Your Comprehensive Guide to Data Loss Prevention
Heer Chheda
Aug 26, 2024
In 2017, Equifax, one of the largest credit reporting agencies in the US, reported a Data breach. The breach went undetected for months and exposed the personal information of 147 million consumers, nearly half the United States population.
What was the cause? A single unpatched vulnerability in the web application. This oversight allowed hackers to access names, birth dates, SSNs, and in some cases, credit card numbers, and driving license information.
Equifax experienced severe financial repercussions right away, as its stock fell 31% and the company was forced to pay a $700 million settlement to federal and state authorities, and consumers. Furthermore, the business pledged to spend $1 billion on improved data security procedures.
This incident led to changes in the executive leadership, congressional hearings, and a serious hit to the company’s credibility and reputation.
If you’re looking to:
- Understand how data loss prevention works
- What DLP safeguards you from
- Some industry best practices
I suggest you stick around…
TL;DR
Data Loss Prevention (DLP) is a strategy that detects and prevents unauthorized data access, use, and transfer. It protects sensitive information across an organization’s network, devices, and cloud services. |
Integrating a data loss prevention tool can enforce granular access controls, continuously validate user identities and device health, and monitor data access and movement in real time. |
DLP practices can significantly enhance data governance by necessitating a deep understanding of data types, location, and ownership. This knowledge facilitates the creation of clear data handling policies, responsibilities, and ensures compliance. |
What is data loss prevention?
Data loss prevention is a mixture of policies and procedures that work in concert to detect, monitor, and prevent data breaches, exfiltration, and unwanted destruction.
DLP, or data loss prevention, is a cybersecurity strategy that protects an organization’s sensitive data from unauthorized access, use, modification, disclosure, or destruction.
Essentially, DLP systems scan for sensitive information, analyze its content, ensure adherence to policies and procedures, monitor data movement, and report on usage, threats, and vulnerabilities.
Different data threats that DLPs scan for
Data leakage prevention systems scan for risks, threats, weaknesses, or vulnerabilities. These systems use a variety of strategies to recognise and reduce possible threats, guaranteeing that data is safe in a variety of settings and situations.
Ten main areas are addressed by DLP systems:
- Intellectual property theft: Protecting proprietary information, trade secrets, or ideas from unauthorized access.
- Insider threats: Keeping an eye out for and preventing data breaches brought on by workers, subcontractors, or other people with inside access.
- Unauthorized user access: Detecting and blocking attempts to access sensitive data by users without proper permissions or credentials.
- Malicious insider actions: Recognising and preventing deliberate data theft or sabotage by compromised workers.
- Accidental data exposure: Preventing sensitive information from being accidentally shared or made public due to negligence or human mistake.
- Cyber attacks: Protection against external threats like phishing, malware, or any hacking attempts.
- Third-party breaches: Ensuring data security when it’s being shared with third party vendors or partners.
- Physical theft or loss: Keeping data on tangible devices—such as computers, cellphones, or removable storage—safe from loss or theft.
- Improper data disposal: Making sure that private data is safely erased or destroyed when it’s no longer required.
- Cloud misconfiguration: Preventing data leaks due to improper cloud configurations.
- Regulatory compliance adherence: Ensuring that data handling procedures meet regulatory compliance standards.
- Business policies: Enforcing security and internal data handling guidelines to ensure consistent protection throughout the company.
To reduce each of these types of data loss, a combination of policy measures, personnel training, and technical controls (such as strong access controls and access limitations) is recommended.
Elements of data loss prevention: How they work in concert to strengthen your security
By seamlessly integrating these elements, you can create a comprehensive security framework that proactively identifies, monitors, and mitigates data loss risks.
Data discovery
Data discovery is the basic component of an effective DLP program. This process includes identifying, locating, and cataloging all data points and sensitive information across your organization’s IT infrastructure, including on premise systems, endpoint devices, and cloud servers.
Data discovery aims to create a comprehensive map of where your sensitive data resides. It also accesses the data flow to understand how the data moves through the organization and determines ownership of various data sets.
