Enterprise Data Loss Prevention (DLP)

Enterprise Data Loss Prevention (DLP) refers to a set of tools, policies, and strategies designed to prevent unauthorized access, sharing, or leakage of sensitive data within an organization. It helps enterprises protect confidential information from both accidental and malicious data breaches by monitoring, detecting, and controlling data movement across various endpoints, networks, and cloud environments.

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Cos'è la Data Loss Prevention?

DLP is any software that identifies, classifies, and allows security teams to place usage policies around sensitive data. Similar to how a firewall can filter the requests reaching a server, modern DLP solutions can assess the caliber of the device or user that is requesting access to data.

The specific policies that define which users can access what data are flexible. They can be chosen by the organization, or aligned with data protection regulations, such as:

GDPR
PCI DSS
HIPAA

Whenever a policy violation is detected, the system alerts security teams, blocks the activity, and may even implement just-in-time safeguards like encryption.

DLP data discovery allows enterprises to identify sensitive information across its various states.

Actively Used Data

When data is actively in use by applications or users, DLP tools are able to identify it as such, and apply monitoring capabilities around its real-time usage. As data moves throughout the corporate network, DLP analyzes these transmissions to detect anomalies or unauthorized attempts to transfer sensitive information.

Data At Rest

For data at rest, DLP tools identify the databases and file repositories in question, and implement perimeter-style controls and continuous monitoring.

They also assess storage environments for vulnerabilities, mitigating risks before attackers can exploit them.

The Importance of Enterprise DLP

The average breach costs have now risen to $4.88 million. Personally identifiable information (PII), such as tax IDs, emails, and home addresses, is a prime target for cybercriminals, with nearly half of all breaches involving customer PII. Intellectual property (IP) records are similarly vulnerable, accounting for 43% of breaches.

One reason for cybercriminals’ success is the fact that safeguarding data is harder than ever.

Its widespread use means it’s stored across vastly different formats, locations, and stakeholders. Plus, different types of data require distinct handling based on sensitivity levels and compliance with privacy regulations – showcasing the importance of strategies like DLP.

The Most Common Types of DLP Solutions

Given that Data Loss Prevention relies on a program that analyzes what’s being accessed illicitly, you may be surprised to hear that there’s more than one way to achieve it.

Network-Based DLP

This form of protection is heavily perimeter-based: as one of the more traditional setups, it’s deployed on network gateways. As requests hit the network, the data that’s sent out in response is continuously monitored

Endpoint-Based DLP

Installed on individual devices such as laptops and desktops, endpoint DLP focuses on data in use.

It controls actions like copying files to external drives, printing sensitive documents, or uploading data to unapproved cloud services, thereby preventing data exfiltration at the device level

DLP nel cloud

With the increasing adoption of cloud apps and services, cloud DLP solutions protect data stored and processed in cloud environments. They monitor data across cloud storage platforms and applications, ensuring compliance with security policies and providing visibility into unauthorized cloud services.

Discovery DLP

Rather than leaving the data identification process until the last second, discovery DLP provides a softer approach, by trawling through the various databases and repositories that an enterprise uses to store its data on. It then classifies the various types of data it finds, according to its sensitivity.

This allows teams to map out where data is stored, and apply their security measures around these deposits.

5 Best Practices for Enterprise DLP Success

Once configured, DLP is highly adept at keeping tabs on where your enterprise’s data is, and who’s accessing it.

However, this data doesn’t remain static – like the databases it protects, your DLP does need to adhere to some basic best practices.

#1: Enable Layered Content Inspection

Content inspection is one of the core ways that DLP tools can detect sensitive information in the first place. By enabling this, your DLP tool is able to trawl through your databases and label types of data.

Older tools rely on manual tag selection, but newer ones offer automated detection of PII and IP.

#2: Choose Contextual Analysis

Only half of the data protection process should focus on the data itself. The other half should be taking into account who’s requesting it. Contextual analysis looks at the wider factors of data transfers – like the user’s own role within the organization, and the local time they’re requesting it.

By having this visibility, your DLP tool can let you put policies in place around who can access the data, and when.

#3: Build On Context with User Behavior Analysis

Contextual analysis only looks at the immediate surroundings of a data access request; user behavior analysis looks at the user’s history of accessed data and builds a profile of normal behavior.

This then allows a DLP tool to identify sudden changes in data accessing behaviors, which is one of the first and last signs of account compromise immediately before a data breach. For instance, if a sales account suddenly begins requesting massive swathes of customer data, a behavioral analysis engine can detect that this is highly suspicious.

(Even if it’s performed by an account with the correct access permissions.)

#4: Design Content Policies According to Risk

Content policies are the core mechanisms through which your organization can implement the controls for each type of data. Designing these content policies should begin with a statement of intent.

For instance, for a US-based enterprise that wants to:

Identify documents containing intellectual property
Prevent them from being shared with a public AI tool

This approach needs to be performed across every type of data your organization handles.

Don’t worry if you don’t know yet – the previous data discovery steps should identify all of this for you. Alongside limiting which apps can access or share sensitive data, make sure to implement policies that restrict access to specific work groups.

For instance, a new sales intern shouldn’t need access to the engineering department’s databases. The higher-risk the data, the more specific the DLP policies should be to the specific intended user.

#5: Enforce those Policies

Finally, it’s no good having policies unless they’re enforced. Your DLP tool’s first line of defense should be strict access controls. It’s why they should integrate with your identity provider, as it gives a wider variety of options for double-checking user integrity. Depending on the data’s risk level, for instance, user integrity can be checked via multi-factor authentication.

Other policy enforcement options should include just-in-time encryption, which can prevent a database from being leaked and shared if a high-risk user or API gains access. This way, it remains unusable even if taken from the enterprise.

Implementing a DLP strategy involves selecting DLP solutions that align with an organization’s specific needs, considering factors, such as:

Regulatory requirements
Data flow patterns
Risk tolerance

By combining various types of DLP solutions and employing multiple protective strategies, organizations can effectively safeguard their sensitive information against a wide range of threats.

How Advanced DLP Tooling Works

Advanced DLP takes the core components of DLP like data discovery and protection, and seeks to solve a number of pain points with older solutions.

For instance, one issue that some organizations were facing was a rapidly changing foundation of not just the data being collected, but also the type and number of employee roles that need access to it. This roughly explains any rapid-scaling startup, or pivoting organization.

Advanced DLP places a heavy focus on automation. This is achieved by extending the user behavior analytics process to include all outgoing data flows. In turn, this is used to automatically create baseline, business-specific policies. Organizations can then review and validate these policies before they are enforced by the system.

This lets an enterprise stay much more nimble, especially when managing a great deal of data.

The controls are usually deeper, too, allowing control over data transfers to social media, webmail, and file-sharing services, regardless of the web browser used. Content inspection can also be applied to outgoing instant messages and images on remote or offline devices.

Bring Advanced DLP to Your Users with Check Point Quantum

Help your users instead of hindering them. With Check Point Quantum’s end-user guidance, let them learn proper data handling practices and achieve real-time remediation without IT or security involvement. On the backend, achieve full visibility into any data type in motion, and centrally manage all policies, access controls, and events from a single console.

Take the protection one step further by integrating your DLP with Next Generation Firewall (NGFW) capabilities to achieve advanced controls. See how Quantum delivers network security, data protection, and threat prevention under one cohesive toolset with a demo today.

Check Point delivers big changes even in small packages: Infinity AI Copilot is an AI assistant that significantly accelerates security admin tasks, including DLP policy changes and control suggestions. Packaged with Quantum, Infinity XDR, and Harmony SASE, Infinity AI allows lean teams to unlock greater potential.