DLP, or data loss prevention, refers to a set of tools and processes used to stop sensitive data from leaving an organization. The goal of DLP is to detect and prevent the unauthorized use and transmission of confidential information.
What is the purpose of DLP?
The main purpose of DLP is to protect an organization’s data from being leaked or stolen. This can encompass data such as intellectual property, customer information, employee records, financial data, and other sensitive information. Some key drivers for implementing DLP include:
- Compliance with regulations such as HIPAA, PCI DSS, and GDPR which require protection of sensitive data
- Preventing costly data breaches that can lead to reputational damage and financial penalties
- Securing critical intellectual property and trade secrets from rivals
- Enabling safe data sharing and collaboration while restricting unauthorized access
- Gaining visibility into how data is used and identifying areas of risk
How does DLP work?
At a high level, DLP solutions work by combining content inspection, context-based rules, and policy enforcement to analyze, detect, and prevent the unwanted transmission of confidential data. The core components of DLP include:
- Content inspection – DLP scans, indexes, and categorizes sensitive information using advanced technologies like optical character recognition (OCR), fingerprinting, data identifiers, regular expressions, and exact data matching.
- Contextual analysis – DLP examines the context around data to distinguish between legitimate and unauthorized use. This can include factors like user behavior, file properties, watermarking, access control levels, etc.
- Policy engine – Organizations define DLP policies to establish content-aware rules governing how data can be used based on its classification. Policies tie data to appropriate actions like encryption, access restrictions, blocking, quarantining, etc.
- Integration – DLP capabilities are integrated across channels like email, web browsers, endpoints, networks, cloud apps, and data storage repositories.
- Machine learning – Advanced DLP solutions apply machine learning and statistical models to improve detection accuracy and enhance policy recommendations.
Where does DLP inspect and analyze data?
DLP systems can analyze and secure data across the following channels:
- Network – Network DLP can inspect traffic on corporate networks using deep packet inspection to identify unauthorized data in motion.
- Endpoints – Endpoints like desktops, laptops, and mobiles are secured via DLP agents that monitor locally stored data and data in use.
- Cloud apps – Integrations with cloud productivity apps like Office 365 enable scanning of data at rest in the cloud.
- Email – Email gateways are integrated with DLP to scan outgoing and incoming email attachments and messages.
- Data storage – DLP connects with data repositories like file shares, NAS, databases, and data warehouses to discover and classify sensitive data at rest.
- Printers/copying – DLP can prevent confidential prints and media copying through on-device protections.
How does inspection work in DLP?
DLP inspection relies on the following techniques to accurately identify confidential data:
- Regular expressions – Pattern matching based on regular expressions, keywords, text strings, etc. helps recognize data types like credit cards and IDs.
- Database fingerprinting – Fingerprinting matches structured data like databases against known patterns including checksums and file properties.
- Statistical analysis – Models determine if data has the statistical properties of sensitive info, like identifying random data vs. real credit card numbers.
- Exact data matching – Comparing data against authoritative sources like HR records or encrypted data loss libraries to find precise data matches.
- Optical character recognition (OCR) – OCR extracts text from images to inspect scanned documents, photos, and screenshots for policy violations.
- Custom data identifiers – Organizations can create customized fingerprints for proprietary information, source code, legal documents, etc.
What actions can DLP systems take?
Based on configured policies, DLP systems can take intelligent actions when sensitive data is detected, including:
- Block – Stop high-risk data transfers immediately to prevent data from leaving the organization.
- Encrypt – Automatically encrypt confidential data prior to sharing externally using rights management.
- Quarantine – Move messages or files to a quarantined area for review prior to release.
- Redact – Scrub sensitive data (e.g. credit card digits, health details) from files while retaining format and structure.
- Notify – Send alerts to data owners, compliance teams, or administrators when policy violations occur.
- Audit Logs – Log all enforcement actions taken for forensics and compliance reporting.
What are the key benefits of DLP?
Implementing DLP provides the following advantages:
- Minimizes risk of data breaches that lead to reputational damage, fines, and customer distrust.
- Improves compliance with data protection laws and industry regulations.
- Centralizes visibility into how sensitive data is used across the organization.
- Reduces insider threats from employees mishandling confidential data.
- Accelerates incident response by quickly identifying unauthorized data access or transfers.
- Enables safer data sharing with partners and subsidiaries by restricting access.
- Protects intellectual property and digital assets from competitors.
- Reduces manual work required for data classification through automation.
What are common DLP deployment options?
