A data governance framework that classifies company data by risk and defines how the organization responds when something goes wrong. Why Arcline needs it now, and what changes when it's in place.
When Arcline was smaller, data handling ran on team norms and good judgment. People knew what was sensitive because they knew the work. That was adequate when thirty people shared a Slack channel and the data footprint fit in one platform.
The company now operates across multiple business units, holds data subject to regulatory frameworks in several jurisdictions, and onboards people who have no institutional memory of which systems contain customer financials. The informal system scaled past the point where it functions as a system.
Customer records, financial credentials, and government-issued identifiers sit in production systems today. Some of that data triggers mandatory breach notification within 72 hours if exposed. The people handling it may not know what protections apply. The people accountable for it may not know they're accountable.
Meanwhile, sales and partnerships represent data handling practices to clients and prospects. Those representations rest on assumptions about what engineering does. No documented policy backs them up. That gap is a liability with or without a breach.
The framework classifies all company data by the severity of what happens if it's exposed. Each tier carries its own controls and response timelines, with accountability assigned at every level.
Health records, financial credentials, government IDs. Breach triggers mandatory regulatory notification within 72 hours.
Unreleased financials, vendor pricing, pre-board audit results. Serious damage to the company but no external reporting obligation.
Departmental planning docs and internal communications. Standard access controls, baseline encryption.
Press releases, published financials. Already through the approval process.
One deliberate design choice: the people who make governance decisions and the people who implement them are separated. This prevents the failure mode where the person closest to the data makes access decisions based on convenience.
Clear accountability at every level means incidents move through a defined chain instead of stalling while people figure out who's responsible.
Sets policy. Accountable for classification decisions and regulatory compliance within their domain.
First point of contact for data questions. Maintains access lists and runs quarterly reviews. Coordinates with legal as needed.
Implements controls and manages encryption. Handles access provisioning based on steward and owner directives.
When an incident happens, the chain activates in sequence. The discovering party reports. The steward triages. The owner decides. Nobody guesses who does what, and the response runs on structure rather than whoever happens to be in the room.
It creates three things the company currently lacks.
A breach today forces the company to build the response while the clock runs. The framework maps every step in advance.
A defined system lets the company build training so every team knows their responsibilities. Right now, there's no process to train people on.
Client-facing teams gain the ability to describe data governance specifics because a documented framework backs up the conversation.
Regulatory changes, client inquiries, incidents: the company handles all of them with a system that already exists instead of assembling one under pressure.