Data Loss Prevention (DLP) protects organizations from their own databases. Thanks to increasingly rabid attempts to infiltrate sensitive customer databases maintained by organizations, data breach expenses are skyrocketing to unprecedented, 3-million-dollar levels. DLP encompasses a broad range of solutions that prevent data from moving beyond the confines of a responsible organization. While traditional protection once relied on piecemeal components – each specializing in disparate sections of information protection – LayerX is shifting the industry towards a new form of cohesive defense.
In order to track how sensitive databases are being used, DLP needs oversight into the incoming and outgoing streams of data that flow throughout your organization’s networks. To start building a contextual understanding of safe vs risky use, organizations must then define employees’ devices, accounts and applications. The final contour of the data landscape mapped out by DLP is the sensitivity of each data piece.
The primary emphasis of most DLP is on obstructing malicious actions; the identification of each piece of sensitive data is the first step toward reliable protection. As such, DLP provides a layer for company-wide policies to be implemented across hundreds or thousands of data-hungry endpoints. For example, one basic form of data protection is to prevent sensitive data from being emailed outside of corporate domains. Upon discovering that the recipient is an outsider, this attempted email is identified as a violation of such policy.
While this form of DLP has firmly established itself, the rise of hybrid and WFH environments has shattered the traditional perimeter that traditional DLP architecture relies on.
Data leaks are an inevitable result of oversights as data flows throughout complex, sprawling networks. There are three major causes of data leaks. Insider threats refer to situations where an authorized account has abused legitimate permissions in order to leak data. This can be an employee that’s actively trying to harm the organization; or a threat actor that’s gained access to their account.
Extrusion, on the other hand, involves cyber attacks that specifically target sensitive data. Attackers leverage pre-existing weaknesses in database code and credential implementation, allowing them unrestrained access to sensitive info. The final – and most common – form of data breach occurs via negligence. Unintentional data exposure is caused by employees who absentmindedly input sensitive info into any unencrypted or publicly accessible form – from sticky notes to ChatGPT.
Even when completely accidental, the implications of a data breach are hugely public, often leading to hefty fines and criminal penalties.
In 2017, one of credit agency Equifax’s databases was compromised. An overlooked patch installation allowed attackers to break in and steal the personal and financial information of almost 150 million individuals. For failing to promptly address the vulnerability – as well as hesitating to publicly disclose the breach for several weeks – Equifax was fined $575 million.
This story has been echoed throughout the years; top Equifax executives resigned following this breach, just as high-ranking management at Target did in 2013. Here, at the height of holiday shopping, the megacorp had exposed the details of over 40 million credit cards. CEO Gregg Steinhafel and his 35 years of company experience were swiftly let go.
Alongside job losses and fines, the final kick that data loss delivers is the loss of customer and public faith. A 2019 report by the National Cyber Security Alliance, based on a survey of 1,006 small businesses employing up to 500 individuals, revealed the aftermath of a data breach. 10% of surveyed businesses had to shut down operations entirely; 25% were forced to file for bankruptcy; and 37% suffered significant financial losses.
These statistics underscore the importance of robust data loss prevention techniques.
Given the sheer range of sensitive data your organization is expected to protect – and the various apps, users, and browsers that are constantly negotiating your networks – data loss prevention must span a wide variety of activities.
You can’t protect what you can’t see. Automated data discovery focuses on establishing what data you’ve got – and where it’s stored. With this foundation established, your organization can begin dropping more contextual protection in place.
Protect data in motion
Innovation and iteration require data to be flowing throughout an organization almost continuously. Malicious breaches will often attempt to exploit the highly fluid nature of organizational data. This could take the form of routing in-transit data to attacker-controller servers. DLP prevents this by comparing its intended destination against where it’s been requested.
Protect data at rest
The databases that hold such treasure troves of info are equally valued by malicious actors. It’s just as important to place strong protective measures against forced entry. Whether on-prem databases, applications, in cloud repositories or mobile devices, at-rest data must have a layer of protection. Traditionally this defense has been offered by a firewall, which blocks any unauthorized party from accessing this sensitive data.
Endpoint DLP looks beyond simple database protection and begins to safeguard data at user level. By seeing and controlling the transfer of information between internal parties and external threats, these solutions can prevent data from being copied, as well as encrypting information even as it travels between endpoints.
Detect Data Leaks
Working off the basis you’ve established so far, establishing a baseline for normal data activity allows for the detection of anomalies. When these abnormal behaviors are picked up on in real time, it becomes possible for security staff to stay alert and aware of possible malicious intrusion.
On-prem data protection looks vastly different to cloud architecture’s demands; hyper-collaborative microservices require a cloud-native approach. Many organizations suffer this key oversight, failing to take advantage of data safeguards for SaaS and IaaS applications.
