Comprehensive data security for modern organizations.

Fortra offers the most comprehensive data security solutions, from endpoint DLP to CASB, with all points in between 

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Data Security

Fortra Data Security Solutions Brochure


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Cloud Data Security Solutions by Fortra

 

Fortra CASB

Get visibility and control over your data in the cloud

With your employees already using cloud applications like Google Drive, Microsoft 365, or Dropbox, extra measures are needed to keep your corporate data safe. With today’s common cloud apps, your employees may upload sensitive files to the wrong places, access cloud apps from risky locations, or download apps or files they shouldn’t.

We can help protect your cloud data by acting like a security guard for your mobile apps, positioned between your employees and the applications they use.

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Fortra ZTNA

Securely connect users to all private resources with zero trust network access

If someone enters your HQ without a keycard, they’re able to freely enter offices, meeting rooms, or even private storage areas without being checked again. That’s how traditional network security works.

ZTNA is like adding a security guard for every room.

Even if someone is inside the building, with ZTNA they will still have to show ID and reason for being there each time they try a different room and will only have access to those for which they have permission to enter.

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Fortra SWG

Secure web gateway

Fortra SWG protects users, networks, and corporate data from internet-based threats. It inspects all incoming and outgoing web traffic for malicious content and sensitive information.

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Fortra DSPM

Understand and Protect Your Entire Data Landscape

Does your data resemble a messy digital office, with files scattered across different cloud apps and databases?

Fortra Data Security Posture Management (DSPM) serves as a security system for your company’s cloud data, protecting commonly used apps like Google Drive, AWS, and Microsoft Azure.

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Fortra DLP

High-Powered SaaS DLP, supported by experts who have your back

We know data loss prevention can be complex... but it doesn't have to be.

Our market-leading DLP is backed by decades of focus, solving real use cases. From data protection solutions for cloud and hybrid environments to remote work endpoints, we help protect workflows across your entire organization.

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Case Studies: Fortra Data Security Solution in Action

Discover how organizations worldwide rely on our data security software to safeguard sensitive information, backed by proven case studies. 

Defending against a $400M intellectual property breach

When an insider stole valuable IP and put millions at risk, a global manufacturer decided to overhaul its data security. By using detailed, role-based controls, they protected key IP worldwide without disrupting teamwork or productivity. 

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Protecting call center data from endpoint to cloud

A global workforce management company needed to control sensitive customer data across more than 7,500 endpoints. Our endpoint-to-cloud protection kept PII and source code safe and allowed for quick setup with a SaaS solution. 

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Simplifying patient data security with DLP and EDR

A healthcare analytics company had to meet strict partner and regulatory rules, but couldn’t add more people to its small security team. With fully managed DLP and EDR, they quickly secured PHI and IP, easing the team’s workload.

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Ready to Get Started?

Request a demo of our data security solutions, or connect with an expert to discuss your needs.

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Data Security Software Resources

FAQs about Data Security Software

To protect sensitive customer data, first determine where it is stored, how it moves, and who can access it within your organization. Good data security means reducing risks, implementing strong controls, and monitoring activity, even as your systems become more distributed.

Most organizations use a combination of tools to protect sensitive information, regardless of where it is stored.

  • Data security posture management (DSPM) software provides visibility into where sensitive data is stored, how data is classified, and whether data is overexposed or misconfigured in cloud and SaaS environments.
  • Data loss prevention (DLP) tools prevent sensitive data from being exposed, shared, or transferred improperly by enforcing policies across endpoints, email, cloud applications, and networks.
  • Data classification tools identify and label sensitive data, such as personal, financial, or regulated information. This enables organizations to apply appropriate protections and comply with regulations more effectively.
  • Cloud data security solutions address risks specific to cloud platforms by monitoring access, configurations, and data usage across multi‑cloud and hybrid environments.
  • AI data security solutions applies multiple layers to detect threats, protect systems, and enable rapid response. 

