Data loss can be detrimental to any organization, leading to loss of revenue, reputation damage, fines and legal issues. Implementing a comprehensive data loss protection (DLP) strategy is crucial for securing sensitive information and preventing breaches. This article explores key DLP strategies and best practices businesses can employ to keep their data safe.
Why is a data loss prevention strategy important?
There are several reasons organizations need robust DLP measures in place:
- Compliance – Regulations like HIPAA, PCI DSS, GDPR mandate data security and privacy. Failing compliance can lead to heavy penalties.
- Reputational damage – Data breaches erode consumer trust and tarnish brand image. A DLP strategy limits this risk.
- Financial loss – Breaches often facilitate crimes like identity theft and fraud. There are also notification and recovery costs.
- Security risks – Lost or stolen devices containing sensitive data enable cyber threats. DLP reduces vulnerability.
Implementing DLP allows businesses to systematically identify, monitor and protect their critical data across all endpoints and systems.
Key elements of a DLP strategy
An effective data loss prevention plan comprises four central elements:
1. Data discovery and classification
The first step is to discover all sensitive data within the organization and classify it based on its level of sensitivity and privacy requirements. This enables policies, controls and monitoring to be applied accordingly. Data discovery should scan repositories like files servers, databases, emails, cloud apps and endpoints.
Once discovered, data should be classified into categories based on sensitivity – confidential, private, public or restricted. Data can also be labeled by type like customer information, financial records, intellectual property etc. This taxonomy should align with compliance needs and privacy priorities.
2. Policy definition
Clear DLP policies must be defined based on data classification and regulatory obligations. Policies determine appropriate data usage and sharing by users and systems. They dictate access permissions, acceptable destinations for transfers, encryption and retention rules. Well-articulated policies are key to governing data flows.
Policies must address data-at-rest, in-motion and in-use. They must cover endpoints like desktops as well as networks, cloud apps and removable media. Priority policies can focus on particularly sensitive data types.
3. Protection methods
Once sensitive data is discovered and tagged, and policies are set, protection measures must be implemented through technical controls like:
- Network DLP – Installing network security tools to monitor traffic, detect violations and block unauthorized transmission attempts
- Endpoint DLP – Endpoint agents that enforce access controls, encryption and monitor activity
- Cloud DLP – For SaaS apps, to extend data policies and controls to the cloud
- Storage encryption – Encrypting data at rest on servers, devices and cloud repositories
- Rights management – Restricting data access, usage and sharing rights based on user roles
- Data masking – Anonymizing sensitive fields in databases and environments like dev and test
4. Incident response
An incident response plan is essential for handling DLP violations and breaches. It enables rapid containment and recovery through procedures like:
- Alert mechanisms to notify relevant teams of incidents
- Escalation workflows for evaluating and assigning incidents based on severity
- Identifying and mitigating the root cause like closing security gaps or user awareness issues
- Disabling user accounts in case of malicious intent
- Isolating or locking down impacted systems
- Revoking access to stolen or leaked data where possible
- Forensic analysis of how the incident occurred and its scope
- Restoring data from backups if needed
- Compliance reporting and data breach notifications
The plan should clearly define roles and responsibilities across security, legal, executives and PR teams.
Choosing DLP solutions
The right mix of DLP solutions should be implemented to enable the above strategy areas. Main types include:
Network DLP
Network DLP solutions monitor network traffic and communications for violations. They analyze traffic against defined policies to detect abnormal data flows and unauthorized attempts to transmit sensitive data outside the network. Detection capabilities like regular expression matching, database fingerprinting, file type examination and statistical analysis are used. Network DLP can block transfers to unapproved destinations or recipients. Solutions are available as hardware and virtual appliances.
Endpoint DLP
Endpoint DLP tools get installed on individual endpoints like desktops, laptops and servers. Agents monitor activity like data copying to external devices and uploading to web applications. Contextual analysis examines user behavior to identify risky users. Endpoint detection also enforces encryption and controls data leakage points like printers and USB drives.
