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Blog 5 min read

Establish an Efficient DLP Policy With These 5 Best Practices

by Emily Heaslip Published Oct 03, 2022

Establish an Efficient DLP Policy With These 5 Best Practices

Strong data loss prevention requires two things: a strong policy that guides user actions and permissions, and the tools to monitor and manage data security. Many organizations know they need to invest in software, platforms, and other security settings to create secure networks, endpoints, and cloud settings. But not every organization has a strong DLP policy to guide these tools. 

Many compliance regimes require companies to record data loss prevention policies. In this guide, we’ll define what goes into a DLP policy and share some best practices to make sure your DLP policy serves your company — and isn’t just another document that gets lost in the shuffle. 

What is a DLP policy?

There are two definitions for a data loss prevention policy. One type of DLP policy is organizational; the other is technical. 

The organizational DLP policy

The first definition covers the non-technical version of a DLP policy. This refers to the rules and guidelines that members of your organization follow to maintain security. This type of data loss prevention policy sets forth the tools and practices you put into place to protect your data. Broadly, an organizational DLP policy has three main goals:

  1. Protect personally-identifying information (PII) and help organizations stay compliant with regulations such as HIPAA, GDPR, and the new CCPA. 
  2. Protect your company’s intellectual property and trade secrets that could give your competitors an advantage. 
  3. Provide data visibility: help companies to understand where data lives and how it moves to ensure all systems are secure from other threats (such as ransomware or malware). 

Your DLP policy should address these specific goals. It will define what content needs to be protected based on your specific industry, compliance regime, and the type of data you collect. It will also provide data visibility, mapping where data is used, stored, and in motion. And your DLP policy should define the parameters and conditions for accessing data. 

[Read more: 5 Identity and Access Management Best Practices

Many companies include business continuity in their DLP policies. It’s useful to map out a worst-case scenario: what actions will be taken, and by whom, when suspicious activity is detected?

Implementing your DLP policy

A good DLP policy must be combined with a good DLP solution, ideally one that lets you easily implement your policies as written. Nightfall, for example, lets you create detection rules that scan for specific types of sensitive data in the locations you specify. This can be specific Slack channels, including private ones, or within specific folders in Google Drive. Nightfall essentially allows users to create templates for their most common workflows by unifying a set of Detection Rules with the actions to be taken when those rules are triggered, including automated actions such as redaction of findings and alerting through webhooks. Ultimately, a good DLP solution will allow you to create rules that determine how user identities can and should be accessing, sharing, and using data in your environments.

How to write an efficient DLP policy

When creating an organizational DLP policy, it’s important to strike a balance between comprehensive information and a usable plan. Too much information can mean that employees don’t read or follow your DLP policy. It also makes it difficult to update the policy regularly as the work environment changes.

Try to make your DLP policy as actionable, concise, and comprehensive as possible. This is a living document that should be regularly updated and used by your company during the course of business. Here are some best practices to make sure your DLP policy covers everything you need, and nothing you don’t. 

Meet your compliance standards

A DLP policy is typically required by compliance regimes, including SOX, HIPAA, and PCI DSS. Each of these standards outlines how your organization should safeguard personally identifiable information (PII), protected health information (PHI), and other sensitive data. Start with the compliance standards that apply to your specific industry to build a DLP policy that keeps your company from penalties and fines. 

Identify intellectual property

Intellectual property theft costs the US economy as much as $600 billion each year. Trade secrets, organizational strategies, and customer lists are popular targets for data thieves. The loss of proprietary data can easily put your business at risk of failure. As a result, your DLP policy should first consider how to protect intellectual property. Identify the critical information your business owns and prioritize protecting that data. 

Improve data visibility

You can’t protect what you can’t see. Make sure you have a clear understanding of where your data is stored, in use, and in motion. Data loss prevention is predicated on knowing where your data is and how it moves through the organization. High data visibility enables your IT team to implement IAM, DLP, and other security controls that make sure your data is used and stored securely. 

Specify user roles

Use your DLP policy to define key levels of access. Determine who and what should be able to access certain types of sensitive information. You may have a separate, more detailed IAM plan that evolves as your company grows, and you work with different vendors. But core roles — such as the network administrator — should be defined in the DLP policy. 

Likewise, you can use a technical DLP policy to decide how the DLP tool will enforce the policy once a violation occurs based on the data or type of identity. Read more in our guide for developers: Welcome to the Nightfall API

Identify tools to secure your perimeter

Your organizational DLP policy should outline the layered approach you will take to maintain data loss prevention, including your endpoint, network, and cloud DLP tools. This section ensures complete coverage of your devices, cloud platforms, and communications into and outside the organization. Going through this exercise enables your IT team to spot any vulnerable access points. It also helps non-IT people get a clear picture of how IT investments are working to protect sensitive information.

Enforce your DLP policy

A DLP policy is a document meant to define processes like training, monitoring, and proactive management that makes data loss prevention possible. Users should regularly be kept up to date on emerging threats to data. Provide training to employees to reduce the risk of insider threat. Make sure to regularly scan for shadow IT and educate your coworkers about the risks of using unapproved programs during remote work.

A tool like Nightfall can take some of the manual effort out of data loss prevention and help your company meet its compliance regulations. Nightfall is the industry’s first cloud-native DLP platform that integrates directly via API – meaning that customers are typically up and running within a few minutes. For SaaS apps like Slack, Confluence, and GitHub, there’s no additional configuration or setup required beyond installation.

Once Nightfall is installed, it leverages machine learning to scan both structured and unstructured data and its surrounding context with high levels of accuracy. This takes the burden off IT and security teams to constantly monitor and manually look for policy violations. IT teams can use Nightfall to create automatic workflows that take action on sensitive data proactively, reducing the time spent manually responding to alerts and reducing mean time to resolution.

Nightfall’s classification is automatic and highly accurate, eliminating the time spent tagging data manually, and reducing time spent reviewing false positives and grappling with alert fatigue. It makes implementing your DLP policy an efficient, painless process.

Learn more about Nightfall by scheduling a demo at the link below.

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About Nightfall

Nightfall is the industry’s first cloud-native DLP platform that discovers, classifies, and protects data via machine learning. Nightfall is designed to work with popular SaaS applications like Slack, Google Drive, GitHub, Confluence, Jira, and many more via our Developer Platform. You can schedule a demo with us below to see the Nightfall platform in action.

 

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