Tuning a Noisy Account-Number Policy at a Retail Bank
Northbridge Retail Bank runs Forcepoint DLP on 9,400 endpoints and through the DLP Gateway for web and email egress. A policy named 'Customer Account Data' matches a 10-to-16 digit regular expression intended to catch account numbers. It fires roughly 11,000 incidents per day; the four-person review team can triage about 250. Ninety-two percent of incidents are closed as false positives, mostly order IDs, ticket numbers and phone-case references pasted into web forms and internal email. Executives want blocking enabled on email within two weeks, and the audit committee has asked for evidence that real account numbers leaving the bank would be caught. The core banking platform exports a nightly CSV of 1.2 million active account numbers, and customer service agents legitimately email masked account numbers showing only the last four digits to branch staff. DLP policy administration is centralised; local policy editing by business units is disabled.
1. Explain why the current policy produces this volume, and describe a classification redesign that cuts false positives without weakening detection of real account numbers. Cover technique, validation and masked numbers.
Read the worked answer
The regex has no context: any 10-16 digit run matches, and internal systems generate such runs constantly. Recall is fine; precision is the failure.
Rebuild in layers:
- Exact data matching. Load the nightly 1.2M-account CSV as a database fingerprint. An exact match against a live account number is high confidence and should drive the top-severity rule.
- Contextual regex as a fallback. Keep the regex only with a proximity keyword ('account', 'acct no', 'sort code') or an issuer prefix pattern, at lower severity and without blocking. This catches accounts not yet in the CSV.
- Masked numbers. Add an exclusion for masked formats (****1234, 'ending in 1234') or route them to a rule that creates no incident, because branch staff have a legitimate need.
- Aggregation. Aggregate incidents per user, per policy, per hour so a single paste of 40 numbers is one incident, not forty.
Rule precedence matters: the fingerprint rule must sit above the generic regex rule so a match escalates rather than duplicates in the queue. Validate by replaying 30 days of historic incidents through the new stack and measuring precision on a 500-incident sample. State the residual gap explicitly: accounts opened since the last CSV load are invisible to exact matching, so the refresh cadence is a detective control the audit committee will ask about.
2. The CISO wants email blocking live in two weeks. Describe the rollout sequence, the evidence you would require before enabling blocking, and the remediation actions you would choose per channel.
Read the worked answer
Two weeks is enough only for a staged rollout, not a big-bang change.
Week 1 - measure. Deploy the redesigned stack in monitor mode everywhere. Sample 300 email and 300 endpoint incidents, hand-classify them, and set a gate: no block until the sample shows at least 90 percent true positives and zero misses of live account numbers. Publish that gate, because it is also the audit evidence.
Week 1-2 - notify, then control. For email, enable user notification with justification on high-severity fingerprint hits, then block only external recipients with two or more distinct live account numbers. Internal email stays monitor-and-notify: the risk is lower and the helpdesk cost is high. Quarantine with release-by-approver is a better first control than silent drop, since a mis-tuned rule that blocks a payment instruction creates a business incident.
Endpoint. Do not block clipboard or print operations yet. Start with block-on-write to removable media and a watermark or encrypt action for downloads, piloted on a 200-user ring drawn from operations and finance before general release.
Run an exception process through directory groups, review it weekly, and count exceptions as a risk metric rather than a failure. Keep a documented rollback: one policy change reverts to monitor mode.
3. Six months later, incident volume is healthy but the audit committee asks how you know the control still works. What metrics, tests and maintenance controls do you present?
Read the worked answer
The committee needs evidence of effectiveness, not activity counts.
Detection evidence. Report precision and true-positive counts, plus the results of periodic seeding tests: insert known account numbers from a test set of live records into an outbound email and a web upload, and confirm both are caught within the expected time. Seeding is the only way to prove the pipeline end-to-end, because reviewers only see what the classifier surfaces.
Coverage evidence. Show channel coverage (endpoint, web, email) and the volume of traffic that bypasses inspection, for example unmanaged devices or browser-based uploads over personal networks. A control with silent coverage gaps is worse than a noisy one, since nobody is compensating for it.
Fingerprint hygiene. Track the last successful CSV load, record count drift against source, and alert when a load fails or record counts move more than a set percentage. A stale dataset quietly degrades precision to regex-only. Agree a named owner for the feed on the core banking side.
Process evidence. Time to triage, time to close, exception counts by group, and repeat offenders. Trend these rather than reporting absolutes; a rising exception count in one business unit usually signals a workflow the policy does not accommodate.
Present the residual risk plainly: accounts created since the last load, and encrypted archives that classifiers cannot read, remain gaps covered by other controls.