Data Modernization for Agentic AI: MOD for CX & SOC
- James F. Kenefick
- 9 minutes ago
- 5 min read
Mid-market team s are piloting “agents” to resolve tickets, issue refunds, and triage alerts—but most stumble when agents hit messy, stale, or siloed data. The result: hallucinations, failed automations, and risk. The fix isn’t “more data”—it’s Minimum Operable Data (MOD): the smallest, governed slice of trustworthy data required for safe autonomy in Customer Experience (CX) and Security Operations (SOC).
Executive brief (what leaders must know now)
MOD > big data. Identify the exact entities, features, and histories agents need to act (customer, order, entitlement, device, endpoint, identity).
Controls travel with data. Align to NIST CSF 2.0 (Govern → Identify/Protect/Detect/Respond/Recover) and your ISO/IEC 27001 ISMS.
Design for oversight. High-risk workflows need human-in/on-the-loop under the EU AI Act.
Outcome lens: CX self-resolution & FCR; SOC time-to-contain; audit artifacts produced per control.
Partner where it counts: BetterWorld’s Data Modernization and Autonomous AI Agents to land both the plumbing and the outcomes.
A reference model for MOD (composable and auditable)
Layers: Infra → Platforms → Data → Engineering → Models/Agents → Apps/Integrations → Security/Risk → Services
Data layer responsibilities
Contracts & semantics: canonical entities for customer, order, device/endpoint, identity, policy.
Lineage & quality: SLOs on freshness, completeness, and duplication; data tests in CI/CD.
Minimization & masking: only fields agents need; dynamic masking for PII/PHI.
Feature/embedding hubs: vetted features for fraud, intent, and endpoint health; vector indexes with retention rules.
Evidence store: immutable decision logs, prompts, tool calls, and approvals.
BetterWorld’s Data Modernization service aligns these with run-state operations; Managed IT keeps SLAs in place.
Control planes: identity, policy-as-code, observability
Identity: workload identities for agents, short-lived credentials, per-tool RBAC/ABAC tied to Govern.
Policy-as-code: purpose limits, thresholds (refund ≤$200 auto; >$200 HITL), region/PII routing, retention, logging (map high-risk to EU AI Act oversight).
Observability: capture prompts, decisions, tool calls, cost & the four golden signals (latency, traffic, errors, saturation) per SRE.
Safety & performance guardrails
HITL/HOTL & rollback on high-risk actions (money, privacy, safety).
Latency/cost budgets per journey (sub-second for CX tool calls; SOC may trade a second for certainty).
Continuous conformance inside your ISO 27001 scope.
Deployment patterns & measuring ROI
Batch: nightly access re-certs, policy drift scans, quality SLO checks.
Streaming: fraud/intent/endpoint signals update features and policies in near-real-time.
Online: agents plan & act during live interactions.
Board-credible KPIs
CX: self-resolution %, FCR, AHT, CSAT/NPS uplift.
SOC: MTTC/MTTR, incidents auto-contained, loss avoided (calibrate with IBM Cost of a Data Breach and Verizon DBIR).
GRC: audit findings closed, exceptions reduced, evidence hours saved.
Mini-cases (what MOD looks like in practice)
Retail CX—“no-receipt” refund
MOD = customer identity + order/entitlement + device reputation + abuse score.
Agent verifies, applies thresholds, executes partial/full refund, updates ERP/CRM, stores evidence.
Backed by Autonomous AI Agents; monitored via Integrated Risk Management.
SOC—tier-1 triage & containment
MOD = endpoint identity + alert context + EDR/AV state + playbook policy.
Agent correlates SIEM+EDR, quarantines per policy, opens ticket with decision log; rollback on clean bill.
Strengthen intel via Proactive Threat Intelligence.
GRC—ISO/NIST evidence packaging
MOD = IAM entitlements, EDR coverage, MDM posture, change records.
Agent assembles evidence bundles; flags gaps for owners.
Run under GRC Consulting with vCISO oversight.
Pilot-to-scale playbook (90-day plan)
Stage 0 (2–3 wks): pick 3 workflows (CX refund, ITSM reset, SOC isolate); define MOD entities & SLOs; set HITL/rollback.
