Data loss prevention: A future-proof strategy for data-driven defense

New-age companies are adopting Data Science as a natural fit to raise their data-driven digital transformation game unpinned at regulatory compliance, better risk management and data visibility. However, while embracing Data Science within their organizational IT infrastructures, these companies deal with the sheer volume of critical data, which is always at high risk of data loss, human errors, and security vulnerabilities due to insensitive personal data handling. 

In the wake of data loss vulnerability, the Data Loss Prevention (DLP) strategy has emerged as a crucial defensive component for new-age enterprises to significantly reduce the risks of potential data breaches, unwanted destruction of sensitive data, or data exfiltration transmissions through early detection. As the name indicates, DLP is a comprehensive go-to strategy that primarily rests on a suite of advanced tools designed to discover, classify, monitor, and protect data intuitively with zero friction in real-time. 

The primary goal of the DLP system is to safeguard customer information, financial records, or intellectual property that is flowing across the web, cloud, email, network, and endpoint. As the true gatekeepers, they help companies strengthen their data fabric from unauthorized access and prevent sensitive information.

In today’s landscape, companies rely on a comprehensive Data Loss Prevention strategy to

• Protect Personally Identifiable Information (PII)
• Maintain compliance with relevant regulations
• Protect business-crucial Intellectual Property
• Achieve data visibility across large processes
• Secure datasets for hybrid work settings and remote cloud systems
• Maintain security in Bring Your Own Device (BYOD) environments

Furthermore, DLP strategy solves challenges related to the database, data leakage, or loss outside organizational boundaries by deploying a powerful combination of policies, encryption, and regular monitoring.

Keeping Data leakage under wraps with a robust Data Loss Prevention strategy

Deeper penetration into Generative AI breakthroughs like large language models (LLMs) within an organizational culture firmly accelerates the data loss risk by exposing business-critical datasets to everyone. However, an enterprise can strengthen its dataset lifecycle by putting robust DLP strategies at the core of everything. Let’s explore how to prevent Data leaks and losses using DLP:

• Pattern Recognition: 

For an organization, Data can be a valuable asset as well as a liability to overall return on investments (ROI) when not handled carefully. Implementing a right DLP strategy instantly prevents the risk of data turning into a liability by rigorously scrutinizing extensive datasets to gain a better clarity on Data patterns. For example, in a retail setting, when businesses leverage a DLP strategy, they can quickly identify the common patterns used by cyber threat agents. Generally, DLP recognizes the patterns by scanning a correlation across specific products consistently being targeted for theft during certain times or locations. This is how DLP uses a targeted and informed approach to prevent Data leaks and losses.

• Anomaly Detection: 

Anomalies often signal potential data leakage challenges when Data is in use (endpoint actions), pre-processing stages, in-motion (network traffic), and at rest (Data storage). In the Data Science landscape, anomalies can be described as the multiple data points which are deviating from the expected patterns within a distribution in a particular datasets. Generally, these issues indicate inconsistencies and multiple distribution of anomalous items within the datasets.

Companies by leveraging Data Loss Prevention solutions, can easily identify numerous anomalies in different data within a given Data model, alarmingly indicating an infrastructure failure, a security threat, false positives, Data noise, an intrusion, an unexpected surge in inventory shrinkage, irregular transaction patterns, or other deviations from the norm. This capability enables organizations to investigate and address potential threats before they escalate promptly.

• Predictive Analytics: 

Predictive Analytics primarily feeds on historical analysis and plays a crucial role in detecting any anomalies by quickly reviewing all the required actions triggered at the user's ends. Also, companies can identify the hidden data leakage challenges falling outside of the regular pattern using Data Analytics, and anticipate future growth trends as well as potential risks. 

To prevent Data anomalies and loss possibilities at every layer, Data Loss Prevention solutions have a significant role to play. As intelligent guardrails, they add an additional security layer to the traditional (read deteriorated) security system so businesses can easily navigate anomalies and malicious actions pre-determined by a list of users, actions, and systems. Embracing predictive analytics-powered DLP helps businesses to scan as many databases and applications as possible to prevent losses.

