Machine learning: Data protection’s next frontier

What machine learning and the future of data protection means for organizations’ data security strategies and how Titus solves the challenge.

As you may have read, today we announced the introduction of Titus Intelligent Protection, which will enable our customers to leverage machine learning to accelerate the adoption of a data protection strategy that works for them. While we’re excited about this, what I want to talk about is machine learning and the future of data protection.

Titus was first to market in 2005 with a data classification solution that became widely adopted across global organizations. Since then, we’ve seen the market dramatically change as it went from early adopters through to validation with major platform players waking up to classification’s importance in the data protection equation. Being first to market means we’ve seen it all – the good of course, but more importantly, we’ve learned why enterprises struggle to adopt data classification and ultimately, data protection. Our job as an enterprise software company is to continue to push innovation so we can make that adoption experience as frictionless as possible. Machine learning will be key to doing that successfully.

It’s not about the user

When deploying data classification, organizations shift accountability/responsibility of your sensitive information to humans and away from security professionals. While we believe it is critical to creating a culture of security within an organization, we need to ensure we are assisting people as much as possible in this experience. The good news is that technology such as machine learning has become democratized and is now used pervasively to solve many problems.

Unsurprisingly, data classification leaders, including Titus, have traditionally championed the user as the critical element in identifying and then applying proper classification to data they’ve created. After all, as creators, they, more than anyone else, know how valuable it is and the best security to apply. But that’s not always true. Users and human, and humans make mistakes. In fact, these mistakes speak to the essential challenge organizations face in deploying data classification and, more broadly, data protection solutions – confidence.

Deploying machine learning as part of your organization’s data protection strategy can provide the critical assistance users need to apply the proper safeguards to data they’ve created without adding friction to their day-to-day activities. In fact, as you start to inform and develop your corpus, machine learning can go one step further and remove the user from the equation while increasing confidence in your organization’s ability to identify, contextualize and classify its unique data

This is a departure from where we’ve been in the past, but it’s a necessary departure. Adding machine learning capabilities will reduce errors and result in more rapid adoption of a data protection strategy that’s unique to your organization.

A new way of thinking

This sounds revolutionary, doesn’t it? That may be so, but it also sounds familiar. Think about when Box and Dropbox entered the collaboration market. They fundamentally and forever changed the way we think of collaboration. Like that example, machine learning is not a feature or a singular solution. It is a market disruptor that marks a new way of thinking about data protection. If you don’t believe machine learning will change the way organizations protect sensitive data, you may well be left behind.

That said, it’s also not a ‘silver bullet’ that will solve all your organization’s data protection concerns. This is a new approach that’s still maturing and will continue to evolve over time as it becomes more ingrained in your overall approach to information security. Approaching this as the one true answer to your data protection challenges isn’t the right approach. Machine learning is a new way of thinking about data protection and a journey – a necessary one, at that.

A necessary journey…together

As we’ve talked to customers about Titus Intelligent Protection and what machine learning can mean for their data protection initiatives, the biggest question we get is, “How do I start?” As I said, machine learning isn’t a destination, but a journey, and one that will require a partner. It requires you and your organization to buy into the fact that your data, especially your sensitive data, is unique to your business. It will take time to fine-tune models to fully understand and reflect this uniqueness.

I can tell you this from firsthand experience. Titus has been on a machine learning journey for some time now. We know that starting small and starting fast will be the best way for your organization to feed its corpus and understand the context around all your unique data. We’ve had to think about what a corpus means for us, and what a policy looks like for us as we continue this journey.

I believe that because we’ve been on this journey, we can confidently partner with your organization to help you take advantage of this amazing technology. Our team has changed the way we think about data protection – we’ve disrupted ourselves! We’ve broadened our professional services offerings beyond our core data classification and data loss prevention services to include ways to help our customers build their models. We understand the work your organization needs to do because we’ve done it, too.

At this point, some of you may have concluded you’re not quite ready to deploy this type of technology within your organization, and that’s okay. But the pervasiveness and democratization of machine learning will continue whether your organization is ready or not, so if you can’t start today, you need to start thinking about when you’ll be ready because this is a journey you’ll need to take.

Excited? We are. This is the beginning of a revolutionary way to think about data protection!

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Mark Cassetta, senior vice president of product management and strategy, is responsible for the execution of product strategy at Titus. His diverse background, including roles in marketing, business development, corporate strategy, applications development, and enterprise software, helps to inform his approach.

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