Because the title suggests, knowledge loss prevention (DLP) know-how is designed to assist organizations monitor, detect, and in the end stop assaults and different occasions that can lead to knowledge exfiltration and loss. The DLP know-how ecosystem—masking community DLP, endpoint DLP, and knowledge discovery DLP—has an extended historical past, going again almost 20 years, and with knowledge losses and leaks persevering with to impression organizations, it continues to be an vital safety management.

On this weblog, we’ll look again on the historical past of DLP earlier than discussing how DLP is helpful in at present’s surroundings, together with compliance, safety, and privateness use instances.

DLP Historical past 

Traditionally, nevertheless, DLP applied sciences have offered some points that organizations have discovered tough to beat, together with: 

DLP options had been additionally born within the period when safety applied sciences had been sometimes {hardware} home equipment or deployable software program—whereas the cloud barely existed as an idea—and most organizations had been targeted on perimeter safety. This meant that DLP was targeted largely on blocking or detecting knowledge because it crossed the community perimeter. With the cloud and different advances, this isn’t the truth at present, and infrequently neither customers nor the purposes reside inside the perimeter.

This new actuality means we’ve got to ask new questions: 

  • How do you reinvent DLP for at present’s world the place containers, microservices, cellphones, and scalable cloud storage coexist with conventional PCs and even mainframes?

  • How does DLP apply on the earth the place legacy compliance mandates coexist with fashionable threats and evolving privateness necessities? 

  • How does DLP evolve away from among the points which have harm its fame amongst safety professionals?

DLP at present

Let’s begin with the place among the confusion round DLP use instances comes from. Whereas DLP know-how is never cited as a management in rules at present (here’s an example), for a number of years it was extensively thought-about primarily a compliance answer. Regardless of that compliance focus, some organizations used DLP applied sciences to assist their risk detection mission, utilizing it to detect intentional knowledge theft and dangerous knowledge negligence. At the moment, DLP is employed to assist privateness initiatives and is used to observe (and decrease the danger to) private knowledge in storage and in use. 

Paradoxically, at some organizations these DLP domains generally battle with one another. For instance, if the granular monitoring of workers for insider risk detection is carried out incorrectly it could battle with privateness insurance policies.

One of the best makes use of for DLP at present reside below a triple umbrella of safety, privateness, and compliance. It ought to cowl use instances from all three domains, and achieve this with out overburdening the groups working it. Trendy DLP can also be a pure candidate for cloud migration attributable to its efficiency profile. In reality, DLP wants to maneuver to the cloud just because a lot enterprise knowledge is quickly moving there.

To display how DLP can work for compliance, safety, and privateness on this new cloud world, let’s break down a Cloud DLP use case from every area as an example some ideas and finest practices.

Many rules deal with defending one explicit sort of knowledge—fee knowledge, private well being data, and so forth. This could result in challenges like methods to discover that specific sort of knowledge so that you could shield it within the first place. In fact, each group strives to have well-governed knowledge that may be simply situated. We additionally know that in at present’s world, the place giant volumes of knowledge are saved throughout a number of repositories, that is simpler mentioned than achieved. 

Let’s take a look at the instance of the Cost Card Business Information Safety Normal (PCI DSS), an business mandate that covers fee card knowledge. (Learn more about PCI DSS on Google Cloud here.) In lots of instances going again 10 years or extra, the information that was in scope for PCI DSS—i.e. fee card numbers—was typically discovered exterior of what was thought-about to be a Cardholder Information Atmosphere (CDE). This pushed knowledge discovery to the forefront, even earlier than cloud environments turned standard. 

At the moment, the necessity to uncover “toxic” knowledge—i.e. knowledge that may result in probably painful compliance efforts, like fee card numbers—is even stronger, and knowledge discovery DLP is a standard technique for locating this “itinerant” fee knowledge. When shifting to the cloud, the identical logic applies: it’s essential to scan your cloud assets for card knowledge to guarantee that there is no such thing as a regulated knowledge exterior the methods or elements designated to deal with it. 