• Optimizing real-time security measures: 

Undoubtedly, a strong security posture is the heart and soul of every organization. Any unauthorized bypass to the datasets available in the existing security apparatus, can provoke specific Data leakage problems. Optimizing Data infrastructure with DLP-driven security measures, helps businesses to gain real-time access to valuable insights into the most vulnerable-prone datasets where heightened security is most needed. 

Also, implementing real-time monitoring systems powered by robust Data Loss Prevention systems, ensures that all the potential threats like data security threats, regulatory compliance violations, unauthorized file transfers, quarantine sensitive documents, and unauthorized user access privileges are addressed immediately as they crop. Also, DLP triggers real-time alerts in response to any suspicious activities, enabling companies to respond swiftly and take appropriate action using real-time monitoring capabilities to mitigate the Data leakage risks.

• Continuous improvement: 

DLP systems contribute to a greater continuous improvement quotient due to their iterative nature facilitating enhanced visibility, better Data classification, and quick incident response. They allow organizations to adapt and refine their data loss prevention strategies to cope with new data threats while understanding the outcomes of existing implemented measures. They oftentimes work as a dynamic approach ensuring absolute defense 

• With greater transparency to how sensitive datasets are being accessed, used, and shared across the organizational fabrics
• Classifying Data in accordance with the sensitivity quotient
• Continuous refinement of Data classification policies to minimize false positives
• Awaring users on Data leakage problems, security measures, and the consequences of data policy violations 
• Identifying high-risk areas within Data fabric and enabling companies to allocate resources efficiently while maximizing the effectiveness of security measures.

• Fraud Detection: 

The journey of minimizing false positive detection and managing risks becomes much easier when DLP solutions are put at the core. With robust Data Loss Prevention systems, companies can launch granular policies to detect sensitive data fraud early and redefine their existing policies in accordance with actual usage patterns, contextual analysis, and incident investigations feedback. This reduces false positives that used to trigger due to weak points like user behavior, data classification, location, and access time. What's more? DLP systems utilize machine learning and artificial intelligence (AI) algorithms to identify the continuously evolving data patterns and abnormal user behaviors causing potential data losses and security threats.

Top Data Loss Prevention Tools trending in 2024

As organizations dive deeply into the data-driven digital landscape, data is transmitted through chat rooms, emails, API calls, and rest in the Cloud, databases, and data lakes. The datasets are prone to data leakage and breaches throughout the journey. Robust data loss prevention measures are always needed to maintain good data governance balance and safeguard sensitive datasets. 

For new-age companies, the top-tier DLP solutions and tools have emerged as true game-changers for data risk management. Let’s explore the top DLP tools dominating in 2024, highlighting their key features, strengths, and contributions to the ever-evolving data protection landscape.

• Symantec DLP:

Symantec Data Loss Prevention (DLP) still stands out in 2024. It has solid features for protecting sensitive data on many platforms. Symantec DLP has advanced detection. It effectively finds and protects data at rest, in motion, and use. Its comprehensive policy enforcement ensures compliance with regulations and prevents data breaches proactively. Additionally, Symantec DLP integrates seamlessly with existing security. This enhances its effectiveness in enterprise environments.

• Trellix:

Trellix is a prominent player in data loss prevention. It offers innovative solutions for modern security challenges, and its easy-to-use interface and customizable policies make it a top choice. Organizations prefer Trellix because of its advanced abilities. It detects and reduces data risks across endpoints, networks, and clouds. The DLP tool also empowers businesses to control their sensitive information and helps them collaborate and be productive.

• Forcepoint:

Forcepoint' is a popular tool companies use for Data Loss Prevention. It comes with advanced threat protection and data visibility capabilities helping companies to monitor datasets seamlessly on various endpoints. Forcepoint is best used to achieve user-centric security and effectively manage data across the entire lifecycle, from creation to disposal. Interestingly, this popular DLP tool has adaptive controls and real-time monitoring capabilities using which organizations stay vigilant towards emerging threats and compliance requirements.

