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January 3, 2024
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 min read

Why should you move from Legacy DLP?

Migrating from legacy DLP to next-gen SaaS DLP offers enhanced scalability, real-time data protection, and advanced threat detection for robust data security

Why should you move from Legacy DLP?
Calendar Icon White
January 3, 2024
Clock Icon
 min read

Why should you move from Legacy DLP?

Migrating from legacy DLP to next-gen SaaS DLP offers enhanced scalability, real-time data protection, and advanced threat detection for robust data security


Legacy DLP systems once considered strongholds of data security, are struggling to keep up. In our conversation with Aatish, founder of Strac, we explored the key reasons for transitioning from legacy DLP to adaptive solutions. Drawing from his extensive experience in the field, Aatish highlights why modern DLP is not just a choice but a necessity in today's digital landscape.

Challenges With Legacy DLP

What challenges do you see with legacy DLP systems? 

Outdated methods :Traditional Data Loss Prevention (DLP) solutions were originally created to protect data within a specific network. Still, they do not provide sufficient coverage for modern work environments such as cloud services, mobile devices, and remote work. As a result, data can be at risk in these scenarios if not properly managed.

Lacks flexibility: Legacy DLP systems lack flexibility, making it challenging to adapt to the changing IT landscape and the increasingly complex data usage patterns within organizations. Their strict rules may result in either excessive blocking of legitimate activities or inadequate protection of sensitive data.  

False positives: These systems are notorious for producing many false positives, leading to extra work for IT teams and potentially hindering important data transfers, thus reducing overall business efficiency. 

Resource-intensive: Legacy DLP solutions can be complex and difficult to manage, requiring constant updates and policies to keep up with evolving data types and transfer methods. This puts a strain on IT resources that could be better used elsewhere.

Lacks context: One of the main drawbacks of traditional DLP tools is their lack of contextual awareness and machine-learning capabilities. These tools often rely on predefined criteria to block or flag data transfers without fully understanding the context of the data usage. This can lead to inefficient data protection strategies. However, with modern DLP systems like Strac, this is no longer an issue. Strac has an advanced contextual understanding of unstructured text and documents, including all document formats such as PDFs, jpegs, PNGs, screenshots, word docs, and Excel spreadsheets for a more effective protection of sensitive data within an organization.

Performance and integration issues : Legacy DLP systems can cause problems with network and system performance, potentially slowing down data transfer rates and impacting the performance of monitoring systems, which can make users unhappy. Integrating legacy DLP solutions with other security tools is no easy task, leading to security gaps and breaches.

Threat underestimation: As data protection rules change, legacy DLP systems might have difficulty meeting new standards, causing problems for organizations trying to stay in line. With the increase in tricky cyber dangers like APTs and insider threats, traditional DLP solutions might not provide sufficient protection because of their rule-based and perimeter-focused nature. Additionally, these systems often lack features to educate users about data security best practices, which can lead to a lack of user engagement and understanding, making it easier for DLP policies to be bypassed or ignored.

Legacy DLP Vs SaaS & Cloud DLP

How are legacy DLP systems compared to newer solutions in terms of scalability?

Legacy systems, originally designed for a more static digital environment, are becoming inefficient as they have difficulty keeping up with the evolution of technology, changing workflows, and the rapid expansion of data in modern organizations. In particular, legacy DLP systems may lack the agility and integration capabilities to keep up with the digital landscape. This can create bottlenecks in an organization's operations, leaving them vulnerable to cyber threats.

In contrast, modern DLP solutions prioritize scalability and adaptability as their fundamental principles. They are specifically engineered to handle the increasing volume and variety of data while seamlessly integrating with cloud-based services and adapting to new IT infrastructures and workflows. This adaptability helps to handle complex cyber threats. With proactive and responsive data security capabilities, these solutions are necessary for meeting regulatory requirements and adapting to the fast-paced business environment.

Expanding operations globally requires a DLP solution to handle increased data traffic and regulatory environments. While a legacy system may require extensive manual updates and configurations to adapt, a modern solution would automatically adjust its parameters for continuous compliance and protection. This allows companies to scale efficiently without sacrificing security.

