SaaS Data Loss Prevention: What is it and Why is it Important?

Updated 1/7/2024

With the rise of cloud-based applications, data loss prevention (DLP) has become an increasingly important part of information security. DLP refers to the policies and technologies used to prevent sensitive data from being lost or stolen. In the context of SaaS, this can include both the security measures implemented by the SaaS provider and the steps taken by the customer to protect their data. In this blog post, we'll provide a more detailed overview of what SaaS DLP is, why it's important, and how you can go about setting up a DLP strategy for your business.

What is SaaS Data Loss Prevention?

SaaS data loss prevention refers to the security controls (configurations, processes, and technologies) you use to prevent potential data breaches within these environments. Cloud-native SaaS DLP like Nightfall uses APIs to connect to SaaS platforms to monitor your security posture in real-time, as well as scanning content continuously for signs of leakage. When human errors occur–like an employee who shares social security numbers or intellectual property in Slack messages, or suspicious activity violates security policy rules–like changing security configurations or sharing sensitive Google Drive or Google Workspace files with external users–Nightfall can automatically take action.

SaaS DLP software like Nightfall AI can help you enforce internal compliance, prevent data exposure, and mitigate the risk of insider threats or breaches in cloud environments.

Why is SaaS DLP Important?

SaaS DLP solutions are important for many reasons. First, it can help you comply with regulatory requirements under compliance standards like HIPAA, GDPR, and PCI-DSS. These regulations require businessesvto protect sensitive customer information and PHI from being accessed by unauthorized users.  Data breaches stemming from unauthorized access can have severe consequences for organizations, including financial losses, reputational damage, and legal penalties. DLP for SaaS can help to mitigate the risks associated with data breaches by preventing sensitive data from leaving your environments.

Why is SaaS DLP so challenging?

Lack of visibility into how sensitive data is handled, shared, and used within third-party SaaS apps makes this channel especially challenging for DLP. A tool would need to accurately and continuously detect sensitive datasets, connect them with users' actions, alert on policy violations, and block actions that are simply too risky. However, traditional DLP tools are built to detect structured, predictable datasets that don't align in format to the sensitive data and secrets modern organizations use daily to conduct business– APIs, encryption keys, passwords, and personal data. So organizations' risk profile in unprotected SaaS is incredibly high. At the same time, a breach of protected datasets could result in serious legal repercussions.

CASB and SSE Providers Aren't DLP Specialists

The way many organizations have historically protected cloud storage and covered workspaces is with CASB, which has now been rolled into SASE and SSE solutions. They are primarily focused on advanced threats coming in from the outside, not internal threats to data. Aware that organizations also need to protect data from sprawl and over-exposure, these solution providers often buy a smaller DLP company in order to provide this service for a holistic approach to data protection. However, the detection engines in these add-on DLP solutions tend to be only well suited for structured data, though they offer to detect other data types.

What this means is that common add-on DLP solutions often fail to detect sensitive data in third-party SaaS locations like Slack, Jira, Google Drive, and more.

Native DLP Solutions Aren't Up to the Task of Unstructured Sensitive Data

As a result of the great struggle organizations have to protect data in their SaaS, some software vendors have decided to offer their own in-app DLP solutions. Slack DLP, Microsoft Purview, Google DLP, and Atlassian Guardium are among the native DLP solutions commonly used on the market. Of the four, Purview has the most users and the strongest reputation as a holistic tool, making it a good place to start for Microsoft shops. However, Purview is also incapable of the kind of detection needed in today's fast-growing companies, where API keys, source code, and passwords are passed back and forth among team members during common modern business processes like software development.

AI Detection Matters When Choosing a Strong Solution

Testing is always key to success in the purchase of cloud-based security solutions. Moving beyond claims to put a solution to the test can help you decide what kind of SaaS DLP is right for you. You'll want to understand what processes are being used. For example, if they are relying on regex (regular expressions) and word matching, or proximity algorithms to detect sensitive data, the results are going to be inaccurate. Due to the popularity of claiming to have AI or ML functionality most organizations include this in their marketing material, but the proof is in the pudding, as they say. Be sure your chosen tool can detect secrets and other unstructured data formats like PHI or PII.

Here's how Nightfall AI's SaaS DLP stacks up against native DLP providers:

Inquire About Vendors' Actual AI Practices and Staffing

Another important factor to consider when choosing among AI-capable SaaS data protection solutions is whether or not the organization has a dedicated team of AI engineers working on their detection model continuously to bring it to maturity. The model should be strong enough to begin self-training after some time, which adds an extra layer of learning to detect instances of unauthorized sharing or other risky data handling practices that can lead to cyber security loss incidents. Some organizations outsource the building of their model, then focus solely on sales and marketing staff to get their product to market, rather than investing heavily in the continuing development of their models. This would be a red flag in the evaluation process.

What are the benefits of SaaS DLP?

There are many benefits of using a SaaS DLP solution, including the following: 

Increased Security

• SaaS DLP solutions provide an additional layer of security for critical assets that are especially prone to data sprawl, external threats, and accidental data loss. Nightfall removes this data from vulnerable locations to make it more difficult for malicious insiders or external threats to gain access. SaaS DLP should be used in conjunction with other tools and processes that make it less likely for sensitive data to be easily accessed on SaaS platforms.