DLP procedures can be put in place across wide swathes of the organization. Each requires oversight for their unique focus.
Personal Information Protection/Compliance
If your organization handles Personally Identifiable Information (PII), Protected Health Information (PHI), or payment card information (PCI), it is likely subject to compliance regulations like HIPAA (for PHI) and GDPR (for personal data of EU residents). These regulations necessitate the protection of sensitive customer data. DLP can play a crucial role by identifying, classifying, and labeling sensitive data, as well as monitoring activities and events associated with that data. Furthermore, DLP reporting capabilities provide the necessary details for compliance audits.
Trade secrets make the difference between steady growth and competitor undercuts. The sheer value of intellectual property demands even greater contextual understanding from modern DLP solutions. Classification must identify and cover IP secrets in any format, structured or otherwise.
Does your organization require enhanced visibility into data movement? A comprehensive enterprise DLP solution enables you to observe and track data across endpoints, networks, and the cloud. This heightened visibility empowers you to understand how individual users within your organization interact with data and make informed decisions accordingly.
Preventing data loss doesn’t have to be complex. Before you start buying and implementing complex DLP solutions, there’s five steps to take first.
#1. Conduct a Data Inventory
It’s essential for a data-focused business to have a clear understanding of the data they possess. Orchestrating a comprehensive data inventory is crucial. This process can be streamlined with help from a solution that conducts thorough scans of an organization’s data repositories.
#2. Classify All Data
Once this first layer of data inventory is in place, it’s time to establish how it needs to be classified. Across both structured and unstructured data types, this framework needs to include categories such as personally identifiable information (PII), financial data, regulatory data, and intellectual property. Classifying data enables a far deeper understanding of not only your organization’s risk, but helps tailor protection accordingly.
#3. Establish Data Handling Policies
Now that all organizational data has been classified, it’s vital to maintain this structure via handling policies. This is particularly important for regulated data or in regions with strict regulations, such as Europe’s GDPR and California’s CCPA.
#4. Implement a Centralized DLP Program
Instead of implementing multiple DLP plans across various departments and business units, DLP programs benefit from a single, centralized approach. This consolidation promotes consistency across the various forms of protection, providing a comprehensive view of the network that prevents fragmented implementation.
#5. Educate Employees
Accidents are far more common than malicious intent. Whie a shocking amount of data breaches stem from unwitting employees sending data to places it shouldn’t go, this issue is the easiest form of data loss to prevent. Employees need to be kept up to date with the dangers they face from phishing attackers, code injection, and more. They represent the strongest and most malleable form of defense on offer.
DLP Tools and Technologies
Preventing users from accidentally or maliciously sharing data that places themselves or the organization at risk may appear complex. Relatively established DLP approaches operate off individual areas of focus – where each tool plays one role within a larger stack. One of the cornerstones to many DLP strategies are Cloud Access Security Brokers (CASBs), which deliver visibility into cloud applications. Offering a comprehensive view of the accounts and apps in your tech stack, CASBs offer organization-wide policies that help retain financial, proprietary, and health data.
While a fantastic early stepping stone toward cloud visibility, CASB protection is limited. Fully sanctioned apps – those that come prepackaged with the API to support CASB governance – are adequately protected from data leaks. However, these apps are not the only SaaS types in use within a tech stack. Semi-sanctioned apps are enterprise applications without the supporting API, while unsanctioned apps remain completely outside the scope of CASB protection. The typical way around this has been blocking all calls made by unsanctioned apps and devices. However, the explosion in WFH and BYOD work styles have expanded today’s attack surface beyond the scope of simple, sanctioned applications.
Network-based tools such as forward proxies work to apply organization-wide policies across both unsanctioned and semi-sanctioned applications, but come with a severe tradeoff. Namely, a lack of visibility into user activities. As a result, access is determined in a binary do-or-die fashion, able to determine only access to a given app or a complete ban. With compliance requirements tightening with every new year, a more comprehensive, contextual approach is required.
Deployed in minutes, the LayerX browser extension places all visibility, monitoring, and governance in the browser itself rather than relying on APIs. In that manner, LayerX eliminates the difference between sanctioned, semi-sanctioned and unsanctioned SaaS apps, providing the same set of comprehensive capabilities to any app that your workforce accesses through the browser.
LayerX SaaS DLP capabilitie
- An intuitive configuration interface with built-in best practices policies that you can easily modify to your needs.
- Data protection policies that safeguard sensitive data across both sanctioned and unsanctioned apps.
- Comprehensive coverage of all actions that put your data at risk, including uploading, downloading, copying, pasting, and sensitive data exposure.