How do modern DLP solutions' incident response capabilities differ from legacy systems, particularly in speed and effectiveness?

Modern DLP solutions surpass their legacy counterparts in incident response capabilities, particularly in speed and effectiveness. While legacy systems rely heavily on manual processes, modern solutions use automation and advanced analytics to quickly identify and address security incidents, reducing the time it takes to resolve them. Moreover, modern DLPs integrate AI and machine learning to proactively identify and mitigate threats, resulting in a more efficient and timely response that is essential for minimizing the impact of security incidents in today's fast-paced digital environment. For instance, a modern DLP can automatically detect unusual data transfers that deviate from typical patterns and promptly block or alert administrators—a task that legacy systems might flag for later manual review. Modern data loss prevention (DLP) tools offer a more comprehensive and agile security posture by integrating with other security tools for faster and more coordinated responses to incidents. With these tools, companies can react quickly and take preventative measures to stay ahead of potential threats.

How do the costs, both financial and in terms - of resource allocation, compare between maintaining legacy DLP systems and adopting modern DLP solutions?

In terms of finances, legacy systems may appear cheaper at first, but they often cost more in the long run due to manual processes, inefficiencies, and frequent updates. On the other hand, modern DLP solutions may have higher initial costs but provide better long-term value with automation, improved security features, and less need for manual work. Regarding resources, modern solutions free IT staff from routine tasks, allowing them to focus on strategic projects. This optimizes resource allocation and boosts overall organizational productivity and security. 

Modern DLP solutions offer greater revenue potential than legacy systems by streamlining operations with automated data protection and reducing the need for extensive manual oversight. They also integrate more effectively with modern IT environments, enhancing productivity and data security. This increased efficiency leads to better resource utilization and reduced downtime, ultimately contributing to a healthier bottom line. Their advanced threat detection capabilities can prevent costly data breaches, protecting not only data but also the organization's financial and reputational assets.

How Dynamic is Strac as a next-gen SaaS DLP ?

Strac SaaS and Endpoint DLP protect businesses by discovering (scanning), classifying and remediating sensitive data like SSN, Drivers License, Credit Cards, Bank Numbers, IP (Confidential Data), etc. across all communication channels like O365, Slack, G-Workspace (Gmail, Google Drive), Email, One Drive, Sharepoint, Jira, Zendesk, Salesforce, etc. and also endpoints like Mac, Windows.

Elevate your data security posture with Strac DLP. Schedule a demo today.

Our SaaS & Endpoint DLP solution helps you:

  • Discover, classify, and protect sensitive data: Strac's AI lets you detect sensitive data with accuracy and precision across volumes of unstructured texts and documents.
  • Remediate sensitive data: Strac provides remediation actions like Redaction, Blocking, Alerting, and Encryption. Strac's redaction replaces sensitive data with a link to Strac's secure Vault.
  • API integration: With Strac, you can leverage Strac's RESTful APIs to do the same alongside their native No-Code integrations. Strac seamlessly connects with popular cloud and SaaS platforms like Zendesk, Slack, Gmail, Office 365, and Salesforce in less than 10 minutes. It also provides DLP solutions for Generative AI products such as ChatGPT and Google Bard. With API access, it can detect and redact sensitive data before sending it to LLM providers like OpenAI or AWS Bedrock. 
  • Dashboard and analytics: See all sensitive data discovered and remediated by Strac in Strac's Vault with beautiful graphs and analytics results, like which employees shared what sensitive data from which devices, etc.
  • Achieve compliance with regulations laws: Strac Data Discover, DLP (Data Leak Prevention) and CASB (Cloud Access Security Broker) solutions will help you achieve PCI, SOC 2, NIST CSF, HIPAA, GDPR, CCPA, India's DPDP (Digital Personal Data Protection) 
  • Exceptional support: Strac's dedicated customer support team guides clients through the integration process and beyond, ensuring a seamless experience overall. 
  • Tailored settings: Strac offers pre-built compliance templates containing all sensitive data elements for detection and redaction and customizable configurations to meet specific business needs, guaranteeing alignment with individual data protection requirements.

Founder, Strac. ex-Amazon Payments Infrastructure (Widget, API, Security) Builder for 11 years.

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