When you think about your DLP approach, what immediately comes to mind?
Is it primarily centered around compliance? Is it simply using vendor-provided patterns of interest to satisfy an industry-specific framework like PCI, PII, or GDPR? Chances are, this probably describes at least some part of your DLP strategy because it is not difficult to set up and can satisfy a key business requirement of regulatory compliance reporting.
After reflection, most customers tell me this has two issues – efficacy and it does nothing to protect exfiltration of sensitive data.
Typically, the vendor-provided PII profile consists of a static set of patterns that can be applied to detect PII violations. It will result in true positive detections, but it will also have its fair share of false-positive detections, i.e. lower efficacy than operationally desired.
False positives are just a fact of life when using static patterns, as the patterns used to match against are in no way, shape, or form related to the actual customer data. And let’s keep in mind that customer data is never static: Is your database unchanged from last week? In addition, lack of data context means that an account number could potentially be mistaken for a social security number, to use just one example.
From my perspective, this “static” approach is based on a need to satisfy compliance. For many organizations this DLP approach is “good enough,” but it doesn’t have to stay that way.
In my interactions with customers, I have helped them reframe their strategy around an expanded understanding of DLP that is less centered around general compliance and more around protecting the information most important to your organization. This helps bring your security strategy into the future with an approach better tailored to what matters most to your organization: protecting data. Oh yeah, and it satisfies compliance needs.
A dynamic alternative to the static
What I’ve found is that there are organizations out there that need more than just unrelated static match patterns to keep their data safe.
Industries like banking or healthcare handle patient medical records, diagnoses, and appointment data, or savings account numbers, withdrawals, and deposits. Thousands of people visit hospitals and banks each day, resulting in proportional changes to records and files that hold the data. And virtual visits directly drive changes to data. With all of these moving parts, what good is a static set of match patterns, unrelated to the sensitive data, if they’re going to generate false positives? It adds even more load on security operations to manually investigate all those false positives. There has to be a better way to serve these kinds of organizations.
Consider a more dynamic approach to data matching where customers use their own living breathing set of data as the source for securely defining the match patterns. Further, consider that those patterns are refreshed on a regular cadence – daily/weekly/monthly – specific to their organization’s needs. This approach not only results in higher efficacy of detection due to pattern relation to the data and pattern freshness, but it also drives the false positive rate down to the floor, reducing incidents and thus saving operational costs. This isn’t just a “nice to have” aspect of DLP for organizations but a “must-have” that security leaders from across industries ask me about. Any Netskope customers today regularly highlight this to their leadership when describing how they prove value and efficiency in their security investments. In my opinion, it’s also a practical prerequisite to protecting data; blocking business transactions by mistake (i.e. false positive detections) will drive the users to find ways around the intended controls.
Because this approach is native to Netskope DLP, and at massive scale, it sets us apart from what other vendors can offer and redefines our role in helping our customers transform their security and DLP strategies.
More of a data governance partner than just a DLP provider
As we’ve begun implementing this dynamic approach to DLP with organizations across industries, it has begun to change Netskope’s relationship with our DLP customers.
I’ve run into instances where security leaders at prospective customers continued reaching out to us because they were interested by just how much our dynamic DLP could handle, or would go well over our set meeting time because they were interested in what this approach to DLP could bring them, even if they haven’t decided to choose Netskope (yet). It’s clear that this kind of dynamic, context-aware DLP with data matching is filling a need that our competitors simply aren’t addressing.
We also go to great lengths to keep that dynamic match data updated. Keeping the DLP running smoothly changes the way customers see us, and the conversations we can enable our customers to have.
I had a recent conversation with a security leader in the financial sector about the idea of DLP with data matching. He lit up when we talked about how the approach led to zero, or nearly zero, false positives. When a near-zero positive false rate resonates with a stakeholder like that, it’s clear that he’s got a real data leakage problem with some sort of database, files, or sensitive data that needs to be protected. He saw this as an opportunity for Netskope DLP to help play a big part in helping protect his crown jewel data.
That perspective, to an extent, continues to enhance the relationship we at Netskope have with our customers. Organizations see an opportunity to evolve their security strategy and prepare for the future utilizing the more advanced capabilities of Netskope DLP, which requires an immense amount of trust. Instead of being “just” a DLP provider, offering the bare minimum to help keep their organizations compliant, they see Netskope as a partner in data governance trusted with protecting their most important, sensitive data. It’s a big responsibility, but Netskope DLP is up for the challenge!
