7 Step DLP Security Checklist and Best Practices in 2024

TL;DR

In today's digital landscape, where cyber threats and data breaches are rampant, prioritizing data security has become imperative for businesses of all sizes.
Remarkably, approximately 95% of data breaches result from human error, underscoring the critical nature of comprehending data flow.
Data loss prevention security best practices stress the importance of fine-tuning your DLP solution to address the specific nuances of your data flow.
Offer explicit guidelines for data handling, sharing, and security protocols, and equip employees to identify phishing attempts, social engineering tactics, and common cyberattack vectors.
Monitor and Respond using Data Loss Prevention Best Practices Continuous monitoring forms the backbone of an effective DLP audit.
Establish alerts to detect potential breaches and create incident response procedures.

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Data Loss Prevention Security Checklist

In today's digital landscape, where cyber threats and data breaches are rampant, prioritizing data security has become imperative for businesses of all sizes. The question no longer revolves around whether a data breach will take place but rather when it will happen.

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As cyberattacks surged by 67% over the past five years, organizations across industries are recognizing the urgent need to fortify their Data Loss Prevention (DLP) strategies. Here is a complete DLP audit checklist and best practices:

Step 1: Understand which SaaS and Cloud apps deal with sensitive data

The foundation of a robust DLP strategy lies in conducting an all-encompassing assessment of your organization's vulnerabilities and data flow.

Data loss prevention security best practices dictate that understanding how data moves within your organization is key. Approximately 95% of data breaches result from human error, underscoring the critical nature of comprehending data flow. However, by meticulously mapping data flow, i.e. which SaaS and Cloud apps accept or touch sensitive data, security analysts can understand the risk exposure of those systems.

Step 2: Define and set up data loss policies

Once you thoroughly understand the data flow and SaaS/Cloud apps, the next crucial step is to define data loss policies.

Highlighting the financial impact of inadequate data loss prevention strategy, statistics reveal that a data breach in the United States costs an average of $9.44 million. Hence, establishing protocols for data sharing, both within the organization and externally, and setting up guidelines for encryption, redaction, and data retention is necessary.

Effective DLP policies and compliances such as HIPAA necessitate outlining the criteria for sensitive data, determining access permissions, and specifying the circumstances under which data can be accessed.

Table showing HIPPA PHI Data Elements — HIPAA PHI Data elements

DLP solutions like Strac allow you to,

Identify and protect sensitive data like PII and PHI by redaction/masking
Set up DLP policies compliant with PCI, SOC 2, HIPAA, GDPR, NIST CSF and NIST 800-53.
Detect DLP policy violations

Graphs showing Sensitive Data Distribution By Data Elements

Read more about sensitive data elements here ➡️Strac Catalog of Sensitive Data Elements.

Step 3: Implement technology solutions in line with data loss prevention best practices

Enhancing your DLP audit requires the integration of advanced technology solutions in accordance with data loss prevention best practices. In one of the recent events, Strac’s executives shared how approximately 68% of breaches take months or even longer to detect, highlighting the significance of automated detection and prevention tools.

Seek out data loss prevention software that seamlessly integrates with existing systems, ensuring efficient and accurate automated detection and redaction of sensitive data across diverse communication channels, including email, messaging apps, and cloud storage platforms.

Step 4: Crafting a system to recognize and categorize data

Once you've determined which data needs to be protected, the following step is to design a system to detect and sort this data. This entails developing ways for identifying various forms of data, including structured data such as databases, unstructured data such as text files, presentations, conversations, videos, and photos, and semi-structured data such as emails and spreadsheets.

Table showing Sensitive Data Classification and Tokenization — Sensitive Data Classification and Tokenization

The capacity to pinpoint sensitive data is at the heart of this technology. Older methods to accomplish this would be via regular expressions, using special rules, spotting specific words, etc. Unfortunately, those methods don’t work as there is no structure in binary documents like images, pdfs, screenshots, word docs, and also in chat messages.

Strac’s advanced machine learning models are trained on millions of PII, PHI, PCI and Custom data elements, documents, and chat messages. Strac can automatically detect and redact sensitive text in unstructured documents and messages with a very high accuracy rate.

Step 5: Educate and train employees to follow DLP policies

Incorporating your workforce as active participants in your DLP strategy is paramount for success. Astonishingly, phishing attacks contribute to 36% of security breaches, emphasizing the crucial role of employee education and training. Rigorous training sessions are essential to impart knowledge about data security and foster a sense of responsibility among your employees. Offer explicit guidelines for data handling, sharing, and security protocols, and equip employees to identify phishing attempts, social engineering tactics, and common cyberattack vectors.

Step 6: Monitor and respond to data activities

Continuous monitoring and swift response to activities are data loss prevention security best practices. They form the backbone of an effective DLP strategy.

On average, identifying and containing a breach takes a staggering 280 days, reinforcing the need for real-time monitoring and swift response mechanisms. So, implement robust monitoring mechanisms that offer real-time insights into data activities throughout your organization.

Establish alerts to detect potential breaches and create incident response procedures. Designate a dedicated team to investigate and promptly respond to any unusual activities. Regularly scrutinize audit logs and conduct post-incident analyses to refine and optimize your DLP strategy.

Step 7: Regular evaluation and improvement

The concluding stage of your data loss prevention security checklist revolves around perpetual evaluation and enhancement. Regularly evaluate the effectiveness of your DLP strategy against predetermined metrics and objectives. Analyze incident reports, breach attempts, and the overall success of your response mechanisms. Identify areas that demand improvement and adapt your strategy accordingly, cultivating a continuous enhancement culture.

Strac DLP for SaaS, Cloud and Endpoint

A comprehensive DLP security checklist is all you need to protect sensitive information and preserve customer trust. However, by adhering to the data loss prevention security checklist and best practices, you can construct a robust defense against data loss.

The pursuit of data protection is an ongoing endeavor. Your dedication to upholding the integrity of your data will not only shield your business but also nurture a culture of heightened security awareness among employees and stakeholders.

In this DLP journey, Strac can be one of the most powerful DLP tools. Here’s what Strac can do for you⬇️

SaaS DLP: Automatically detect and redact sensitive data across communication channels like email, Slack, Zendesk, Google Drive, One Drive, Intercom, and more.

Protect sensitive data on front-end apps and back-end servers.
Integrate with most SaaS apps, including Box, Salesforce, ChatGPT, and more.

Discover & Protect Data on SaaS, Cloud, Generative AI

Strac provides end-to-end data loss prevention for all SaaS and Cloud apps. Integrate in under 10 minutes and experience the benefits of live DLP scanning, live redaction, and a fortified SaaS environment.