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How To Protect and Store Sensitive Data in SaaS Platforms with Cloud DLP
Mega-breaches, or reported breach incidents that impact more than one million records, have increased dramatically. Our analysis shows that, on average, mega-breaches increased 36% year over year since 2016. In total, mega-breach incidents that we analyzed cost at minimum a combined $8.8 billion and exposed 51 billion records.
An important impact of the pandemic has been the rise of the “perimeter-less” workspace. Increased use of cloud platforms like Slack, Google Workspace, and AWS has shifted security from managing endpoints to following data where it is used in the cloud. To that end, organizations must understand how to protect and store sensitive information in SaaS platforms using cloud data loss prevention tools. Cloud DLP is a critical component in preventing and mitigating the risk of mega-breaches.
What are the risks of using SaaS platforms?
Cloud computing is a broad category, which may be why so many organizations struggle to secure their SaaS platforms. Many organizations believe that native security protocols within collaborative platforms like Slack, Microsoft Teams, and Google Suite are sufficient to protect valuable data.
However, while these tools allow teams to be productive, they also tend to create environments where data policies and security best practices can be difficult to enforce. This can cause organizations to not only fail to be compliant with data privacy regulations but also can make them more susceptible to the variety of security threats that can emerge.
There’s been an uptick in organized crime and nation-state actors targeting platforms like Slack, as well as traditional cybersecurity threats from rogue, unaffiliated hackers. These types of threats are outside your company’s control, but still form part of the risk assessment of using a SaaS platform.
More importantly, insider threat is one of the biggest risks of using a SaaS platform to share and store sensitive data. Research by Gartner suggests that through 2025, 99% of cloud security failures will be the customer’s fault. 80% of hacking-related data breaches leverage weak and compromised passwords — a problem that can easily be avoided with password management tools. Through a combination of security misconfigurations, lax security protocols, human error, and insufficient monitoring, data loss is expected to continue to rise year over year.
Organizations must also be cognizant of outdated, misconfigured, or out-of-the-box security protocols— protocols that may not be compliant with HIPAA, FERPA, or other regulatory requirements. In instances where IT professionals need to adapt native cloud security to be compliant with industry regulations, IT teams need to start with a thorough understanding of data architecture. Failure to properly develop data governance can leave information security teams at the mercy of shadow IT and data sprawl, which simply compound the potential security risks of SaaS applications.
How can cloud DLP help?
Cloud data exposures aren’t new, but the high volumes of activity and data in the cloud today make the cost of an error much greater. The risk of a mega-breach can, however, be mitigated through implementing a cloud data loss prevention tool.
Cloud DLP can help teams find the best way to store sensitive data. More specifically, cloud DLP tools help organizations ensure that sensitive data is encrypted before it is transferred to cloud platforms, as well as trust that sensitive information is only sent to authorized cloud systems only. Cloud DLP tools scan and audit data to detect and encrypt PII and other valuable information shared across IaaS, PaaS, and SaaS programs.
Nightfall is a unique cloud DLP tool that leverages AI and machine learning to scan structured and unstructured data to find a range of regulated sensitive information, including: patient names, addresses, medical record numbers, social security numbers, as well as a number of industry codes like ICD, FDA, DEA, NPI, DOB, and more.
Nightfall can help IT teams discover and classify PII, PHI, and PCI that must be protected and take immediate steps to remediate issues by notifying admins or quarantining or deleting data. Nightfall can filter data streams to restrict suspicious or unidentified activity, log data for incident response and auditing, and pull everything together to help you prevent customer data from falling into the wrong hands.
Without cloud DLP, organizations are at risk for a data breach — or worse, an undetected data leak. Implementing Nightfall allows IT teams to spend less time monitoring for policy violations and avoid alert fatigue, using automatic workflows that reduce mean time to resolution.
How to implement cloud DLP
There are dozens of cloud DLP tools on the market, but Nightfall is one of the easiest DLP solutions to set up. Nightfall integrates directly via API – meaning that customers are typically up and running within a few minutes. For SaaS apps like Slack, Confluence, and GitHub, there’s no additional configuration or setup required beyond installation.
Check out our resources for developers to learn more about implementing cloud DLP for your business. And, to learn more about Nightfall, set up a demo using the calendar below.
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Nightfall is the industry’s first cloud-native DLP platform that discovers, classifies, and protects data via machine learning. Nightfall is designed to work with popular SaaS applications like Slack, Google Drive, GitHub, Confluence, Jira, and many more via our Developer Platform. You can schedule a demo with us below to see the Nightfall platform in action.
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