DLP solutions can be deployed on-premises, in the cloud, or through a hybrid model:
- On-premises – DLP software and infrastructure are installed on your internal servers and networks.
- Cloud-based – DLP is delivered as a service by the vendor and accessed over the internet.
- Hybrid – Combines on-prem DLP for some data channels with a cloud service for other channels.
Cloud DLP offers faster deployment times and lower administrative overhead but may have limitations in scanning local data stores or endpoints. Hybrid models provide the most flexibility.
What are common use cases for DLP?
Typical use cases where organizations deploy DLP include:
- Compliance – Enforcing regulatory mandates around consumer privacy (CCPA, GDPR), financial data (GLBA), healthcare data (HIPAA), or payment card data (PCI DSS).
- Intellectual property protection – Safeguarding sensitive formulas, recipes, source code, patents, or design documents from theft.
- Data governance – Discovering where sensitive data resides and controlling its usage across the organization.
- Insider threat prevention – Deterring employee theft of customer lists, employee records, M&A data, and other digital assets.
- Cloud security – Extending data protection policies by scanning cloud apps and storage for policy violations.
- Third-party management – Monitoring vendors and outsourcers who handle sensitive data to prevent unauthorized disclosures.
How can you strengthen DLP policies?
Best practices for creating effective DLP policies include:
- Map policies directly to regulatory and business requirements.
- Utilize all relevant data types, fingerprints, and identifiers to improve detection.
- Leverage contextual factors like user role, project, and data origin to reduce false positives.
- Test policies before activation to fix gaps and evaluate business impact.
- Align actions like blocking and encryption to the level of risk.
- Set exceptions to avoid impairing employee productivity.
- Create separate policies for internal users, external partners, and high-privilege accounts.
- Document policy changes and periodically tune against new use cases.
What are best practices for DLP adoption?
Key best practices for driving DLP success include:
- Obtain executive sponsorship and have clear business goals.
- Build internal support by showing DLP protects employees and the organization.
- Phase deployment across data channels to maximize value incrementally.
- Start withHighest-risk data to quickly demonstrate ROI.
- Set notifications before blocking to ease policy acceptance.
- Communicate new policies to users transparently before activation.
- Refine policies gradually based on user feedback and compliance needs.
- Leverage DLP reporting insights to highlight program maturity and success.
How can you optimize DLP to reduce false positives?
Strategies to cut down on false positives include:
- Tuning fingerprinting thresholds to balance precision and recall.
- Accounting for regional differences in data formats like IDs and postal codes.
- Regularly updating fingerprints and policies aligned to new data types.
- Establishing whitelist exceptions for trusted systems, users, file paths, etc.
- Modeling the behavior of employees and partners to set dynamic baselines.
- Analyzing content through natural language processing to determine sensitive context.
- Correlating DLP findings with other security tools to improve accuracy.
- Providing users self-service to report false positives and unlock files.
Ongoing policy refinement and leverage of contextual signals is key to improving precision over time.
What are the limitations of DLP systems?
Potential limitations to consider include:
- Cannot prevent insider abuse of authorized access to sensitive data.
- Encrypted data cannot be scanned until it is decrypted on endpoint.
- Resource-intensive deep content inspection impacts network performance.
- Implementation requires mapping data flows and integrating multiple systems.
- Cloud app APIs may not provide full visibility compared to on-prem solutions.
- Regular policy and fingerprint updates are required as new data types emerge.
- Difficulty handling highly dynamic, unstructured data like software code.
- Tight policies can negatively impact employee productivity and collaboration.
How can you demonstrate DLP results and value?
Metrics to quantify DLP success and value include:
- Policy coverage across locations, channels, and data types.
- Number of data leakage incidents and violations blocked.
- Reduced data breach risk measured by metrics like MVR (mean value at risk).
- Faster incident response due to centralized monitoring and alerts.
- Audit logs demonstrating regulatory compliance controls.
- Automated enforcement relieving manual policy enforcement workload.
- Quantified IP protection by identifying attempts to export source code.
- Employee perception surveys indicating increased trust and confidence.
Ongoing monitoring of metrics demonstrates program maturity and helps justify continued investment.
Conclusion
DLP provides a critical data protection layer by stopping sensitive information loss across multiple channels. Core capabilities like content inspection, endpoint security, and integrated enforcement reduce the risk of costly data breaches. While DLP has some limitations, proper planning, deployment, policy tuning, and user education enable organizations to maximize value. When positioned as enabling safe data usage, DLP can strongly augment regulatory compliance and intellectual property protection programs.
