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Data security is the group of techniques, tools, and practices used to protect digital information from unauthorized access, malicious users, and corruption. It includes every aspect from the physical security of servers and storage to access management. Protecting organizations from data loss is a critical aspect of data security.
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When online activity increased due to the COVID-19 pandemic, the challenges for data protection increased as well. Attack surfaces expanded and multiplied with more widespread remote work practices. This resulted in an increase in data breaches—ransomware in particular.
In addition to the growth and complexity of attacks, there are a number of compelling reasons to implement data security practices and tools:
Vulnerability to almost all attacks like phishing or malware depends on human action, whether intentional or unintentional. Data security technologies can provide adaptive access to restrict permissions based on role, timing, and devices, and significantly reduce the risk of attack.
The physical perimeter of business networks has expanded with hybrid work, and now every laptop and desktop is an endpoint that can be attacked. Securing access to physical workstations, devices, and endpoints ensures that even if a phone is stolen or lost, unauthorized users can’t access the data. Typical methods include multi-factor authentication and data encryption.
With both insiders and outsiders operating in office spaces, organizations should understand the risks from third-party contractors and employees who have access to data. Insider threats can be malicious or non-malicious (those that cause harm accidentally or because of negligence). For instance, one data security strategy is to eliminate access privileges upon an employee’s termination, but this requires timely attention and coordination. A comprehensive solution can automate this process, enforcing rules, roles, and responsibilities without the need for manual effort from IT.
This is one of the key aspects of security and compliance, as data breaches usually aim to extract or expose intellectual property. Regulatory requirements like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) address privacy as a priority. As the amount of collected data grows, protecting data privacy is more costly every year. Companies in possession of customer data need to invest in security technologies that enable them to protect the privacy of their records and ensure that the data can be safely recovered and restored if needed.
The increasing role of the Internet of Things (IoT) and mobile devices in work have further expanded the attack surface. Today’s security professionals need to protect data that can be accessed from anywhere. This makes it essential to monitor your network with real-time security analytics.
This is one of the major data security threats. The term refers to malware delivered by attackers that infects corporate systems, encrypting the data and demanding a ransom for the decryption key. Ransomware spreads quickly and can rapidly infect critical parts of a corporate network.
Loss of data can be accidental or intentional and result from negligence or malicious actors. One of the most common risks is data loss resulting from migrating data to the cloud. Multiple points of data access, unsecured networks, and poor sharing practices compound the risk of data loss.
There isn’t a single technique that can solve all data security problems, but there are several solutions that, when combined, can strengthen an organization’s data protection. Whichever combination of techniques you use, ensure that data is protected in motion and at rest.
One of the main risks of data security is data loss, which can result from negligence or the actions of a malicious actor. Data loss prevention (DLP) is the group of tools, practices, and processes organizations use to protect confidential data from unauthorized access, misuse, and loss.
Data loss prevention software monitors the environment to detect and identify indicators of compromise and prevent the loss of sensitive data. The software classifies the data, categorizing it by criticality to detect violations of pre-set DLP policies as part of the organization’s security policies pack. This set of rules typically originates from regulatory compliance bodies like HIPAA, PCI-DSS, or GDPR. Once the DLP identifies a violation, the software puts in practice encryption and other remediation actions.
Data loss prevention tools protect data at rest, in motion, and in use. These software tools also generate detailed reports to meet compliance and audit requirements.
Network DLP secures the perimeter around data in motion on the network. This type of solution tracks and monitors data while in transit in the organization’s network. Network DLP works well with connected devices, but it doesn’t cover laptops or remote devices connected through the public internet or otherwise away from the network.
This solution is installed on each endpoint device and monitors data in motion and at rest, even when the device is not connected to the network. Endpoint DLP provides a wider range of protection, but it requires more management since you need to install the Endpoint DLP software on each device.
This type of software enforces the security rules and policies of the DLP protocol on cloud accounts. It is often integrated with cloud tools and it doesn’t cover on-premises networks.
EDLPs are dedicated DLP solutions that can be deployed on endpoints, network, and cloud. They feature comprehensive inspection and response capabilities.
These solutions have the advantage of being integrated within other services, such as a secure web gateway (SWG) or cloud access security broker (CASB), enhancing and complementing the tools’ capabilities. An example of this native integration is Citrix Secure Internet Access.
Broadly speaking, a DLP tool works in a two-step process:
Step 1: Inspect and identify
The tool reads and understands files, analyzing the level of criticality. It uses pattern recognition to analyze the data in motion, decrypting and decompressing it if needed, looking for sensitive information.
Next, it applies rules to look for matches and perform the desired action. For instance, let’s say you want to prevent egression of customers’ credit card numbers but not prevent employees from performing online purchases. The DLP solution can use one of two methods: It can look for exact matches of credit card numbers or block the exfiltration of data from a database. Finally, at this stage, the DLP identifies a typical traffic flow and user behavior.
Step 2: Protect and alert
DLP requires applying rules pre-defined by the administrator. A DLP tool will implement general and granular rules at the user level to protect the data. For instance, it may allow only corporate credit card numbers in data output traffic.
Data loss protection solutions can minimize alert fatigue by setting alerts according to severity. For example, there may be an alert only if there are more than three credit card numbers in the egress stream. It also allows administrators to find the source or user of DLP rule violations and define the severity.
The solution will also capture and analyze files that trigger a DLP violation and block access to sensitive files.
Data loss can impact all sizes and types of businesses, and its consequences can result in disruption and reduced productivity, damaged reputation, loss of customer loyalty, and even business failure.
Here’s why implementing data loss prevention should be a high priority:
Benefits of data loss prevention solutions include:
Enhancing your data security posture requires a strong DLP strategy—one that prevents internal and external threats that aim to compromise your data integrity. That’s exactly what Citrix Secure Internet Access offers. With natively integrated data loss prevention, this solution inspects incoming and outgoing streams of data for sensitive information. It monitors social security numbers, credit card numbers, encryption keys, and more—and offers granular security controls at the user level based on role, source IP, or user group.