How to use data detection and response (DDR) to gain early warning into data leakage from insider threats

Data leakage is one of the most serious threats to any organization that handles sensitive or confidential data. Data leakage can result in financial losses, reputational damage, legal liabilities, and regulatory penalties. Data leakage can also compromise the security and privacy of customers, employees, partners, and stakeholders.

Data leakage can occur from various sources, such as hackers, malware, phishing, or external devices. However, one of the most challenging and dangerous sources of data leakage is insider threats. Insider threats are individuals or groups within an organization who have legitimate access to data or systems but misuse or compromise them for malicious or personal gain. Insider threats can be motivated by various factors, such as greed, revenge, curiosity, or negligence. Unfortunately, in some circumstances, insider threats can also be accidental or unintentional due to a lack of education about the need for security when it comes to managing sensitive data.

According to a report by Verizon, insider threats accounted for 30% of data breaches in 2020. Another report by the Ponemon Institute found that the average cost of an insider threat incident had increased from $15.4 million in 2022 to $16.2 million in 2023. Some of the most notorious examples of malicious internal data leakage caused by insiders include Edward Snowden, Chelsea Manning, and the Panama Papers.

How can organizations receive alerts from data leakage from insider threats, whether they are intentional or not? One of the most effective and innovative solutions is Data Detection and Response (DDR). DDR is a data security solution that leverages artificial intelligence, machine learning, and behavioral analytics to monitor, detect, and respond to data activity across endpoints, networks, cloud, and applications. DDR provides real-time visibility into data activity, detects anomalous or suspicious data behavior that may indicate insider threats, alerts security teams of potential data leakage or exfiltration incidents, and provides actionable insights for response.

In this article, we will explain how to use DDR to gain early warning into data leakage from insider threats and help minimize resulting damages. We will also share a case study of how Laminar Security, a leading provider of data security solutions for enterprises, provided DDR services to an NFT company to protect their sensitive data from malicious or compromised insiders.

What is data detection and response (DDR)?

Data Detection and Response (DDR) is a data security solution that aims to provide real time activity monitoring and early warning into possible breaches for data across the entire data lifecycle. 

• DDR covers all data sources, destinations, users, and behaviors.
• DDR monitors data activity across endpoints, networks, cloud, and applications using artificial intelligence, machine learning, and behavioral analytics. 
• DDR detects anomalous or suspicious data behavior that may indicate insider threats or other data security risks. 
• DDR alerts security teams of potential data leakage incidents and provides actionable insights for response. 
• DDR also provides audit trails and reports for data security posture and compliance.

DDR vs DLP,  EDR, or SIEM

DDR differs from other data security solutions such as Data Loss Prevention (DLP), Endpoint Detection and Response (EDR), and Security Information and Event Management (SIEM) in several ways. 

1. DLP focuses on preventing data loss by enforcing policies and rules on data access and transfer. However, DLP can be easily bypassed by insiders who have legitimate access to data or systems. DLP can also generate a lot of false positives and noise by blocking legitimate data activity. 
2. EDR focuses on detecting and responding to threats on endpoints such as laptops, desktops, and mobile devices. However, EDR does not provide visibility into data activity on networks, cloud, or applications (although XDR does for servers). EDR can also miss some of the subtle signs of insider threats such as abnormal file access or transfer patterns. 
3. SIEM focuses on collecting and analyzing security logs and events from various sources. However, SIEM can be overwhelmed by the volume and complexity of data generated by modern organizations. SIEM can also lack the context and intelligence to identify insider threats or other data security risks.

DDR has several advantages over other solutions for detecting and responding to insider threats. Some of the benefits of DDR are:

• It provides real-time visibility into data activity across the organization
• It detects anomalous or suspicious data behavior that may indicate insider threats
• It alerts security teams to potential data leakage incidents and provides actionable insights for response
• It reduces false positives and noise by using advanced analytics and intelligence
• It enhances data security posture and compliance by providing audit trails and reports

Implement DDR for early detection of data leakage caused by insider threats

Implementing DDR to detect data leakage from insider threats involves several key steps and best practices. Here is recommended practices on how to deploy and use DDR:

1. Define your data security objectives and scope

Before implementing DDR, you need to define your data security objectives and scope by asking the following questions: 

• What are the types of data that you want to protect? 
• Who are the users that have access to the data? What are the sources and destinations of the data? 
• What are the risks and challenges that you face in protecting the data? What are the compliance requirements that you need to meet?

These questions will help you determine the scope and goals of your DDR implementation.

2. Choose a DDR solution that suits your needs

There are various DDR solutions available in the market, each with different features and capabilities. You should choose a DDR solution that suits your needs and preferences. Some of the factors that you need to consider when choosing a DDR solution are:

• The coverage and compatibility of the solution: Does the solution cover all the data sources, destinations, users, and behaviors that you want to monitor? Does the solution integrate well with your existing data security infrastructure and tools?
• The performance and scalability of the solution: Does the solution provide real-time and continuous monitoring of data activity? Does the solution handle the volume and complexity of data generated by your organization? Does the solution scale well with your data growth and security needs?
• The intelligence and accuracy of the solution: Does the solution use artificial intelligence, machine learning, and behavioral analytics to detect anomalous or suspicious data behavior? Does the solution reduce false positives and noise by using advanced analytics and intelligence? Does the solution provide actionable insights for response?
• The usability and support of the solution: Is the solution easy to use and manage? Does the solution provide a user-friendly interface and dashboard? Does the solution provide adequate support and guidance for installation, configuration, deployment, and maintenance?