Breaking down data silos requires coordinated efforts, from technical integration and organizational alignment to collaborative processes that include employees.
- Automated scanning tools: These tools can quickly comb through vast amounts of data from several systems and find patterns corresponding to pre-specified sensitive data types. They may also find lost or undiscovered data archives.
- These tools typically scan for personal identifiable information, protected health information, payment card information, and IP.
- The tools scan structured databases as well as unstructured data stores like file shares, email servers, cloud storage, and backups.
- The goal of the scans is to identify sensitive information that may be scored in unauthorized locations or data that might not be cataloged.
- Content analysis: Advanced DLP solutions go beyond simple pattern matching to comprehend the type and sensitivity of information by analyzing the context and content of data using content-aware technology.
- Metadata analysis: Analyzing file attributes and metadata can reveal information about how and when data is created, modified, and used.
- User interviews and surveys: By talking to process and data owners, you can learn details about how data is used and stored that may not come to light automatically.
Issues can arise when your data is unstructured, leading to difficulty in classification and addressing the same. These are generally referred to as “dark data”, and they are stored and collected by the organization by rarely analyzed or used.
Content analysis
Content analysis is the intelligence engine that analyzes and interprets data to find sensitive information and possible security threats. This complex process uses sophisticated algorithms to decode data’s context, meaning, and significance as it is stored, used, and communicated inside an organization. It goes beyond simple pattern matching.
Analyzing content involves these steps:
- Deep Inspection: Looking beyond metadata or file types to examine the actual content of emails, files, database entries, and network traffic. While content analysis and deep inspection sound similar, the extent to which a deep inspection takes place is quite thorough.
- Consider the following scenario: an employee wants to email the “Q2 Report” spreadsheet to a third party. Metadata-based scans would only identify the file name and kind. A deep inspection, on the other hand, would open the file and search its contents to see if any sensitive information—like financial data, client PII, or intellectual property—is present.
- Pattern recognition is the process of locating particular patterns that point to private and sensitive data such as social security numbers, credit card numbers, or proprietary code structures.
- Contextual analysis: Recognizing the environment in which information emerges might help detect sensitive material more effectively and minimize false positives. False positives refers to a situation where a scanning system incorrectly flags sensitive data when it’s not.
- Natural language processing, or NLP, is the process of interpreting textual meaning and purpose using linguistic algorithms. It is useful for locating sensitive information inside unstructured data.
- Machine learning: Using AI methods to gradually increase detection accuracy and adjust to novel patterns in data.
Content analysis engines are configured according to the company’s security policies. A security policy defines sensitive information, how it should be handled, and what corrective measures should be taken when potential violations occur.
Policy engine
The policy engine is a component of a DLP strategy that serves as the core of decision-making and governs how an organization is protected. The policy engine is essentially in charge of:
- Establishing clear and specific data access, utilization, and transmission criteria.
- Consistent application of defined policies across all organizational data endpoints.
- Triggering appropriate actions or alerts upon detecting potential policy breaches.
- Making nuanced decisions based on user roles, data sensitivity, and contextual circumstances.
One of a well-developed policy engine’s main advantages is its capacity to offer precise control. Depending on the user’s location, the time of day, or the device being used, it can apply multiple criteria to the same piece of data. Without sacrificing general security, it permits the development of exception rules for particular users or business processes.
It also guarantees that data handling procedures comply with all applicable laws (e.g., GDPR, HIPAA) and provides an auditable trail of policy enforcement actions for compliance reporting.
Monitors and alerts
Monitors and alarms are key components of the Data Loss Prevention (DLP) strategy. They serve as watchful sentinels that constantly guard an organization’s data environment.
Monitors and alerts provide real-time visibility into data movement and potential policy violations by leveraging advanced security controls and integrating with various security solutions.