This use case is one thing that ought to develop into a part of what PCI DSS now calls “BAU,” or enterprise as common, quite than an assessment-time exercise. A very good apply is to conduct a periodic broad scan of many places adopted by a deep scan of “high-risk” places the place such knowledge has been identified to by chance seem. This will likely even be mixed with a deep and broad scan earlier than every audit or evaluation, whether or not it’s quarterly and even yearly. 

For particular recommendation on methods to optimally configure Google Cloud DLP for this use case, review these pages

DLP applied sciences are additionally helpful in safety danger discount initiatives. With knowledge discovery, for instance, somes apparent safety use instances embrace detecting delicate knowledge that’s accessible to the general public when it shouldn’t be and detecting entry credentials in uncovered code. 

DLP outfitted with data transformation capabilities may tackle an extended listing of use instances targeted on making delicate knowledge much less delicate, with the purpose of creating it much less dangerous to maintain and thus much less interesting to cyber criminals. These use instances vary from the mundane, like tokenization of checking account numbers, to esoteric, like protecting AI training data pipelines from deliberately corrupt knowledge. This method of rendering helpful, “theft-worthy” knowledge innocent is underused in fashionable knowledge safety apply, partially due to a scarcity of instruments that make it straightforward and easy, in comparison with, say, merely utilizing knowledge entry controls. 

The place particularly are you able to apply this technique? Account numbers, entry credentials, different secrets and techniques, and even knowledge that you just don‘t want a particular employee to see, such as customer data, are great candidates. Note that in some cases, the focus is not on making the data less attractive to external attackers, but reducing the temptation to internal attackers looking for a low hanging fruit.

Using DLP for privacy presented a challenge when it was first discussed. This is because some types of DLP—such as agent-based endpoint DLP—collect a lot of information about the person using the system where the agent is installed. In fact, DLP was often considered to be a privacy risk, not a privacy protection technology. Google Cloud DLP, however, was born as a privacy protection technology even before it became a security technology.

However, types of DLP that can discover, transform, and anonymize data—whether in storage or in motion (as a stream)—present clear value for privacy-focused projects. The range of use cases that involve transforming data that’s a privateness danger is broad, and contains names, addresses, ages (sure, even age can reveal the individual’s identification when small teams are analyzed), telephone numbers, and so forth.

For instance, let’s take a look at the case when knowledge is used for advertising and marketing functions (equivalent to pattern evaluation), however the manufacturing datastores are queried. It might be prudent on this case to rework the information in a manner that retains its worth for the duty at hand (it nonetheless permits you to see the correct pattern), however destroys the danger of it being misused (equivalent to by eradicating the bits that may result in individual identification). 

There are additionally helpful privateness DLP use instances within the space the place two datasets with lesser privateness danger are mixed, creating an information set with dramatically larger dangers. This will likely come, for instance, from a retailer merging a buyer’s buying historical past with their location historical past (equivalent to visits to the shop). It is smart to measure the re-identification dangers and rework the datasets both earlier than or after merging to scale back the danger of unintentional publicity.

What’s subsequent

We hope that these examples assist present that fashionable cloud-native DLP could be a highly effective answer for a few of at present’s knowledge challenges.In case you’d wish to study extra about Google Cloud DLP and the way it can assist your group, listed here are some issues to attempt:

  • First, undertake DLP as an integral a part of your knowledge safety, compliance, or privateness program, not a factor to be bought and used standalone

  • Second, evaluation your wants and use instances, for instance the forms of delicate knowledge it’s essential to safe

  • Third, evaluation Google Cloud DLP supplies, together with this video and these blogs. For privateness initiatives, review our guidance on de-identification of non-public knowledge, particularly.

  • Fourth, implement one or a really small variety of use instances to study the precise classes of making use of DLP in your explicit surroundings. For instance, for a lot of organizations the beginning use case is prone to be scanning to find one sort of knowledge in a selected repository.

We constructed Google Cloud DLP for this new period, its explicit use instances, and its cloud-native know-how. Take a look at our Cloud Data Loss Prevention page for extra assets on getting began.

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