Stage 1 (4–6 wks): stand up MOD pipelines, feature/embedding hubs, lineage & data tests; launch constrained pilots.
Stage 2 (6–8 wks): expand data sources; enforce policy-as-code; publish autonomy SLOs & cost caps.
Stage 3 (ongoing): add playbooks; integrate with CRM/ITSM/SIEM; close audit gaps; tune MOD SLOs.
Actionable checklist
Name a Data Product Owner for MOD (CIO/CDO co-sponsor).
Define canonical entities & retention/masking rules.
Implement lineage & quality SLOs in CI/CD.
Stand up feature/embedding hubs with retention/PII rules.
Enforce policy-as-code and agent identities.
Turn on golden-signal SLOs and cost budgets.
Ship 3 pilots; compare to baselines; publish results to the ELT/board.
Request a MOD Readiness Assessment + reference design workshop. BetterWorld will map your MOD footprint and stand up a pilot that moves CX and SOC needles in 90 days.
BetterWorld links used: Data Modernization · Autonomous AI Agents · Integrated Risk Management · Proactive Threat Intelligence · GRC Consulting · vCISO · Managed IT
Authoritative sources: NIST CSF 2.0 · ISO/IEC 27001 · EU AI Act · IBM Cost of a Data Breach · Verizon DBIR · Google SRE—Golden Signals
MOD Q&A: The Blueprint in CX & SOC
1) What exactly is “Minimum Operable Data (MOD)” and how is it different from “more data”?
MOD is the smallest, governed slice of trustworthy data an agent needs to take a safe action—nothing more. Think canonical entities with just the features required to decide and execute (e.g., customer, order/entitlement, device/endpoint, identity, policy). More data increases noise, latency, and risk; MOD increases precision, auditability, and speed.
2) How do we scope MOD for a workflow like CX refunds or SOC triage?
Start from the action, not the lake. For each step in the workflow, list the decision required and the minimum attributes to make it:
CX “no-receipt refund” → customer identity, order/entitlement history, device reputation, abuse/risk score, policy thresholds (e.g., ≤$200 auto, >$200 HITL).
SOC tier-1 triage → endpoint identity, alert context, EDR/AV state, playbook policy.If an attribute isn’t referenced by a rule, threshold, or model feature, it’s out of scope for MOD.
3) What controls must “travel with the data” for safe autonomy?
Identity: workload identities for agents, short-lived creds, per-tool RBAC/ABAC mapped to NIST CSF 2.0 Govern → Identify/Protect/Detect/Respond/Recover.
Policy-as-code: purpose limits, monetary thresholds, PII/region routing, retention, logging; high-risk actions tied to EU AI Act oversight (human-in/on-the-loop).
Observability: prompts, tool calls, decisions, costs, plus SRE “golden signals” (latency, traffic, errors, saturation) with SLOs and budgets.
4) How do we keep agents from hallucinating or over-reaching in production?
Constrain the environment and surface evidence:
Retrieve-then-act from vetted feature/embedding hubs; block free-text data fishing.
Enforce minimization & masking so only allowed fields are visible.
Require evidence store entries (input, features, prompt, tool calls, decision, approval) per action.
Set HITL/HOTL and rollback on money/privacy/safety moves.
Track drift and quality SLOs in CI/CD; fail closed on staleness or duplication breaches.
5) How do we prove ROI and scale beyond the pilot without losing control?
Measure what boards care about and publish deltas against baselines:
CX: self-resolution %, FCR, AHT, CSAT/NPS uplift, cost per contact.
SOC: MTTC/MTTR, % incidents auto-contained, loss avoided (calibrated with IBM Cost of a Data Breach & Verizon DBIR).
GRC: audit findings closed, exceptions reduced, evidence hours saved. Scale via a 90-day cadence:
Stage 0 pick 3 workflows + define MOD + HITL;
Stage 1 stand up pipelines, lineage tests, constrained pilots;
Stage 2 expand sources, enforce policy-as-code, publish autonomy SLOs/cost caps;
Stage 3 add playbooks, integrate with CRM/ITSM/SIEM, close audit gaps, tune SLOs.