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Data Loss Prevention (DLP) is a cybersecurity solution that focuses on detecting and preventing data breaches. With its goal of blocking the exfiltration of confidential corporate data, organisations today now rely on it for their regulatory compliance and internal security.
DLP allows businesses to uncover data loss, prevent the forbidden transfer of data to the outside, and unwanted destruction of confidential data, most prominently intellectual property (IP) and personally identifiable information (PII). It also helps companies in their data security initiatives and ensures compliance with relevant data usage regulations, such as the EU General Data Protection Regulation (GDPR). The terms ‘data leakage prevention’ and ‘data loss’ are typically used interchangeably, but DLP empowers organisations to protect themselves against both. Specifically, it enables them to:
1. Identify confidential information across many cloud-based and on-premises systems.
2. Suppress accidental or intentional sharing of data.
3. Monitor and safeguard company data.
4. Educate employees on how to remain compliant.
Data breaches, i.e. incidents where protected data gets used, viewed, or stolen by unauthorised parties, are a growing threat as the world progressively becomes more digital. In the first half of 2019 alone, over 3,800 data breaches took place, which exposed more than 4.1 billion records. As such, DLP is indispensable for businesses looking to properly secure their data.
DLP safeguards data by firstly identifying what counts as sensitive information and utilising deep content analysis to discover and deter potential data leaks. This analysis entails methods such as regular expressions, keyword matches, and internal functions to identify assets matching an organisation’s DLP policy, effectively recognising, monitoring, and automatically preventing exposure or theft of protected data.
Deploying DLP starts with businesses defining the sensitive data, such as email addresses, customer financial details, Social Security numbers, and anything else they wish to protect and base their DLP policy on. A DLP policy typically includes:
1. Systems and locations where data protection is necessary.
2. When and how to protect data.
3. Rules defining sensitive data and actions to take upon detecting a security risk.
4. Conditions assigning the required actions for different risk levels.
Although largely beneficial, implementing a DLP solution alone will not suffice to keep cybercriminals at bay. Organisations must constantly monitor user activity and safeguard their confidential data while it is in use, in motion, or at rest.
Data currently undergoing access, erasure, processing, update, or a read operation by a system counts as being in use, including information stored in databases or processed by CPUs and/or RAM. An example is when users request access to their transaction history in their online banking account.
Data in transit or motion is classified as data actively moving from one system or location to another between networks, over the Internet, from a local storage device to a cloud environment, and more. Since data is often less secure during transit, it is crucial to implement robust data protection measures to keep it safe.
Data that is not in the process of moving and is currently archived in a storage device is referred to as data at rest. Despite being less vulnerable at this state, data at rest makes for a higher value target by hackers. Hence, it is essential to employ strong security measures that prevent them from gaining access to it.
Out of the many methods of detecting sensitive data, the regular expression pattern is the most commonly used in DLP. It analyses content for common patterns like nine-digit and 16-digit numbers for Social Security numbers and credit card numbers, respectively, alongside indicators such as the proximity of some keywords.
For instance, while Visa cards have 16-digit numbers, not all of them count as credit card numbers. Thus, DLP runs checksum calculations to verify if the numbers are similar to the patterns used by other brands. In addition, it searches for keywords like AMEX or VISA close to dates that may be an expiration date to determine if sensitive information is at risk.
Upon discovering a violation, DLP remediates the issue by taking actions - like pushing alerts and encrypting data - that prevent users from intentionally or accidentally sharing confidential information. Furthermore, it compiles reports that allow businesses to maintain their compliance and auditing requirements and identify areas of weakness.
Solutions like intrusion prevention systems (IPS) and security information and event management (SIEM) also provide similar functions that help detect suspicious activity and notify IT teams of potential breaches.
Cybercriminals employ many hacking methods ranging from simple to highly sophisticated. Some common types of data threats include:
1. Extrusion
Extrusion is when hackers target and attempt to steal sensitive data by penetrating an organisation’s security perimeters using techniques such as malware, code injection, and phishing.
2. Unintended exposure
Data breaches can also stem from negligent or unintended exposure that results from inadequate employee data procedures. This is when the business’s employees willingly give access to their accounts or data or simply lose confidential information. It can also be due to businesses failing to put sufficient access restrictions in place on organisational policies.
DLP’s content analysis engine lets companies identify when critical information is at risk of being shared externally so they can take immediate action by logging the event for audit, warning the employees that could unintentionally be exposing information, or actively preventing the file or email from being shared.
3. Insider threats
Insider threats are breaches that originate inside the organisation. The malicious insider could be a contractor, a former or current employee, or any other business associate with information regarding the business’ security systems and practices. This insider either abuses their permissions or gains access to a user account with higher privileges to steal data.
DLP prevents such risks with its comprehensive visibility of user activity and file transactions across an IT environment. This enables businesses to store files for as long as necessary to protect data and adhere to compliance requirements, regardless of whether an employee is no longer part of the organisation. DLP also has file recovery capabilities for recovering from accidental or malicious data loss.
A robust data loss prevention system is indispensable for any business handling large volumes of data and defending against increasingly sophisticated cyber threats. All in all, whether on-premises, in the cloud, or anywhere else, ensuring that business-critical, sensitive data remains secure is a top-level priority.
GROUP8, Singapore’s leading cyber intelligence and cybersecurity company, offers cutting-edge Data Loss Prevention solutions that guarantee your data stays safe from outsider and insider threats. Our offensive-inspired cyber defence ecosystem of cybersecurity solutions also covers web and network security, incident response, threat intelligence, vulnerability research, and CREST-certified Pen Test Services in Singapore. Contact us at hello@group8.co to learn more about our full services and other details.