Mayank Sethi.

The industry's reflex answer to finding sensitive data is to point a large language model at every column. At this volume that is the expensive way to get a slow answer, which is why most organisations classify once and let the result go stale.

This engine resolves each column with the cheapest method capable of deciding correctly. Pattern matching handles the bulk, native classification catches what patterns miss, and generative AI is reserved for the genuinely ambiguous remainder. The hard problem was never detection. It was deciding what deserves the expensive tier.

Classification and enforcement are a single step: results write to tags that masking policies are already bound to, so a column is protected the moment it is identified. Data owners correct misclassifications through a self-service app, and those corrections travel the same orchestrated path as the automated run, so human and machine answers cannot diverge.

In a regulated firm, a request as small as read access to a table waits two to three business days. The delay is not difficulty. It is that nobody can prove the action was authorized unless a person signs off on it.

ADCP separates every request into four independent concerns, so authorization is evaluated mechanically and evidence produced automatically. The data owner still approves. The database engineer is no longer the bottleneck, and the automated path becomes more auditable than the manual one.

Built at PIMCO, then independently rebuilt at Group1001 under insurance regulation — only the policy and audit layers required reconfiguration. Over twelve months: 80% of ~6,200 requests resolved autonomously, latency cut from days to under five minutes, and zero incorrect authorizations across ~4,960 automated actions.

Most enterprises manage database credentials reactively — rotated after incidents, tracked in spreadsheets, governed by manual checklists.

SCALER treats credential lifecycle as a first-class engineering problem, and the ordering is the contribution. Scale → Classify → Assign → Lifecycle → Enforce → Rotate. Ownership is established before anything is touched, which is what separates remediation from an outage.

Applied across 1,000+ Snowflake service accounts spanning 150+ application teams, with automation handling 1,300+ related requests and no production outage. Derived entirely from production experience rather than vendor guidance.