Data Security for Software Companies: Protecting Sensitive Information Using a Modern Approach
Most of today’s global software companies have a cloud-first philosophy, with data stored across many different SaaS applications and public cloud providers. These data-driven software companies encourage employees to use data by making it easy to access. But this leads to more data in the form of Tableau dashboards, spreadsheets, and other files.
The problem is that cloud-first software companies are generating massive amounts of data and storing it in so many different locations that it becomes difficult to manage. This data sprawl also makes it harder to identify and secure sensitive information like source code, product roadmaps, collaborative documents, and customer data.
In this post, we’ll discuss four types of sensitive data in the software industry, and why software companies need to protect their data using a modern data security approach.
4 Types of Sensitive Data for Software Companies
Most software companies have sensitive data in the form of intellectual property. In this way they are just like pharmaceutical and semiconductor companies. Industry-aligned software companies, think fintech or biotech, also have sensitive financial or health data that increases security and compliance risks. This makes data security an important consideration for software companies.
Here are four types of sensitive data that software companies need to protect.
Software companies store source code on GitHub, GitLab, in S3 Buckets, and many other locations. Development teams often choose their own tech stacks. This spreads source code for dozens of programming languages across multiple locations. The code consists of many different file types, depending on the language or application. So overall, this data security challenge can quickly become overwhelming if it’s not managed properly.
In the software industry, protecting source code is important because it’s proprietary information and often a competitive advantage. Software companies in highly-regulated industries like financial services and healthcare also need to keep their source code protected to prevent malicious actors from discovering potential vulnerabilities.
Software companies continue developing their products after release to better meet customer expectations. Product teams design a vision for the product based on customer feedback, which usually includes a product roadmap with development timelines, release schedules, and feature updates. This product roadmap contains valuable strategic information and innovation ideas that guide the direction and evolution of the product, drive decision-making, and help the software company stay competitive in the market.
Documents related to the product roadmap are sensitive. They are worth protecting because they reveal the software company’s future initiatives. If the product roadmap gets leaked, then competitors can bring upcoming features to market faster. This means confidentiality is important for any files that contain product roadmap details.
Many collaborative documents stored on Google Drive or SharePoint contain sensitive data like intellectual property, trade secrets, or other proprietary information. This could include competitive intelligence on features or market research that marketing and sales teams use. Feedback from beta testers — who are often early adopters of new features — also contains valuable information that competitors shouldn’t have access to.
Protecting collaborative documents is crucial for maintaining confidentiality, safeguarding intellectual property, and complying with regulations. Software companies should prioritize security measures for shared drives and collaborative SaaS applications to prevent unauthorized access or exposure of confidential information.
Most software companies collect customer data to improve the user experiences for their products. This includes who is using which features, any issues or support tickets, feature requests, and much more. Software companies use customer data to understand potential bugs in their products and areas to improve.
Customer data can contain sensitive information and is largely meant for internal use by certain employees. This means access to customer data should be restricted to only those who need it, such as product managers, customer support representatives, or data analysts.
In addition, customer data could contain personally identifiable information (PII), which might be stored as part of user registration, authentication, or payment processing. This poses a security risk if there aren’t adequate measures in place to minimize the impact of a data breach.
The Need for a Modern Data Security Approach
Leading software companies leverage the cloud to scale their apps and support a growing user base. At the same time, they have rapid development cycles that lead to constant source code changes and software releases. Since data is constantly changing, software companies require a faster and more intelligent approach to data security than what legacy solutions can provide.
Here’s a few reasons legacy security solutions are incongruent to software company’s cloud-first approach:
- Data Discovery: You need to manually identify the datastores to scan for. These include cloud storage buckets, databases, containers, virtual machines, and SaaS applications. Discovering data becomes a bottleneck for global software companies that have hundreds of datastores.
- Data Classification: You need to write regex policies to classify the data. This is a problem because the process is manual, requiring tuning to ensure data is accurately classified. Regex-based policies rely on basic formats and patterns to classify data. This means they are unable to distinguish the difference between a 9 digit employee ID number and 9 digit Social Security number.
- Data Context: You don’t get a lot of context about the data. There’s no information about the data subject role of the data. It doesn't tell you about the environment that houses the data or the relationship of data within the same table.
- Data Loss Prevention: You can’t write enough rules to prevent sensitive data from leaving the environment. Legacy DLP solutions are rule-based, leveraging out-of-date and incomplete data classifications to prevent sensitive data from leaving the environment. Because of the velocity of data growth and variety of data types that software companies possess, legacy DLP solutions simply cannot keep up.
- Incident Response: You don’t get actionable advice for remediating data security vulnerabilities. Visibility without action is not really useful for security teams trying to reduce the data attack surface.
In short, legacy data security solutions are slow and require a lot of manual effort to implement and maintain. They also rely on pattern matching, signatures and static rules, so they lack any understanding of your data. This means they can’t scale to meet the needs of today’s increasingly diverse and unique data environments that cloud-first software companies depend on. Instead software companies need a cloud-native data security solution that can protect data across different cloud platforms, container environments, virtual machines, and more.
Data Security with Cyera
Cyera’s data security platform provides deep context on your data, applying correct, continuous controls to assure cyber-resilience and compliance. This modern approach to data security can help cloud-first software companies protect their source code, product roadmaps, collaborative documents, customer data, and other sensitive information.
Cyera automatically learns, classifies and contextualizes massive amounts of data to create an inventory of sensitive information. The platform can continually discover data across IaaS, PaaS, and SaaS environments, which helps software companies gain a holistic picture of their complex data infrastructure. A deep understanding of exactly what the data represents also allows Cyera to classify new and existing data more intelligently and accurately.
In addition, Cyera can validate the controls around the data and highlight potential data security issues. This includes identifying sensitive data that is stored in plaintext, exposed to the public, has overly permissive access rules, or poses a security risk in other ways. Security teams can use these insights to improve their cloud data security posture. The platform also highlights real-time security exposures, misconfigurations, or misuse to stop data breaches as the actions are taking place.
Cyera takes a data-centric approach to security, assessing the exposure to your data at rest and in use and applying multiple layers of defense. Because Cyera applies deep data context holistically across your data landscape, we are the only solution that can empower security teams to know where their data is, what exposes it to risk, and take immediate action to remediate exposures and assure compliance without disrupting the business.
See what data classes and context Cyera can reveal about your environment by scheduling a demo today.