DLP’s monitoring component is an ongoing data surveillance process throughout an organization’s IT infrastructure’s numerous touchpoints. Complex monitoring technologies can track who has access to what sensitive data, recognize odd data transfer patterns, and spot attempts to circumvent access controls.
Monitors can be set up, for example, to alert users to any efforts to duplicate, move, or alter proprietary material outside of approved intellectual property workflows. With this level of fine-grained monitoring, businesses can keep a thorough audit trail of how their most precious data assets are accessed and utilized.
Alerts, on the other hand, act as a means of prompt action when possible data loss is identified. Monitors can set up automated reaction mechanisms or notify relevant individuals when they spot actions that go against established security regulations or show questionable tendencies.
Reporting
Reporting is a vital element of DLP that provides crucial insights into the effectiveness of security controls, access restrictions, and the overall measures taken to strengthen security. Reports can highlight malicious intent attempts to gain unauthorized access to intellectual property.
Reporting is also crucial in demonstrating regulatory compliance to auditors and stakeholders. Comprehensive reports can demonstrate how the company complies with data protection laws including:
- Payment Card Industry Data Security Standard or PCI DSS
- Health Insurance Portability and Accountability Act or HIPAA
- General Data Protection Regulation or GDPR
These reports also contain statistics on policy enforcement actions, data access trends, and the efficacy of access controls. Additionally, they can demonstrate the company’s proactive commitment to data protection, frequently a crucial prerequisite in many regulatory frameworks.
Understanding the workings of a data loss prevention system.
A DLP system provides a layered defense against data breaches, unwanted or unauthorized disclosures, or insider threats. Here’s how it works.
Data identification
DLP begins by identifying and classifying sensitive data across your entire organization. It uses content engineering and analytics to scan your database, which includes repositories, file servers, cloud storage, and all endpoints. There are various techniques through which data identification can be done:
- Pattern matching: Recognizing specific patterns, i.e. data formats like credit card numbers, social security numbers, etc.
- Fingerprinting: Creating a unique fingerprint, i.e. unique identifiers for sensitive documents.
- Machine learning: Identifying content and classifying data accurately.
Enforcing policies
DLP systems apply predefined policies once your data is accurately categorized. Essentially, these policies are dos and don’ts regarding how the data should be stored, handled, and transmitted. It does so by either:
- Restricting the use and transfer of confidential files to external email addresses.
- Encrypting files that contain customer PII before allowing them to be stored on the cloud.
- Blocking the copying of source code to USB.
Monitoring and analyzing
A DLP system continuously and consistently monitors data movements across channels and sources connected to your network. It:
- Monitors the data that is in transit, emails, web traffic, and file transfers.
- Tracks usage on all devices, including all the Copy/Paste actions taken in a file.
- Oversees data interactions within SaaS applications.
Real-time prevention
When a DLP detects a policy violation, it takes immediate action by:
- Blocking unauthorized data transfers or attempts to gain access.
- Automatically encrypting sensitive information before it’s stored and or transmitted.
- Alert users about policy violations that have taken place and thereafter educate them on how to handle data effectively.
Setting an incident response plan in motion
For accidents that require human remediation, a data loss prevention system will:
- Automatically generate an alert for the security team.
- Provide the teams with logs and evidence, including the members involved, what was accessed, and when.
- Offers tools and remediation plans to mitigate further damages, like wiping data from stolen or lost devices,
Data loss prevention systems don’t work in isolation. To give you a holistic view of your security, they integrate with your existing systems, such as Identity and Access Management systems, SIEM, and Cloud Access Security Brokers, for extended protection.
It also needs to be noted that DLP systems are not a one-size-fits-all solution. Different industries, environments, or scenarios require tailored approaches.
Understand types of data loss prevention solutions
The three different types of data loss prevention are:
- Network
- Cloud
- Endpoint.
These three form a triad of data protection, each focusing on a specific aspect of data protection. What sets them apart are their areas of coverage and implementation methods.
Here are the three types of data loss prevention in detail
1. Network DLP
Network DLP primarily monitors data in motion, inspecting network traffic to identify and control the flow of sensitive data. It is a set of technologies and procedures designed to prevent unauthorized data transmission over an organization’s network.
