If your organization handles sensitive data that’s subject to regulatory compliance or other security requirements, you may be required to (or benefit from) implementing data loss prevention (DLP) measures. Many organizations rely on DLP to mitigate data breaches and optimize their data security posture. So, what is DLP in cyber security? Let’s discuss.
What is Data Loss Prevention?
Data loss prevention refers to established measures that prevent data from being compromised due to data loss, modification, or erasure. The main goal of cyber security data loss prevention is to mitigate data breaches from compromising the integrity and availability of sensitive data.
This blog will explore DLP by delving into:
- How DLP technologies work in practice
- Best practices for deploying DLP solutions
- Reasons for implementing DLP tools
When optimized and implemented with the help of data protection experts, DLP security will mitigate risks to any sensitive data you handle. Beyond keeping data safe, DLP security also tracks processes that may create data security risks. If you handle large amounts of sensitive data, DLP makes a difference by minimizing the risks of business disruption if a breach occurs.
How Does DLP Work?
Put simply, most data breaches occur because of gaps or vulnerabilities in security controls. DLP helps close these gaps by safeguarding data at rest, in use, and in transit. But to be most effective, DLP solutions should categorize data based on its sensitivity and then determine which security controls will best protect these data categories.
Based on the sensitive data categories identified and their corresponding safeguards, DLP tools will then guide the optimization of security controls to ensure data is adequately protected. Optimizing DLP security in alignment with your organization’s security policy helps minimize additional vulnerability risks that may come with new, untested controls.
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Data Loss Prevention Best Practices
Although DLP might theoretically seem simple and straightforward, it may require significant effort and optimization to get it from the testing phase to full operation. The most critical aspect of data loss prevention is ensuring all sensitive data is identified and protected at all times.
Whether you deploy DLP in-house or outsource it to a data protection specialist, the success of DLP cybersecurity deployment depends on several best practices. Some of those include:
1. Utilize Data Loss Prevention Software
Investing in data loss prevention software to meet your DLP security needs goes a long way in streamlining DLP controls. With various types of DLP software available on the market, you’ll need to identify those that automate DLP and simplify the process from start to finish. When purchasing DLP software from a vendor, verify its compatibility with your systems and security configurations. Furthermore, any DLP software you implement must align with the requirements of your organization’s security policy and any regulatory frameworks you’re operating within.
2. Identify When Data Is At Risk
One of the most effective ways to identify when data is at risk is to leverage the expertise of an expert, using robust DLP technologies to classify and categorize data based on risk thresholds.
Certain types of data may be at a higher risk of data breaches than others.
For example, data transmitted via emails is prone to social engineering attacks, whereas data stored locally or on the cloud may be susceptible to access control threats. In such instances, the threat risks will depend on the data environment. However, if data at risk is not identified promptly, there are higher chances it will be compromised during a cyberattack.
3. Employee Training
Training your employees on how to best leverage data loss prevention tools is also critical for their continued success in your security program. DLP training should not be limited to the dedicated IT teams who manage and oversee DLP cybersecurity. All employees should be considered stakeholders in DLP processes and must be expected to comply with policy-based DLP recommendations and best practices. Outsourcing employee training to a leading data protection specialist can also help minimize training gaps among your employees.
4. Classify Data
To a large extent, the effectiveness of data classification will determine how useful a DLP tool is.
Unless DLP technologies can appropriately classify data based on intrinsic risk, they will not effectively safeguard sensitive data categories. One benefit of using DLP solutions to classify at-risk data is the ability to create or define rules by which the DLP tools classify data.
For example, you can create a ruleset to classify cardholder data as a sensitive category based on a finite number of digits detected for each transaction. You can also optimize rules based on:
- File types (instead of content)
- Fingerprints of standard documents
- Select (or all) statistical analyses
However, be aware that any newly optimized DLP tools for data classification must be fully tested in a lower-stakes setting before rolling them out into an actual production environment.
5. Monitor Data
Even after your DLP system is up and running, you must make frequent checks to ensure data is being classified and protected appropriately. One way to monitor data is to check DLP metrics and observe how many events are being flagged as threat risks. Beyond evaluation, it helps to assess the integrity and availability of the data to ensure that the DLP solutions are working.
You can also leverage other types of security monitoring tools such as those conducting security information and event monitoring (SIEM) to evaluate the effectiveness of DLP solutions.
6. Identify & Classify Sensitive Data
The majority of your cybersecurity data loss prevention efforts should be focused on safeguarding sensitive, high-risk data, which is frequently targeted by cybercriminals.
Identification and classification of sensitive data may be conducted based on categories like:
- Personally identifiable information (e.g., social security numbers)
- Intellectual property (IP) data (e.g., novel experimental data)
- Proprietary financial data or trade secrets
- Data describing business relationships
Data classification will also help DLP software effectively assign data security risk rankings.
Why You Should Implement Data Loss Prevention
The most critical reason for implementing DLP is to mitigate data breaches. Depending on your industry and the type of data you handle, data breaches can have serious consequences.
You may be asked to pay a hefty fine, face legal action, or have your reputation damaged.
Implementing data loss prevention will also help mitigate disruptions in business continuity if sensitive data critical to your business operations is lost and rendered irrecoverable. DLP can also help meet the compliance requirements of regulatory standards like the EU GDPR, PCI DSS, and HIPAA. DLP will ensure that data safeguards are up to par with industry standards.
How RSI Security Can Help You Implement DLP
Going back to the initial question: what is DLP in cyber security? It comes down to finding tools or best practices that will safeguard data while it is in your hands. However, not all organizations have bandwidth to handle data security internally, hence the need for data protection services.
At RSI Security, we offer a range of data protection services:
- Monitoring compliance with frameworks such as the EU GDPR
- Providing education about data privacy risks
- Conducting data protection impact risk assessments
- 24/7 support for DPO services
- Management of data breach notifications
Partnering with RSI Security will help you implement robust DLP by securing data privacy and ensuring that data remains private and confidential at all times, meeting applicable compliance requirements of industry standards, and overseeing in-house training for data loss prevention.
Our team of experts is here to simplify enterprise data security management across your organization and help you achieve robust DLP. Contact RSI Security today to learn more!
