"We definitely wanted to avoid data leaks, so about two years ago we had DLP configured across the board. However, nobody has touched it since, and to be frank, we don't really know if it's still working." This was an inquiry we received recently from an IT lead at a company with about 70 employees.
DLP (Data Loss Prevention) warns or blocks outbound transfers when confidential data—such as My Number IDs, credit card numbers, or proprietary files—is about to leak through emails, shares, or external links. The challenge is that this is not a feature you configure once and forget. As operations evolve, tool counts grow, and data types expand, rules established two years ago quietly drift from reality. In this article, we outline how to operate DLP continuously without letting it decay, from our perspective of supporting clients.
Why DLP is most effective in its first month and degrades thereafter
The reason DLP deteriorates is not because functionality breaks; it is because the company itself keeps changing.
Communications with new partners increase, leading to files being sent in previously unconsidered formats. New SaaS platforms are adopted, multiplying locations where sensitive data lives. Departments expand, altering types of personal data handled. With each change, gaps emerge between the data that needs protecting and the active detection rules. Rules aligned perfectly in month one routinely cover only half of operational reality a year later.
To make matters worse, no errors appear on screen as rules deteriorate. The rules appear to be running normally. Consequently, organizations leave them untouched under the assumption that "everything is probably fine," only discovering blind spots after a leak actually happens. We touched on the challenge of losing visibility over confidential files in our article on AI classification for Drive, and the same applies to DLP: the greatest danger of neglect is the false sense that it is working.
The June 2026 update: DLP rules can now be managed as code
This is where the Google Workspace DLP API expansion introduced in June 2026 comes into play. Previously, DLP rules and detection conditions could only be queried (read) via the API, but create, update, and delete operations have now been added.
What this means in practice is that instead of clicking manually through screens in the Admin Console, you can maintain, inspect, and batch-update rule sets as code or configuration files. You can inventory active rules in a unified list, maintain change histories, and institutionalize workflows such as quarterly rule reviews. While manual configuration often leaves teams unsure of who changed what and when, managing rules as code preserves full transparency.
The first step in client development is auditing existing rules, not adding new ones
When clients come to us for consultation, the first thing we tackle is not adding new detection rules. It is taking inventory of what rules are currently running and seeing whether they match today's actual operations.
Rules from two years ago often retain exceptions for tools that are no longer in use, or they fail to cover the information you now want to protect most in the first place. Adding rules without organizing this first increases false positives, causing frontline staff to get used to warnings and making things even more dangerous. Through an inventory, you visualize the gap between the actual data you need to protect and current rules, then prioritize and fix them from there. Being able to list rules with the DLP API has made it possible to run this inventory with a realistic amount of effort.
Case study: A company where false positives were so frequent that everyone ignored the warnings
Here is a specific example (company name withheld). We received a consultation from a company handling personal data that had implemented DLP in the past. In reality, however, warnings popped up every time something was shared, and reflexively closing warnings had become a habit for all employees. Under these conditions, if a truly dangerous incident occurred, no one would stop.
Therefore, we first aggregated the warnings actually occurring over a certain period and identified which rules were reacting even to routine business operations. We then narrowed down the clearly excessive rules and added back missing detections for information that truly needed protection. As a result, the total number of warnings dropped significantly, leaving only those that genuinely required verification. What worked was not adding advanced detection, but restoring the frontline's ability to trust warnings. For mechanisms to detect insider data exfiltration, please also see our article on audit logs and alerts.
Designing to keep business running before stopping operations
What you must watch out for to the very end with DLP is making rules so strict that business grinds to a halt. Blocking everything will prevent leaks, but it will also halt legitimate communication, leading frontline staff to view DLP as a nuisance and start looking for workarounds. That defeats the entire purpose.
That is why we do not suddenly jump to an outright block; we start with a phase of warning and logging, then elevate only operations that truly need to be stopped to blocking while reviewing actual logs. Preventing leaks from misdirected emails is covered in detail in our article on preventing Gmail misdirection and external data leaks, but the underlying philosophy is the same: continuously fine-tuning protection to a strength that allows frontline work to proceed is essential.
If you have not inspected your data leak prevention DLP since installing it years ago, if it has become a mere formality due to excessive warnings, or if you are worried about whether your current rules are actually protecting your data, please feel free to reach out through GleamHub's free IT and Google Workspace consultation. From inventorying existing rules to designing operations that keep business moving and setting up mechanisms for periodic audits, we will support you at a manageable pace.