Here’s what network DLP covers:
- Outgoing and incoming email communications.
- Inspecting HTTP and HTTPS traffic for unauthorized data transfers.
- Monitoring FTP and other file transfer protocols.
- Monitoring data shared over through chat applications.
- Analyzing various network protocols.
When a user wants to send out an email or a message with sensitive attachments, nDLP devices inspect the traffic and take appropriate actions based on predefined policies, such as:
- Block
- Quarantine
- Audit
- Forward
- Encryption
While nDLP offers faster deployment compared to other DLP types, its visibility is limited to data within the corporate network. It cannot monitor data on offline or connected devices via VPN.
2. Cloud DLP
CDLP is a combination of tools and processes to protect sensitive data stored, processed, or transmitted through cloud environments. It extends the traditional DLP services to cloud based services and applications.
Cloud DLP covers:
- Data is cloud applications, eg: Salesforce, Office365, G Suite
- Protects data in cloud storages, eg: dropbox, Google Drive
- Secures data in IaaS and PaaS environments, eg: Azure, AWS.
While cDLP scales easily and offers immense flexibility, managing different cloud providers can also be difficult. To manage this, you can utilize CASB solutions that support multiple cloud providers, develop a core set of standardized practices across different cloud environments, or implement a unified DLP management console that integrates with multiple cloud providers.
3. Endpoint DLP
eDLP works on securing data at the device level including laptops, mobile devices, desktops, and servers. It controls data use, access, storage, and transmission on these endpoints.
So eDLP covers:
- Data storage in rest on endpoint devices
- Monitors data in use by applications.
- Controls data transfers to external devices or over networks.
EDLP can work offline since the software is installed on each endpoint device, enabling it to provide protection even when devices are not connected to the corporate network. This is especially necessary when a large number of your employees are working from home.
Which of the three are you picking?
I’d suggest you take the three-pronged approach, as it is crucial to ensure that sensitive information is protected across all potential vectors of loss or exposure. By stacking them together, you can create a seamless security fabric that covers data at rest, in motion, and in use, addressing the modern complexities of IT, especially with the rise of WFH policies, BYOD, and multi-cloud environments.
Pair these types with the industry’s best practices to maximize your DLP program’s effectiveness.
Leveraging DLP best practices to craft an effective data loss prevention program.
Through the integration of appropriate technology and tested best practices, enterprises can establish a flexible defense against data loss that adjusts to changing threats and demands.
1. Risk based DLP
Implementing a risk-based DLP is a great approach to data loss prevention. This technique realizes that not all users or data interactions pose the same risk to an organization. This method involves tailoring the data loss protection policies based on a nuanced understanding of user risk profiles, allowing for more effective and efficient data protection.
Here’s how you can go about it:
- Create a detailed user risk profile based on various factors, such as the employee’s designation, access to sensitive data, track record of adhering to company policies, and completion of any awareness programs conducted. Additionally, you can consider factors like previous security incidents, the employee’s tenure, and their level of access to sensitive data.
- Once you have profiled employees, segment them into high, low, and medium-risk profiles and tailor policies accordingly. This might include mandatory encryption for all outgoing emails, needing additional authentication for certain data transfers, etc. For people on the lower side of the risk profile, you can add more flexibility or reduce the frequency of their data scans.
- Ensure you integrate the DLP system with the IAM tools to leverage these user information and guardrails. You can even utilize user entity and behavior analytics tools to continuously assess and update the risk profiles.
This method allows for a dynamic and responsive DLP strategy. It enables the efficient use of resources and ensures that the DLP policies remain updated to the ever-changing risk landscape.
You must ensure you walk a fine line between overly lax and complex.
2. Continuous refinement of DLP solutions
This approach recognizes that data loss prevention solutions are not “set it and forget it” but a dynamic system that requires regular attention and adjustment to remain relevant. The initial DLP solutions will always need fine tuning, once deployed.
