Data is a valuable asset for organizations, containing sensitive information such as customer data, intellectual property, financial records, and trade secrets. However, with the increasing volume of data and the evolving threat landscape, the risk of data breaches, leaks, or unauthorized access has become a pressing concern. This is where Data Loss Prevention (DLP) comes in.

DLP is a both a strategy and a set of technologies designed to protect sensitive data from unauthorized disclosure, loss, or leakage. It encompasses a range of measures aimed at identifying, monitoring, and safeguarding sensitive information throughout its lifecycle.

Here, we’ll focus on two key components: Content Awareness and Contextual Analysis.

In 2022, an investment firm discovered a data breach and took action. Unfortunately, there was a breach before that of which they became aware after the 2nd breach.

There are at least two DLP lessons here.

1. The Not-So-Good - A DLP strategy that was not in place for the initial breach was encryption. The passwords retrieved were only hashed - not salted or encrypted. While hashes are one way to secure some things, it’s quite possible to find the hash.

2. The Good - A DLP strategy in use before the second breach was encryption and data separation. Because of the these, 1) the data stolen was not financial or private personal information, and 2) any confidential information stolen could not be viewed because of the encryption.

Content Awareness is a critical aspect of DLP that enables organizations to understand and analyze the actual content of data, helping to identify and protect sensitive information effectively. Here are three key things to know about Content Awareness in DLP:

    One fundamental aspect of Content Awareness is the ability to discover and classify sensitive data within an organization's environment. This involves identifying personally identifiable information (PII), financial data, intellectual property, trade secrets, and other confidential information. By implementing data discovery techniques, such as data fingerprinting, metadata analysis, and even machine learning algorithms, organizations can accurately locate and classify sensitive data.

    Content Awareness enables DLP systems to analyze the content of files, documents, emails, and other forms of data to enforce data protection policies. These policies define how sensitive data should be handled, specifying rules and guidelines for data access, storage, transmission, and disposal. Through content analysis techniques (e.g., data classification, keyword matching, pattern recognition), DLP solutions can identify potential policy violations, flag suspicious activities, and enforce security controls to prevent or mitigate data breaches.

By continuously monitoring data flows, both within the organization's network and at endpoints, DLP solutions can detect unauthorized transfers of sensitive information, accidental emailing of confidential data, or attempts to copy data to removable storage devices. Real-time prevention mechanisms, such as blocking transfers, email filtering, and contextual prompts for user actions, add an additional layer of protection to ensure data security.

Essential to security programs is “monitoring end user access to sensitive information, as well as the movement of this data.” We’ll see the importance next.

In June 2023, a university discovered a cyberattack. A great aspect of this was their incident response (IR) – “…they discovered the breach on Tuesday, June 6th, and immediately launched an investigation.” On the downside, they don’t know what all data – research, personal, or otherwise – was accessed, or by whom. Knowing that would speed up the investigation. Because crime can happen anytime, one of the primary items to have in place is an IR plan.

Contextual Analysis involves examining the contextual factors surrounding data to gain a deeper understanding of its meaning, relevance, and potential risks. Here are three key things to know about Contextual Analysis in DLP:

By analyzing metadata (data about data), DLP systems gain insights into the origin, purpose, and handling of the data. Contextual analysis examines relationships between different data elements, such as the sender and recipient of an email, the parent-child relationships between files and folders, or the connections between data entities in a database. This analysis helps organizations understand the context in which data is being accessed, shared, or stored, aiding in the detection of potential data risks or anomalies.

Contextual Analysis in DLP involves analyzing user and device information to understand the context surrounding data access and usage. This includes considering user identities, roles, permissions, device types, IP addresses, and geographical locations. By analyzing user and device context, organizations can detect suspicious activities, unauthorized access attempts, or policy violations. For example, if a user attempts to access sensitive financial data from an unfamiliar device or location, it can trigger an alert for further investigation.

By establishing baseline patterns of data access and usage, DLP systems can detect deviations that may indicate potential data loss or leakage. For instance, large-scale downloads, multiple failed login attempts, or access from unauthorized locations can indicate suspicious activities. Behavioral analysis techniques, such as machine learning algorithms and user behavior analytics, enable organizations to detect insider threats or compromised accounts and take timely actions.

Implementing DLP solutions can be a complex undertaking, as organizations need to address various challenges and considerations to ensure successful deployment and use. Below are a few key challenges and considerations when implementing DLP:

Overly strict DLP policies will hinder productivity and create user frustration, leading to workarounds or circumvention of security controls. Engage stakeholders from different departments to find the balance between data protection and the ability to perform daily tasks efficiently.

Privacy regulations (e.g., GDPR (General Data Protection Regulation), CPRA (California Privacy Rights Act) pose significant challenges in DLP implementation. Organizations must ensure that their DLP solutions align with privacy requirements, such as data minimization, consent management, and secure data handling.

Integrating DLP solutions with existing systems and security infrastructure (such as networks and firewalls) can be complex. Interoperability and compatibility with existing infrastructure are important factors to consider when selecting DLP solutions.

DLP doesn’t have to be onerous. Due to all kinds of factors, it’s going to be more complex than one would wish, but it’s not infeasible. It’s a natural follow-up to risk management.

Develop a roadmap and take the next step.

Ross Moore is the Cyber Security Support Analyst with Passageways. He has experience with ISO 27001 and SOC 2 Type 2 implementation and maintenance. Over the course of his 20+ years of IT and Security, Ross has served in a variety of operations and infosec roles for companies in the manufacturing, healthcare, real estate, business insurance, and technology sectors. He holds (ISC)2’s SSCP along with CompTIA’s (News - Alert) Pentest+ and Security+ certifications, a B.S. in Cyber Security and Information Assurance from WGU, and a B.A. in Bible/Counseling from Johnson University. He is also a regular writer at Bora.