Steps organizations take to make their data more secure:

  1. Identify and classify sensitive data across devices, SaaS platforms, and cloud systems to establish a foundation for consistent protection.
  2. Implement access controls and enforce policies, often with DLP and access management tools, to ensure only authorized users and systems access sensitive data.
  3. Use encryption to protect data both when it is stored and when it is being sent. This reduces risk even if someone accesses or moves the data improperly.
  4. Monitor user and system behavior to detect anomalies, including insider threats, fraud, or misuse involving AI tools.

As organizations use more AI, automation, and cloud workflows, data security software becomes more important for managing risks and catching early signs of fraud or misuse involving sensitive data. Modern data security software combines these features, making it easier to protect customer data, even as systems become more spread out and complex. 

A prompt and organized response after a breach is critical to limiting damage and restoring trust. Many organizations rely on data security services or incident response plans to guide their actions. 

Step 1: Contain the incident immediately 

Immediately stop ongoing data leaks by isolating affected systems, disabling compromised accounts, or blocking suspicious access. For example, if unusual downloads are detected from a SaaS application, suspend the account and block access from the associated IP address. 

Step 2: Assess what data was exposed 

After containing the breach, determine what data was accessed, altered, or taken, and establish the timeline. For example, review file access and audit logs to identify if customer records, financial data, or regulated information were viewed or downloaded. 

Step 3: Preserve logs and evidence 

Preserve all relevant logs, alerts, and system snapshots to support investigations, legal reviews, and audits. For example, authentication logs, cloud access records, and endpoint activity data can help determine the breach's cause and severity. 

Step 4: Investigate the root cause 

Security teams must identify how the breach occurred, whether through stolen passwords, system errors, insider actions, or malware. For example, correlating unusual login activity with file access may reveal the use of stolen credentials to obtain sensitive data. 

Step 5: Notify required stakeholders 

Once you know the scope and impact, tell your internal leaders, legal and compliance teams, and any outside parties if the law or contracts require it. For example, if personal data is involved, you may need to report to regulators and notify affected customers within certain time limits. 

Step 6: Strengthen controls to prevent recurrence 

The last step is to fix any weaknesses found during the incident and update your data security practices. For example, you might tighten access controls, improve monitoring, or update training to help prevent similar problems in the future. 

By following these steps, enterprises and organizations can respond quickly to breaches and improve their long-term data protection and incident readiness. 

Use the steps below as a practical guide for responding to and investigating a data security incident. It's normal not to have all the information at first. This approach helps teams work step by step, reduce confusion, and figure out what happened without missing important details. 

1. Establish the incident timeline 

  • Identify when suspicious activity first occurred based on logs, alerts, and user activity. 

  • Determine whether the activity was isolated or sustained over a period of time. 

  • Confirm which systems, users, or applications were involved during the incident window. 

 

2. Review access and authentication activity 

  • Analyze login records to identify unusual access patterns such as new locations, devices, or times. 

  • Check for repeated failed login attempts that may indicate credential abuse. 

  • Validate whether privilege escalation or unauthorized role changes occurred. 

 

3. Examine data access and movement 

  • Review file and data activity logs to determine what data was accessed, modified, or transferred. 

  • Look for abnormal patterns such as bulk downloads or repeated access to sensitive records. 

  • Confirm whether data access aligns with the user’s normal role and responsibilities. 

 

4. Correlate alerts with user behavior 

  • Compare security alerts with actual user actions to determine their significance. 

  • Validate whether system detections correspond to meaningful activity or routine operations. 

  • Use multiple data sources to reduce the risk of false conclusions. 

 

5. Assess intent and root cause 

  • Determine whether the incident resulted from error, misuse, external attack, or internal misconduct. 

  • Identify contributing factors such as misconfigurations, excessive permissions, or missing controls. 

  • Confirm whether similar conditions exist elsewhere in the environment. 

 

6. Document findings and next steps 

  • Record investigation results, evidence sources, and conclusions in a consistent format. 

  • Share findings with security, legal, and compliance teams as appropriate. 

  • Use the investigation outcomes to inform remediation and future prevention efforts. 

An integrated data security tool consolidates access, activity, and alerts, enabling faster and more reliable investigations. 