This is especially important, as DLP systems can flood your system with false positives because the systems have not been updated.
Here are three key checkpoints to ensure that you are on the right track:
- Regular updates for DLP software and engines are needed to ensure that they can detect and protect against the latest threats and data exfiltration techniques.
- Periodic testing of data leakage protection controls using simulated data loss scenarios.
- Implement change management policies to ensure that updates are properly vetted, documented, and communicated to the relevant stakeholders.
3. Data discovery and classification.
Effective data protection policies are built on this method, which guarantees that organizations have a thorough grasp of their data landscape and can deploy suitable security measures according to the worth and sensitivity of various data types.
Comprehensive data discovery across all environments is the first step in the process. This covers endpoint devices, cloud services, and on-premises systems. The objective is to compile a comprehensive inventory of all the data types, locations, and users. In today’s complex IT ecosystems, where data can be dispersed across multiple platforms and storage locations, this all-encompassing strategy is essential.
The future of data loss protection
DLP is no longer a security tool. It is a key component of business continuity strategy that integrates with your organization’s overall data protection and solutions.
These three things will characterize the future of DLP:
- Artificial intelligence
- Adaptiveness
- A comprehensive approach for complete visibility
In the coming years, we can expect data leakage prevention techniques to be proactive prediction-led, leveraging AI and ML to anticipate potential data loss. To ensure you’re staying on top of these, you must foster a culture of continuous improvement and awareness among employees and management.
Forward-thinking companies will see DLP as an essential component of their whole business plan, not just a stand-alone security tool that lets them act quickly to protect themselves when necessary without sacrificing productivity.
Since data is still a valuable asset, having comprehensive DLP capabilities is crucial for resilience, meeting ever-evolving compliance requirements, staying ahead of sophisticated cyber threats, and long-term success in the corporate sector. Being able to safeguard data effectively will become a major differentiator.
Implement strong DLP controls with Sprinto
Sprinto is crucial in establishing and helping you maintain the foundational elements necessary for effective data protection. As a GRC tool, Sprinto enables you to develop robust policies, implement and map essential internal access controls, and educate employees on best practices for preventing data loss and business disruption. This approach is fundamental to any DLP strategy as it addresses the policies and procedures aspect of data protection.
Sprinto streamlines and manages the process of setting up strong data security compliance programs. The platform assists in creating, implementing, and monitoring policies that align with various regulatory compliance frameworks and industry best practices.
Ready to take the first step? Choose Sprinto.
FAQs
1. What is the best way to prevent data loss?
The best way to prevent data loss is to implement a thorough data loss prevention (DLP) strategy. This entails routinely backing up your data to redundant, secure storage methods, including external hard drives or cloud storage.
To defend against malware and online dangers, it’s also essential to use dependable antivirus software and to keep your operating system and applications up to date. One way to further lower the risk of data loss due to malicious or human error is to train personnel on best practices and establish clear procedures for managing data.
2. What are DLP rules?
DLP rules are particular standards or requirements that are specified in a DLP system in order to recognise, track, and safeguard sensitive data. These regulations can be tailored to meet the unique security needs of an organization and are based on preset policies.
DLP rules lower the risk of data breaches, automate the process of locating and protecting sensitive data, and guarantee that data protection laws are followed.
3. What is the DLP guideline?
The DLP guidelines address several data protection-related topics, including employee training, incident response, data encryption, access controls, and data classification. By following DLP principles, organizations can create a robust security framework that reduces the risk of data loss and allows them to react swiftly and efficiently to data-related occurrences.
4. What is DLP used for?
DLP prevents unwanted access, misuse, and exfiltration of sensitive data. It aids businesses in protecting vital assets, including employee records, financial data, intellectual property, and customer information.
DLP solutions monitor and control data at rest (data kept on devices or servers), data in use (data accessed or processed by users), and data in motion (data sent across networks).
DLP also ensures that organizations adhere to guidelines and data protection rules and avoid data breaches.