3. Configure and deploy the DDR solution 

Once you have chosen a DDR solution that suits your needs, you need to configure and deploy it. 

• Enable DDR to recieve activity logs (from cloud hosts such as AWS CloudTrail)  
• Configure the DDR policies and rules that define what data activity you want to monitor, what data behavior you want to detect, and what actions you want to take in response. 
• Configure the DDR alerts and notifications that inform you of potential data leakage incidents. 
• Test the DDR solution to ensure that it works properly and does not interfere with your normal data activity.

Use the DDR solution to monitor, detect, and respond to insider threats

Once you have configured and deployed the DDR solution, you can use it to monitor, detect, and respond to insider threats. 

• You can use the DDR dashboard to view the data activity across your organization. 
• You can use the DDR reports to analyze the data security trends and patterns. 
• You can use the DDR alerts and notifications to identify potential data leakage incidents caused by insiders. 
• You can use the DDR insights to investigate and respond to insider threats. 
• You can also use the DDR audit trails to provide evidence and accountability for data security incidents.

Tips and tricks on how to optimize DDR performance and efficiency

Review and update your DDR policies and rules regularly 

• Your data security objectives and scope may change over time due to various factors such as business growth, organizational changes, regulatory updates, or an evolution in the  threat landscape. You need to review and update your DDR policies and rules regularly to ensure that they reflect your current data security needs and preferences.

Fine-tune your DDR alerts and notifications 

• You may receive a lot of DDR alerts and notifications from various sources, destinations, users, and behaviors. You need to fine-tune your DDR alerts and notifications to ensure that they are relevant, accurate, timely, and actionable. You can do this by adjusting the thresholds, filters, priorities, frequencies, formats, channels, recipients, and escalations of your DDR alerts and notifications.

Train your security team on how to use DDR effectively 

• Your security team is responsible for using DDR effectively to detect data leakage from insider threats. You need to train your security team on how to use DDR effectively. You can do this by providing them with adequate documentation, guidance, support, feedback, incentives, recognition, and rewards for using DDR effectively.

Case study: how Laminar Security employed DDR for detecting data leakage from insider threats

In the fast-paced FinTech industry, data security is a linchpin of customer trust and business integrity. Shakepay, a Canadian bitcoin technology company, embarked on a transformative journey to safeguard its critical financial data in the cloud. With over $7 billion in digital currency and approximately 1 million users, Shakepay faced the daunting challenge of manually validating its data security posture. They needed a DDR solution that could provide comprehensive, proactive protection across its database. 

Before approaching Laminar, Shakepay’s security team was dedicating around 20 hours for each new feature development to ensure security compliance. This labor-intensive process was not sustainable, especially for a company dealing with sensitive financial data like Bitcoin and Ethereum.

Enter Laminar’s DDR solution, an agile data security platform that offers visibility and control to support cloud security, privacy, and governance initiatives. Laminar’s cloud-native Data Security Posture Management (DSPM) platform provided autonomous and continuous data discovery, classification, and protection across multi-cloud environments.

Laminar’s DDR features which benefited Shakepay included full and continuous visibility of cloud data without requiring connectors or access credentials, tailored policies to alert when unique production data was in a non-production environment, enhancing risk awareness, an API-only approach, avoiding regulatory compliance issues by not removing sensitive data, and fast and easy implementation, taking only about 30 minutes to set up and review.

The adoption of Laminar brought significant time savings and peace of mind to Shakepay. What previously took 20 hours of security validation now took merely 30 seconds. This efficiency allowed the security team to allocate resources to other critical areas. Moreover, Laminar provided Shakepay with the confidence and peace of mind regarding their DDR requirements. Carella noted that Laminar reduced their risk by making processes around data more mature and secure, allowing him to check data security off his list of concerns.

Shakepay’s case exemplifies the importance of robust data security in the FinTech sector. By partnering with Laminar, Shakepay not only enhanced its data security posture but also gained operational efficiency and peace of mind. This case study underscores the critical role of DDR in protecting sensitive financial data in the cloud, ensuring customer trust, and fostering business growth in the digital age. Read the complete case study on how the bitcoin technology company used Laminar’s DDR solution to prevent data leakage from insider threats.

Conclusion

Data leakage from insider actors is a serious threat to any organization that handles sensitive or confidential data. Data leakage can result in financial losses, reputational damage, legal liabilities, and regulatory penalties. Data leakage can also compromise the security and privacy of customers, employees, partners, and stakeholders.

Data Detection and Response (DDR) is a data security solution that leverages behavioral analytics to monitor, detect, and respond to data activity no matter where data resides or travels. DDR provides real-time visibility into data activity, detects anomalous or suspicious data behavior that may indicate insider threats, alerts security teams to potential data leakage incidents, and provides actionable insights for response.

We have shown you how to use DDR to gain early warning into data leakage from insider threats and minimize resulting risks. We have also shared a case study of how Laminar Security used DDR to protect their sensitive data from malicious or compromised insiders. Discover the latest information and insights from Laminar experts on our data security blog.