster audit evidence assembly in the pilot."
  ],
  "faq": [
    {
      "question": "How do we prevent the trust layer from becoming a bottleneck?",
      "answer": "Treat it like any production service: scale horizontally, enforce <150ms policy decision SLOs, and precompute classifications for high-volume schemas. We instrument decision latency and evidence-write rates and alert if thresholds are crossed."
    },
    {
      "question": "Can we use LLMs to classify sensitivity?",
      "answer": "Use deterministic classification for enforcement and optionally use LLMs to enrich context. Hard boundaries—residency and secret data—should never depend on probabilistic decisions."
    },
    {
      "question": "Where do the logs live, and how do we prove residency?",
      "answer": "Each region writes to its own Snowflake account with RBAC and retention aligned to local law. Control reports are generated from in-region datasets with a decision ledger that references immutable IDs."
    }
  ],
  "business_impact_evidence": {
    "organization_profile": "Global financial services firm (32k FTE) operating in EU and US; ServiceNow + Jira; Snowflake data platform; AWS + Azure hybrid.",
    "before_state": "Region-agnostic automations with ad-hoc approvals; 7 audit findings on residency and logging; 18-hour average exception closure.",
    "after_state": "VPC trust layer with deterministic routing, RBAC, prompt logging to in-region Snowflake, and on-prem EU inference.",
    "metrics": [
      "0 cross-border violations across 14 pilot workflows",
      "5,200 analyst hours returned annually by eliminating manual exception routing and evidence prep",
      "45% faster audit evidence assembly (hours → minutes)",
      "Exception queue cut from 18h to 3.5h"
    ],
    "governance": "Legal and Security approved because routing was deterministic by policy, prompts/responses were logged to in-region Snowflake with RBAC, all sensitive content redacted pre-inference, and models never trained on client data."
  },
  "summary": "CISOs: Use policy-based routing to segment sensitive data by region and risk while automation scales. 30-day audit → pilot → scale with audit trails and RBAC."
}