Why Data Classification Is the Foundation of DLP
In my conversations with agencies, the biggest challenge isn’t deploying tools, it’s understanding their data.
Most departments have a mix of structured and unstructured information spread across endpoints, servers and cloud platforms. Before any meaningful DLP strategy can take hold, that data must be identified and classified. Without that foundation, enforcement becomes inconsistent or ineffective.
There are tools that can help automate classification, but agencies still need to define policies that determine what qualifies as CJIS-sensitive data. That’s the first step toward controlling how information moves across users and systems.
From a technical standpoint, DLP can be implemented in two primary ways: at the endpoint or at the network layer.
Endpoint-based DLP uses agents installed on devices to monitor and control how data is handled locally. This approach is powerful because it follows the data wherever the device goes, whether users are in the office or working remotely.
Network-based DLP, on the other hand, focuses on inspecting traffic as it moves between systems, whether that’s email, web traffic or cloud applications. This allows agencies to stop sensitive data from leaving the environment in real time.
In practice, the strongest DLP strategy combines endpoint and network controls. However, many agencies are starting with network-level controls as a first step. It’s often faster to deploy and can immediately reduce risk by monitoring outbound data flows.
READ MORE: Here is what to know about monitoring vs. observability.
How SSE Platforms Support DLP in Public Safety
One of the more interesting developments I’m seeing is the adoption of security service edge (SSE) platforms to support CJIS requirements.
These platforms allow agencies to route traffic through a centralized cloud service, where it can be inspected, decrypted and analyzed for sensitive content. That’s particularly valuable for distributed environments like public safety, where officers may be accessing CJIS data from patrol vehicles or remote locations.
SSE solutions can also maintain persistent connections, which is critical in the field. If an officer loses cellular connectivity briefly, they shouldn’t have to reauthenticate and disrupt their workflow. Maintaining that session continuity while still enforcing security policies is a major advantage.
But even with these advancements, there are gaps.
One of the biggest challenges today is text-based data leakage, particularly through SMS text messaging or personal messaging platforms. There’s currently no simple, scalable way to apply DLP controls to those channels. That creates a potential blind spot where sensitive information could be shared outside of controlled systems.
Because of that, agencies are increasingly focusing on device-level restrictions — limiting what systems can access CJIS data and ensuring those devices are properly secured.
LEARN MORE: Governments must carefully manage device end-of-life.
A Practical Path Forward for Public Safety Agencies
If you’re just getting started with CJIS 6.0, my advice is to begin with network-level DLP. It’s a practical way to check compliance boxes and gain visibility into how data is being used.
From there, agencies can move toward deeper data classification efforts and eventually extend controls to endpoints. Cloud storage platforms can also play a role here, helping centralize data and make classification more manageable.
The reality is that CJIS enforcement often happens gradually. Agencies typically go through self-assessments before facing more formal audits, which gives them time to adapt — but that doesn’t mean they should wait.
CJIS 6.0 is a clear signal: Data itself is now the security perimeter. And the agencies that embrace that shift early will be in a much stronger position to protect the communities they serve.
