A 360-Degree Approach to Data Loss Prevention

By Sanjay Bhakta, VP & Head of Solutions
data loss prevention

Digital fraud, identity theft, and ransomware are the disruptive and sometimes catastrophic results of data loss. Data loss prevention is becoming a more important and scrutinized topic as chief information security officers, heads of fraud operations, and risk/compliance directors become more aware of inter-related threats to their businesses and seek solutions to stop those threats. In addition, a new Securities & Exchange Commission rule has created greater accountability and transparency in how businesses handle cybersecurity threats and incidents; this, in turn, has sparked more interest in DLP as a way to mitigate against cybersecurity threats.

A heightened interest from the private and public sector in securing their digital estates is a reason why the global data loss prevention market is growing at a compound annual rate of 24.1 percent. At Centific, we believe that a rigorous, 360-degree approach to data loss prevention that encompasses generative AI is the key to staying ahead of bad actors who are looking for every conceivable edge in their quest to breach corporate data and commit acts of fraud. This approach encompasses the right processes, tools, and much more. Let’s take a closer look.

Data Loss Prevention Defined

DLP is a security approach that focuses on identifying and preventing the unauthorized use, disclosure, or destruction of sensitive information. It’s like a security guard for your data, constantly watching for suspicious activity and stopping any attempts to take it out of the walls surrounding your digital estate. Data loss refers to an event in which important data is lost to the enterprise, such as in digital fraud. Data loss prevention focuses on preventing illicit transfer of data outside the organization’s digital estate.

Effective data loss prevention works by classifying sensitive data effectively; monitoring all the channels and devices for behavior that might indicate data is being shared or accessed inappropriately; detecting unauthorized behaviors (such as some someone trying to upload confidential documents to a personal cloud storage), and prevention (taking action to prevent data loss).

Data Loss Prevention in Action

For illustrative purposes, let’s examine the example of a retail bank branch, which holds sensitive customer information such as account numbers and Social Security Numbers across multiple databases needed to manage a bank’s services ranging from wealth management to mortgage lending. Protecting this data from unauthorized access or leakage is crucial, and that’s where data loss prevention comes into play. In this example, data loss prevention might cover:

  • Classification: the branch classifies its data based on sensitivity. Customer names and addresses might be considered low risk. Account numbers, credit card details, and tax documents are high-risk. This classification helps prioritize protection efforts.
  • Monitoring: data loss prevention tools keep a watchful eye on various channels within the bank’s network. For example, when a teller sends an email to a customer, the data loss prevention system scans the content for high-risk keywords like “account number” or “Social Security Number.” If detected, the system might flag the email for review before it is sent, preventing accidental data exposure.
  • Detection: when the data loss prevention system identifies a potential data leak, it analyzes the content and compares it to pre-defined policies. For example, a policy might prohibit sending customer account numbers via email. If the system detects an email containing an account number, it triggers an alert.
  • Prevention: depending on the severity of the violation, the data loss prevention system takes various actions such as blocking the sharing of data and alerting appropriate personnel.

That’s how DLP is supposed to work. But organizations may not prioritize DLP, which makes them vulnerable to bad actors. By using techniques such as social engineering, bad actors can and do take advantage of a fragmented security perimeter lacking effective DLP and exploit the weakness. 

Why Data Loss Prevention Is a Complex Problem

Effective DLP is becoming more difficult for organizations to manage well for a number of reasons. For one thing, bad actors are constantly evolving their tactics, employing AI-powered tools and social engineering techniques to bypass traditional security measures. But the increased sophistication of bad actors is only part of the problem. Other issues include:

  • An explosion of data. Businesses are generating and storing more data than ever before, across diverse sources like emails, cloud platforms, mobile devices, and IoT sensors. This vast data landscape makes it challenging for any data loss prevention system to track and secure sensitive information effectively.
  • Remote work expansion. The shift towards remote work has blurred the lines between corporate and personal environments, increasing the risk of accidental data leaks through unsecured personal devices and unauthorized file sharing practices.
  • The bring your own device (BYOD) trend. BYOD means that here employees use their personal devices for work purposes. This is a constant challenge to businesses trying to practice good data loss prevention. For example, BYOD expands the organization’s attack surface by multiplying the number of endpoints accessing sensitive data. Personal devices might not have the same level of security controls as corporate devices, making them more vulnerable to malware, phishing attacks, and unauthorized access. In fact, Fortune Business Insights cites BYOD as one of the main drivers for the growth of the data loss prevention market. 
  • Cloud adoption. The migration to cloud-based solutions brings flexibility and scalability, but also introduces new data security challenges, and effective data loss prevention approaches need to accommodate those risks.