The most effective data security tools monitor actual data usage, not just rule violations. Modern data protection identifies unusual patterns in access, movement, and behavior to detect risks early. 

Data loss prevention solutions

Data loss prevention tools monitor how sensitive data is accessed, shared, and transferred across endpoints, email, and cloud applications. For example, a DLP tool can alert when an employee attempts to upload sensitive files to an unauthorized external service. 

User and entity behavior analytics 

Behavior analytics tools establish baselines for users and systems, then alert you to unusual activity. For example, they can detect if a user suddenly downloads significantly more data than usual or accesses data outside their normal scope. 

Cloud data security and CASB

Cloud data security and CASB tools provide visibility into data activity across SaaS and cloud platforms. For example, they monitor third-party app access, identify risky configurations, and detect unusual downloads from cloud storage. 

Data security posture management tools 

DSPM tools identify where sensitive data is overly accessible and track associated storage and access risks. For example, a DSPM tool may reveal that sensitive data in a cloud bucket is accessible to more users than necessary. 

Centralized investigation and response platforms 

Centralized platforms aggregate alerts, activity logs, and context to enable faster investigations. For example, security teams can review authentication events, data access, and alerts together to quickly assess whether activity is suspicious or routine. 

Together, these features form the foundation of modern data security, particularly in cloud and hybrid environments where data move frequently, and traditional methods are insufficient. 

Healthcare and financial organizations require data security software that meets strict regulatory requirements while remaining practical for daily operations. The best solutions balance compliance, visibility, and risk reduction in complex environments. 

Data security software for healthcare compliance 

Healthcare organizations must protect patient data while also supporting clinical work and wide system access. Good data security in healthcare keeps sensitive records safe without getting in the way of care. 

Key capabilities typically include: 

  • Data discovery and classification to identify protected health information across systems. 

  • Controls aligned with HIPAA requirements for access, monitoring, and auditability. 

  • Detailed logging and reporting to support audits and breach investigations. 

  • Protection for hybrid environments, including on-premises systems, cloud platforms, and SaaS applications. 

 

Data security software for finance and financial services 

Financial institutions operate under strict regulations and face significant risks from fraud and data misuse. Data security software for finance emphasizes access control, activity monitoring, and regulatory compliance. 

Common requirements include: 

  • Discovery and classification of financial, personal, and transactional data. 

  • Controls aligned with PCI DSS and other financial data protection regulations. 

  • Continuous monitoring to detect suspicious access or data movement. 

  • Reporting and retention capabilities that support regulatory exams and internal audits. 

When selecting data security vendors, organizations in both industries seek solutions that support compliance, enhance visibility, reduce risk, and perform effectively in hybrid and cloud environments. 

Effective data security is an ongoing effort that evolves with changes in data, users, and technology. Leading programs treat data protection as a shared responsibility and emphasize visibility, control, and continuous improvement. 

Make data ownership and accountability explicit 

Organizations with strong data protection practices assign clear responsibility for data security decisions and outcomes. This ensures policies are followed and risks are not overlooked between teams. For example, assigning ownership of customer, employee, and financial data aligns protection efforts with business objectives. 

Know what data you have and where it’s stored 

You cannot protect data you do not know exists. Ongoing data discovery and classification help teams identify where sensitive information is stored across devices, cloud platforms, and applications. For example, knowing the location of regulated or confidential data enables stronger controls and faster response to suspicious activity. 

Limit access based on actual need 

Giving people only the access they need lowers the risk of misuse and insider threats. Access should match job roles and change when responsibilities change. For example, taking away broad access to sensitive data after a project ends helps prevent unnecessary exposure. 

Monitor data activity, not just systems 

Maintaining logs and monitoring data access is essential for detecting abuse, errors, or compromised accounts. For example, tracking unusual downloads or repeated access to sensitive records can provide early warning of larger issues. 

Data environments continually evolve with new cloud tools and business processes. Regular reviews ensure data security practices remain effective. For example, updating controls after adding new SaaS applications helps prevent security gaps. 

Organizations that adopt these best practices are better equipped to protect sensitive information, meet compliance requirements, and respond promptly to emerging risks.