Cloud DLP
Also called CASB (Cloud Access Security Broker), Cloud DLP extends data protection to SaaS applications. It enforces security policies and monitors for anomalies. Cloud DLP facilitates discovery of sensitive data in cloud apps and can enforce access controls, encryption, tokenization and masking. Integrations with cloud platforms like Microsoft 365, G Suite and Salesforce make deployment easier.
Storage DLP
Protecting data at rest is critical. Storage DLP provides capabilities like enterprise rights management, database auditing and file encryption. It also enables dynamic data masking to anonymize sensitive fields in non-production environments. Strong access controls can be applied to allow only authorized apps and users to access confidential data.
Integrated DLP
Many vendors offer integrated suites that combine multiple controls – network, endpoint, cloud, storage, email etc. Integrated DLP provides centralized policy management and reporting across different data channels. It correlates insights across gateways to better contextualize incidents.
Key DLP best practices
Beyond solutions, organizations need to embed DLP discipline through processes like:
- Ongoing scanning – Regularly scan for sensitive data in repositories, new systems and user endpoints
- Backups – Maintain secure, encrypted backups to enable restoration after incidents
- Access limitations – Allow only authorized users and devices to access confidential data based on least privilege
- BYOD policies – Have stricter DLP controls for BYOD devices compared to managed devices
- Secure transmission – Enforce encryption for data in motion, and data at rest where viable
- Secured deletion – Retire old devices safely using techniques like disk wiping to prevent data recovery
- DLP training – Educate staff regularly on DLP policies and their joint responsibility
- Separation of duties – Ensure no single user has end-to-end data access. Distribute privileges.
- Minimize data copies – Reduce duplication of sensitive data across environments to limit exposure.
The role of user behavior analytics
Aligning DLP monitoring to actual user activity provides valuable risk context. Analyzing behaviors like
- Accessing unusual data volumes
- Access during odd hours
- Copy-pasting data to personal apps
- Repeated failed login attempts
- Downloading huge files
can reveal compromised credentials or malicious intent. User behavior analytics (UBA) leverages machine learning to baseline normal activities and detect anomalies in real-time.
UBA provides dynamic risk scoring of users based on activity patterns. Combining UBA with DLP enhances threat detection and enables adaptive access controls.
Creating a data classification taxonomy
A well-defined classification schema is essential to categorize data correctly and consistently across the organization so that protections can be applied accordingly. A data taxonomy matrix may comprise categories like:
| Classification | Categories | Examples |
| Public | – Marketing content – Public product info |
– Brochures -Blogs, whitepapers |
| Internal | – Internal communications – Process docs |
– Internal newsletters -Work instructions |
| Confidential | – Customer data – HR records – Financial data |
– Account information – Salaries – Invoices |
| Restricted | – Trade secrets – IP, source code |
– Secret formulas – Product design docs |
Classifications should be defined in policies and embedded into DLP systems to enable automatic tagging. Regular audits help verify correct classification and any policy gaps.
Key questions when evaluating DLP solutions
Key parameters to assess when comparing vendor DLP offerings:
- Breadth of coverage across endpoints, networks, cloud apps and data repositories
- Precision of sensitive data discovery across unstructured and structured data
- Accuracy of policy violation detection with low false positives
- Interoperability with the organization’s ecosystem – OS, databases, infrastructure etc.
- Flexibility around custom data types, lexicon configuration etc.
- Automation capabilities for faster deployment and scalability
- User behavior analytics for risk-based analysis
- Dashboard and reporting for compliance needs
- Data protection techniques like encryption, tokenization, masking etc.
- Support options, training resources and implementation assistance
- Cost structure aligning with unique needs
The goal is to select solutions that provide comprehensive coverage, precision detection, ease of use and superior protection capabilities for sensitive data.
Conclusion
A holistic data loss prevention program requires an integrated strategy spanning people, processes and technology controls. Core elements include data discovery, policy definition, technical enforcement and incident response. By deploying the right mix of network, endpoint, cloud and storage solutions; conducting regular user training; and embedding DLP discipline into data handling processes, organizations can secure their sensitive information against internal and external threats.