For these and many other reasons, businesses and public organizations need to look beyond the adoption of better data loss prevention tools. They need a stronger DLP approach that encompasses the right processes, tools, and much more – what we call a 360-degree approach. Here are its key components:

A 360-Degree Approach to Data Loss Prevention

Take a Methodical Approach

A methodical approach means using a vetted methodology to systematically test every conceivable way that a bad actor can cause data loss. For example, the MITRE ATT&CK Framework is a comprehensive matrix of tactics and techniques used by threat actors and adversaries in cyber attacks. It’s organized into several matrices, each catering to different environments like enterprise, mobile, and industrial control systems (ICS). This framework can be a valuable way for organizations looking to enhance their cybersecurity posture, including data loss prevention (DLP) strategies. The framework can be used to identify gaps in an organization’s DLP controls by mapping the techniques and tactics used by bad actors to the organization’s existing controls. Although MITRE ATT&CK is comprehensive, it’s not exhaustive of all possible attack scenarios. 

Use Effective Tools

Fortunately a number of tools have emerged to help businesses protect their digital estates. For example, Microsoft Purview is an integrated solution designed to help organizations understand, secure, and manage their data across various environments. It offers a unified experience to address the complexities of data security, governance, and compliance. The platform uses AI-powered data classification technology to provide data protection. It includes features like data maps, extensive audit logs, signals, and a management experience to ensure comprehensive coverage.

Microsoft Purview’s AI-driven data classification can identify sensitive data across your organization’s digital landscape. This information can be used to prioritize the protection of assets that are most likely to be targeted by attackers, as outlined in the MITRE ATT&CK framework.

On the other hand, UpGuard BreachSight can monitor an organization for 70+ security controls providing a cyber security rating and automatically detect leaked credentials and data exposures in S3 buckets, Rsync servers, GitHub repos, and more. 

Strengthen Your Security Posture with a Zero Trust Framework

A security posture includes, among other things, technological controls (e.g., firewalls, intrusion detection and prevention systems, encryption technologies, and other security hardware and software solutions) and access controls (ensuring that only authorized individuals and devices can access certain information). 

Once you assess your security posture, your organization will be faced with a crucial question: just how far are you willing to go to safeguard your company’s systems? This is where zero trust architecture (ZTA) comes into play. Traditional security models often operate on the assumption that everything inside the organization’s network is trusted, creating a strong perimeter to keep threats out. But ZTA assumes that threats can exist both outside and inside the traditional network perimeter, thus necessitating rigorous verification and control measures. As a result, a company employing ZTA protects its systems with a far greater level of rigor. 

For instance, under the ZTA principle of least privilege, an organization would grant users and devices the minimum level of access they need to perform their duties. This limits the potential for data leakage from over-privileged accounts. In addition, an organization would implement role-based access controls (RBAC) to ensure that only authorized individuals have access to sensitive data.

We recently blogged about the value of ZTA. For more detail, read our recently published blog post, “Should Your Business Adopt a Zero Trust Architecture?

Deploy Purple Teaming

Purple Teaming can be highly effective for DLP. With Purple teaming, one team simulates both attacks on cybersecurity perimeters and their defense. Each team member plays both the role of attacker and defender, which ensures a more robust and intricate breach/attack simulations that may be applied to DLP

The MITRE ATT&CK Framework provides a structured approach to Purple Teaming, which allows you to mimic the actions of real-world adversaries to test your security posture.

For example, the Purple Team could simulate realistic attack scenarios that could lead to data breaches or loss, helping to identify potential vulnerabilities in the current DLP strategy. The Purple Team conducts these simulations and monitors and responds in real-time, providing immediate feedback on the effectiveness of current DLP measures. To improve detection and response, the Purple Team’s activities could test the organization’s ability to detect unauthorized access or exfiltration of data. The Purple Team’s response to these simulated attacks helps refine incident response plans, ensuring faster and more effective actions in the event of a real data breach.

Purple Teaming goes way beyond the examples cited here; this blog post gives the reader a glimpse of what’s possible.

Use Generative AI

Generative AI can help an organization more efficiently and quickly protect its data. Let’s take the example of the retail bank branch I cited earlier, which holds sensitive customer information such as account numbers and Social Security Numbers across multiple databases needed to manage a bank’s services ranging from wealth management to mortgage lending. Generative AI can help the bank in many ways, such as such as threat hunting, anomaly detection, generation of risk rules used by security products, tabletop exercises, incident management, and automation of information capture for the new SEC disclosure ruling from December 2023 cited earlier in this blog post.

For instance, the bank could analyze data activity patterns using generative AI models to detect anomalies and suspicious behavior that might indicate potential data leaks or unauthorized access attempts. This proactive approach can help prevent data breaches before they occur. Generative AI can also create realistic synthetic datasets without compromising real customer information. This synthetic data can be used to train and test DLP systems, improving their effectiveness without privacy risks. 

Here again, this blog post offers a small glimpse of what is possible.

Learn More

We believe a 360-degree approach ensures that an organization constantly stays vigilant with DLP. Centific can help you succeed. We know how to apply DLP holistically through our digital safety capabilities, which combine process and technology with human oversight.

Click here to learn more about our digital safety capabilities