Srikanth
Protecting sensitive data is more challenging than ever in a world dominated by the cloud. Traditionally implemented DLP solutions are not able to keep pace with current needs. Their inefficiency, complexity, and slowness are hampering team productivity. Artificial intelligence and machine learning-enabled DLP tools could be the best solution here. Being intuitive, cost-efficient, and purpose-specific, these tools are more effective. Seems interesting, isn’t it?
Learn how AI can prevent next-gen data loss
Learn a little about AI and DLP.
Let’s get the what straight before moving on to why or how.
Artificial Intelligence – Computers and machines using self-learning algorithms can think like humans through Artificial Intelligence. These algorithms can recognise speech, solve problems, learn, and plan.
Machine Learning: An area of artificial intelligence in which algorithms predict outcomes without human involvement.
Data Loss Prevention: A way to protect sensitive data using technology. It analyses, inspects and encrypts data both at rest and in motion.
But is data loss prevention a need of the hour?
Security is considered a must-have across industries when it comes to DLP. It’s not just recommended but required to comply with regulatory standards in highly regulated sectors. There are, however, differences between DLP solutions. It is important to remember that DLP has existed since the 1990s. Workplaces have changed dramatically since then.
DLP is sometimes referred to as an ageing technology due to its pace of change. With the traditional DLP methods, which redact sensitive email content, keeping firms secure in the cloud-first, Zoom-and-Teams-driven world is impossible. The reason is that it relies on conventional approaches to describe and identify data. Enterprise workflow relies heavily on unstructured data, which these solutions cannot scan.
While the workplace has evolved, numerous DLP vendors have changed along with it – and new players have entered the game with DLP solutions that tackle the shifting challenges of data protection in a hybrid world. Consider how much data your organization generates: PII, trade secrets, and sensitive information could be scattered across spreadsheets. We could only imagine the word documents and Slack chats on countless devices in different locations. A data breach cannot be prevented unless you find, classify, and secure this data. A need for next-generation, AI-enhanced DLP has surfaced from this need. Read on to learn how it works.
So what’s the deal with AI-powered DLP?
When infused with artificial intelligence and machine learning, DLP finds business-critical information faster and more accurately than legacy solutions. The self-learning nature of this DLP also frees up IT teams’ time, allowing them to focus on other important tasks instead of constantly answering false alarms. What else can AI prevent in the next-gen data loss? Check on!
Keeping up with amorphous data
With AI and machine learning, data can be analysed at super-fast speeds – and remain just as accurate as if done by a human. Not to forget error-free results as an outcome. Managing the scattered and unstructured data is an inherent speciality. To improve AI, they need to analyse as much data as possible. The more information they investigate, the more accurate and efficient the solution will be.
Accelerate Data Loss Prevention.
The security team often has to update policies and rules weekly, which continuously puts them on edge and their data at risk. AI, however, makes DLP self-learning. This tool identifies sensitive data by looking at previous logs, rules, and patterns, even when there aren’t any strict policies in place. Additionally, AI-driven DLP can prevent an insider threat in real-time while also increasing end-user awareness about data security with robust user behaviour analysis.
Augment your IT team with AI
Swamp is a common complaint among cybersecurity professionals. An overwhelming number of false positives contribute to burnout. You can make security teams ‘ jobs easier when adding AI/ML to DLP. Automatic decision-making helps to focus on more critical tasks. It’s important to remember that AI-powered DLP does not replace security analysts. Data classification and redaction are some of the more menial, time-intensive tasks it handles to help people respond in real-time to threats.
Take advantage of cloud computing securely.
A single data security incident can damage your reputation, brand equity, compliance fines, and downtime. Even as data travels through cloud applications, you can protect your organization’s data with the right DLP solution.
Finally, next-generation AI and machine learning can help organizations understand how their data is exposed on the deep dark web. By refining and developing models and methodologies, organizations can better detect data loss and avoid catastrophic events that could harm their business operations, finances, and reputation.