Insider Risk Mitigation

Most security practitioners recognize that of all the threats in their attack surface, loss by insiders ranks highest among the most expensive breaches. Yet, the rarity of malicious insiders often makes this a task they seek to tackle later. Using proactive protection measures like SaaS DLP helps teams tackle accidental insiders' mistakes before they can lead to massive losses in an external breach, as well as catch and stop malicious insider activity.

Regulatory Compliance

• Many industries have strict regulations surrounding the handling of sensitive data assets like PHI or sensitive file contents. By using a SaaS DLP solution to enforce compliance, track and remediate rule violations, and enable leakage prevention, organizations can ensure that they are in compliance with these regulations.   

Avoidance of Legal Fines

• Even unintentional exposure of protected data through internal errors can result in hefty regulatory fines and loss of customer trust.        

Real-time Monitoring

• Cloud DLP solutions for SaaS should provide you with active continuous monitoring of all your mission critical apps to catch and remediate instances of accidental exposure that won't be caught by add-on DLP tools for (cloud access security brokers (CASB) or secure service edge (SSE), which are better aligned to external threats to corporate networks–not internal risk mitigation in SaaS and cloud workspaces.   

Catch Anomalous Behavior

• By catching and remediating instances of accidental leakage or noncompliant sharing, you can get ahead of insider risk management. Further, context-based detection will provide you with the user, date and time stamp, content impacted, and even the history of that content via data lineage.

Employee Training

• Using a tool that allows end users to fix their own mistakes helps you reduce likelihood of a breach and partner with employees, who can become your first line of defense in taking action on improving data hygiene and implementing cybersecurity strategies on front lines.

Ease of Use for Effective Data Loss Prevention

• SaaS DLP solutions are typically easy to deploy and manage. Nightfall, for example, is agentless and connects to SaaS Platforms via API in just seconds. The only thing that needs to be configured are the policies you’ll use to find the types of sensitive data that matters to you.

Cost Savings

• Because SaaS DLP solutions do not require extensive tuning and can be automated, there is a great potential for cost savings. If you use a provider whose detection is extremely accurate, you can also reduce total cost of ownership by reducing time your security team spends on pervasive false positives in less mature detection models. In turn, this reduces alert fatigue often caused by insufficient solutions that continuously send them false alarms.

Prevent Potential Security Threats Caused by False Negatives

• Cloud data loss prevention tools that are ill-equipped to handle challenges like complex datasets, unstructured sensitive data, and other instances that need mature AI and ML models.

Improved Productivity for Security Teams

• By automating many tasks associated with data protection, such as identifying, classifying, and remediating sensitive data exposures, SaaS DLP solutions can free up security teams to focus on other tasks.

Business Continuity

Enable business processes to continue at their normal speed, using cloud applications to conduct legitimate activities. Well-designed SaaS DLP solutions should not be heavy or create latency. By using a solution that won't cause disruptions to business operations, allowing employees to fix their own mistakes and sending only unremediated alerts to security teams, you allow user activities to continue as normal. Employees continue to work, security teams don't waste time, and business requirements can be met at pace.

Historical Scans and Baseline Audits

One final factor to consider when looking for a SaaS DLP solution is historical scanning. When you implement a tool that will protect your organization from data sprawl and risky data handling practices, you need to do a little spring cleaning. Using a historical scan to review existing data inside your SaaS can help you find and remediate serious risks you didn't even know you had. This is not the result of inadequate security practices, but typically the result of well-meaning and productive employees. To accomplish daily work, employees often share sensitive data like passwords, API keys, and other secrets, causing sensitive data to end up in shadow IT file sharing applications or websites, SaaS messaging apps, Google Drive files, emails, and more–and email security solutions have the same struggle with detection.

In other words, you need to use a high-powered detector to scan all the mission critical SaaS locations your employees use to accomplish work.

Remediation Gets You Started on the Right Foot

Once you identify and remediate all instances of sensitive data using automated response security workflows, you will know which SaaS are your high-risk applications and need the most focus for leak detection and anomalous activity with regards to data handling behaviors.

Use SaaS DLP to Enforce Compliance, Reduce Insider Risk, and Avoid Major Breaches

Data loss prevention is a vital part of information security in the age of cloud services. With the average cost of a data breach in the U.S. surpassing $9 million last year, it's time to take a 360-degree view of your SaaS DLP strategies. Cybersecurity experts will tell you that insider risk to corporate data within SaaS applications is what gives them the most headaches when it comes to discovery tools. Don't fall prey to the rising cost of cybercrime. Investigate the benefits and performance of flexible, cloud-native, AI-powered SaaS DLP as part of your holistic strategy to protect sensitive data and mitigate the risk of a breach.

By understanding the types of data loss that can occur and implementing appropriate security measures, businesses can protect themselves from costly data breaches. Implementing a SaaS DLP strategy doesn't have to be difficult, simply evaluate your needs based on industry, size, and your current data security coverage.

If you want to test Nightfall AI or just learn more about our offering, you can schedule a call with us anytime, and we can guide you through the core considerations of building a SaaS DLP strategy. Get a Demo Today

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