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Updated: Oct 23, 2023   |  

Data Protection for the Mid-Market

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Protecting sensitive data from external attackers and malicious insiders is a requirement for all organizations, irrespective of their size. Some data, such as personally identifiable information (PII) or personal health information (PHI), must be protected by law. Other information, like trade secrets, customer lists, product plans, and source code, they must safeguard to maintain their market competitiveness.

Mid-market companies – those with between ~1,000 and ~5,000 employees – face special challenges. Regulators, customers, and partners expect the same protection as their larger counterparts, but these companies often lack the same personnel and financial resources. Here are some common challenges mid-market companies face.

Mid-sized organizations often have few security staff

“Salary expectations vary, but it is likely that many small- to medium-size enterprises simply cannot compete with larger enterprises on salary.”

2022 ISACA State of Cybersecurity

Cybersecurity Ventures recently reported there are over 3.5 million unfilled cybersecurity jobs. Unsurprisingly, another study by ISACA found that 62 percent of organizations have understaffed cybersecurity teams. Security professionals are in high demand, and midmarket organizations must compete for these people against larger organizations with larger budgets.

Mid-sized businesses are attractive targets

Organizations in this category have sufficient data to make them attractive, and adversaries view them as “softer” targets. The 2023 Verizon Data Breach Investigation Report found that organizations with fewer than 1,000 employees experienced over 67 percent more breaches than those with larger headcounts.

Legacy solutions require too much overhead

Most offerings cover either insider risk management or DLP. Mid-market organizations lack the budget to dedicate teams of security professionals to manage legacy versions of either, much less both. They also lack the personnel to sift through the “noise” of false and inconsequential alerts generated by legacy solutions.

Small business solutions are too simple

Mid-market companies are also too complex for small business solutions, which are often just repackaged home security products. They typically have a mix of legacy systems, cloud-based applications, and third-party platforms that need to be integrated with their DLP solutions. Ensuring seamless integration, data flow, and coverage across these diverse systems can be more challenging than for small businesses with simpler IT environments.


What Mid-market Organizations Need

Traditionally, mid-market organizations were forced to choose between large enterprise solutions or small business solutions and find a way to manage an ill-fitting solution. As noted, the former are expensive and require too many resources while the latter simply cannot support the needs of a growing business.

A solution like Next DLP’s Reveal includes several key characteristics:

Insider Risk Management and DLP in a unified solution: Malicious insiders, negligent insiders, and external attackers are looking for the same data. When the latter is using stolen credentials, they can look like an authorized user. Using separate solutions for each is a holdover from traditional approaches to security. It is no longer necessary when you adopt a solution built for today’s threat landscape.

A better approach is to use Reveal’s unified platform to apply insider risk best practices to DLP and vice versa, all while delivering a single viewpoint into your organization’s data.

No data pre-discovery and classification: Legacy solutions require organizations to build a classification schema for all sensitive data then search the enterprise to identify all instances of the data before it can begin protecting it. This includes data in cloud environments, on endpoints, and on network drives. It can take months to identify and classify all the data in an organization, delaying deployment and time to value.

A better approach is to classify data as it is created and accessed. After all, when data is being acted upon is when it is at risk. With machine learning on each endpoint, Reveal eliminates the need for pre-classification of data. Instead, Reveal’s real-time data classification considers content and context to identify and classify data as it is created and used. Content-level inspection identifies patterns for PII, PHI, PCI, and other fixed data types. Contextual inspection helps your team identify when more varied data like intellectual property is at risk.

Simple policy creation and management: Legacy DLP and insider risk management platforms rely on granular policies that dictate what each class of users can do with each class of data. New data types, new users, and new threats result in new rules and false positives that frustrate administrators and users. Changes to business processes and the threat landscape can also create false negatives where a granular policy doesn’t even know what to look for.

Reveal’s policy-free approach enables rapid time to value and eliminates workflow disruptions due to outdated policies and complements our traditional, policy-based approach. Machine learning on each endpoint allows Reveal to baseline each user in days instead of months to understand acceptable behavior and report on risks to data without preset rules. Once a baseline is built on actual user activity, information teams can use those insights to build policies that more accurately reflect reality. Meanwhile, machine learning continues to evolve with the users and can flag growing trends and data risks before an incident.

Protects data without compromising user privacy: Respecting employee privacy is important to maintaining both internal trust and external compliance with privacy regulations. Invasive employee monitoring solutions can lead employees to circumvent data protection solutions. Information security teams need a way to balance security and productivity to match the business needs.

Reveal uses pseudonymization to detect and mitigate threats without compromising the privacy of users. This gives administrators the information they need to uncover risks while maintaining the strict confidentiality of users. Scoped Investigations empower organizations to meet employee privacy expectations and comply with information security regulations by limiting the information accessible to security analysts for forensic analysis. Scoped Investigations grants time-bound, revocable, and audited data access to allow comprehensive investigations by authorized personnel only.

Agent management capabilities: Deploying agents to laptops is only the first step in the process; the information security team needs to ensure that these agents are up to date, receiving the correct policy pushes in a timely manner, and reporting in as expected. This labor intensive task often requires manual intervention to machines, taking time from the security team’s schedule that could be better used investigating serious threats.

The Reveal agent is self-auditing to minimize management overhead. The state of the health of various components of agents across the entire deployment can be easily viewed on a central dashboard for quicker troubleshooting. Performance analysis reports are automatically generated if resource consumption exceeds certain thresholds. Finally, agent diagnostics can be pulled from the management console by administrators without end user intervention further streamlining the task and eliminating the end user from the process.

Learn more about Next Reveal

Mid-market companies need to choose solutions that align with their unique requirements, considering factors such as scalability, cost-effectiveness, ease of deployment, and ongoing support. Finding a solution that can grow with their organization and adapt to changing needs can be a challenge. 
Next Reveal can help. It was designed with today’s technology for today’s threat space. Book a demo or contact Next DLP to learn how your company can deploy a single solution to protect your sensitive data from insider threats and external attacks.


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