• Palo Alto Network DLP:

Palo Alto Network is the industry’s first enterprise DLP solution delivered through the Cloud. It is a top choice for many organizations in today’s competitive digital landscape because of its comprehensive data protection capabilities. Resting on the solid foundation of Machine Learning and Advanced Analytics, the Palo Alto Network DLP tool enables companies to attain unparalleled accuracy faster by identifying and mitigating data leakage risks. Furthermore, the tool integrates seamlessly with Palo Alto's Next-Generation Firewall, which adds security to the network. This also ensures regulatory compliance and stops data exfiltration attempts effectively.

Industry-wise use cases for loss prevention data mining

Data Loss Prevention is gaining huge momentum as a critical aspect of risk management, assisting companies to keep Data leakage at bay. And yes, the adoption of DLP systems is rising exponentially across industries. Earlier, the global market size for data loss prevention was valued at $1.84 billion in the year 2022. But, now reports projecting an immense growth from $2.21 billion in 2023 to $10.05 billion by 2030.

In the wake of stringent Data protection regulations imposed worldwide, DLP systems are a proven transformative force to proactively identify potential threats, detect anomalies, and optimize processes. Let’s delve deeper and explore industry-wise use cases for data loss prevention significance, highlighting how various sectors harness the power of advanced Data Science, AI & ML to fortify their defenses against fraud, inefficiencies, and other potential risks.

Financial Services:

Banks and financial institutions use DLP for real-time fraud detection. Breakthroughs like Machine Learning and Artificial Intelligence models accelerate the analyzing process of transaction patterns, user behavior, and other Data points to identify potentially fraudulent activities. Also, when Data Science is employed in DLP to assess credit risk accurately, these Predictive Analytics-driven DLP tools analyze historical data to predict the likelihood of default, helping companies make informed lending decisions.

Retail:

Retailers using Data Science powered DLP solutions to optimize inventory levels and prevent losses from overstocking or understocking. The demand forecasting capabilities of these tools help companies to analyze historical sales data and external factors to predict future demand. Video Analytics and point-of-sale data are other advantages of using DLPs underpinned at Data Science to identify suspicious behavior in stores and pattern recognition.

Supply Chain:

In industries reliant on machinery, DLP solutions are used for maintenance purposes. Data engineers leverage these tools to analyze equipment data, and predict valuable insights on when maintenance is needed. This way, these systems helping companies to prevent unexpected breakdowns and production losses. Also, they enhances supply chain visibility by analyzing shipments, supplier performance, and logistics data, resulting in early detection and mitigation of risks in real-time.

Cybersecurity:

Many companies prefer to deploy Behavioral Analytics to detect anomalies in user behavior and datasets within their networks. Unusual patterns can indicate a potential cybersecurity threat, allowing for early intervention. DLP systems play a crucial role in analyzing vast amounts of threat intelligence data to identify patterns and trends associated with cyber threats. This information helps companies strengthen their cybersecurity defenses.

Healthcare:

Healthcare organizations use DLP solutions to protect Personally Identifiable Information (PII) and navigate challenges related to unauthorized health data transfers due to the use of third-party unauthorized apps. In Healthcare companies where sensitive patient data is restricted from leaving an organization’s premises, people often leverage instant messaging applications, personal emails, and cloud storage services to access the data. In the process, sensitive datasets are highly vulnerable to data leakage as a result of unauthorized data access.

Predictive modeling-powered DLP systems help Healthcare companies flag anomalies in billing patterns and contribute to patient care by analyzing medical records, treatment outcomes, and other Data points. This helps identify areas for improvement, such as fraudulent claims, malware attacks, and abusive billing practices, while preventing the risks of potential patient data losses in phishing cyberattacks.

Insurance:

Insurers deploy DLP solutions to fortify their fraud detection capabilities. By analyzing patterns in claims data, underwriters can identify potential fraudulent activities and mitigate financial losses. This Data-driven approach enhances the accuracy of risk assessments, enabling insurance companies to maintain profitability while ensuring fair premiums for policyholders.

Navigate loss prevention challenges faster with Kellton

Kellton, as your Data Science partner, solves loss of data by harnessing advanced Analytics and Machine Learning techniques rooted in the robust Data Loss Prevention strategy. We scrutinize the vast dataset patterns and identify the anomalies creating potential risks. Using the meaningful insights derived from complex datasets using our Data Analytics capabilities, we empower businesses to take preemptive actions to fortify their defenses and mitigate losses effectively.