Scale what’s safe to scale
The fastest path to AI regret is skipping governance. Support workflows touch PII, logs, credentials, and contractual obligations. You need a policy-to-practice framework that leadership can sign off on—and auditors can verify.
The governance triad
- Policy: Data classification, redaction and retention, residency, model access tiers, approval thresholds.
- Controls: Input redaction, source grounding, safety filters, rate limiting, prompt/tool change control.
- Assurance: Eval sets, red-teaming, drift monitoring, incident runbooks, and immutable audit logs.
What this looks like day to day
- Use-case intake: Risk assess the scenario; pick model class; define guardrails and HITL steps.
- Change control: Prompts, connectors, and tools are versioned, peer-reviewed, and canaried.
- Quality management: Track grounded-answer rate, refusals/overrides, and policy violations; review outliers weekly.
- Vendor oversight: DPAs, residency alignment, SOC2/ISO evidence, breach clauses, and model update cadence.
How to talk about this with execs
- Accuracy you can measure: Before/after eval scores against reference answers.
- Privacy you can prove: Redaction coverage and exception handling.
- Control you can trust: Time-to-rollback, audit timelines, and incident post-mortems.
Start small, certify early
- Stand up an AI Review Board (Support, Security, Legal, Data).
- Publish Acceptable Use and Redaction policies.
- Instrument your first quality dashboard (grounded answers, overrides, violations).
- Create a prompt change RFC and an AI incident playbook.
With governance in place, you unlock the real gains: higher automation levels, lower risk, and executive confidence.
RCG can operationalize AI governance that satisfies Security and delights the COO.