If you’re thinking about licensing a DLP product, or are questioning whether or not DLP is worth it, then this blog is for you. Find out how DLP works, why it’s essential, and what separates the leaders from the laggards when it comes to DLP solutions.
What do all these acronyms mean?
Before we dive into the why or the how, let’s first clear up the what. The title of this blog has three pretty important acronyms. Here’s what they mean:
AI aka Artificial Intelligence: The ability of a computer program or machine to think like a human through self-learning algorithms, enabling speech recognition, problem-solving, learning and planning.
ML aka Machine Learning: A subset of AI where software applications self-learn and become better at predicting outcomes without human intervention
DLP aka Data Loss Prevention: A technology-based strategy for ensuring compliance and protecting sensitive data. It works by analyzing, inspecting and encrypting data both at rest and in motion. Whether it is being sent or received through messaging applications, downloaded to an end-user device or being stored in the cloud.
Do I really need DLP?
Across industries, DLP is thought of as a must-have to ensure data security. In fact, DLP isn’t just recommended in highly regulated sectors but required to meet compliance standards. However, not all DLP solutions are created equal. We have to remember that DLP has been around since the mid-90s. Just think how much the workplace has changed since then.
The pace of change means that DLP sometimes gets a bad rep for being an antiquated legacy technology. We agree that the old school of DLP, which is focused on redacting sensitive data from emails, doesn’t keep companies secure in the cloud-first, Zoom-and-Teams-led world. This is because it tends to rely on traditional describing and fingerprinting technologies to identify data. These solutions simply aren’t effective at scanning unstructured data, which makes up the bulk of the enterprise workflow.
However, just as the workplace has evolved, many DLP vendors have too – and there are now new challenges (like us!) in the space who have designed DLP solutions that overcome the challenges of protecting data in the hybrid world.
Just think of the amount of unstructured data that your organization produces: sensitive data, trade secrets, and PII could be in any number of spreadsheets, Slack chats and word documents across a vast number of devices in different locations. Finding, classifying and securing this data is essential to compliance and preventing a data breach. It’s from this need that next-generation, intelligent – as in, AI-enabled – DLP has arisen. Let’s dive into how it works below.
How does AI and ML-powered DLP work?
DLP infused with AI and ML is better and faster at finding business-critical data than legacy solutions. Because this DLP is also self-learning, it needs much less intervention from IT Teams, freeing up their time so that they can focus on more high-value tasks rather than constantly responding to false alarms raised by their DLP solution.
With ML, your DLP solution is able to automatically find and secure sensitive data, like customer PII or PHI, across your cloud applications, APIs and broader infrastructure. At first, you’ll set up the DLP solution with a few rules, so that it knows what to look for and how to respond. From there, the machine learning element of the DLP enables the solution to learn and interrogate new data, leading to the automatic redaction of sensitive data. Moreover, machine learning can also be applied to user behavior, enabling your DLP solution to respond to risky or abnormal user behavior by redacting or even blocking users from sending certain pieces of information.
It might sound slightly futuristic, but ML-powered DLP is a reality right now. Here are some of the significant benefits of adopting it:
Give your IT team a boost
Research shows that 83% of cybersecurity professionals feel overworked. One of the major causes of burnout is having to sift through an overwhelming amount of false positives. By adding AI/ML to DLP, you can alleviate some of the burdens on your security teams. These solutions can automatically make decisions, allowing your security personnel to focus on more critical tasks and better prioritize their workload. It’s worth noting that DLP for AI is not meant to replace the security analyst’s job. It’s there to enhance their ability to respond to threats in real-time by taking on some of the more menial, time-intensive tasks with data classification and redaction.
Make DLP faster
Traditional DLP solutions can make the process of classifying and redacting data cumbersome and time-consuming. Policy-based rules often need to be updated on a weekly basis, meaning the security team is constantly on the backfoot – and data is continuously at risk. However, with AI, DLP becomes self-learning. It utilizes previous logs, rules, and pattern recognition to identify sensitive data, even when no hard and fast policy has been implemented. Moreover, in the case of the insider threat, AI-driven DLP’s user behavior analysis capabilities mean DLP can prevent a leak or breach in real-time, while also helping end-users to improve their awareness of data security.
Get a handle on unstructured data
AI and ML have a unique ability to analyze a huge amount of data at super-fast speeds – all while being just as accurate – if not more accurate – than a human being doing the same thing. In fact, for AI, the more data they can analyze, the better. More data = more learning, which makes the solution more efficient and accurate.
Securely embrace cloud applications
Keeping data secure in the age of Slack, Teams and remote work is what keeps security leaders lying awake at night – especially given the risks of a data breach. Compliance fines, downtime, reputational damage and brand equity can all be harmed by a single data security incident. The good news is that, with the right DLP solution, you can achieve sure-fire protection for your organization – even as data travels through cloud applications.
