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Secure data and cloud apps everywhere
As companies continue to remodel their culture for a hybrid working environment, a data and SaaS security strategy based solely upon a controlled office-based network environment no longer makes sense.
Securing data and the SaaS ecosystems has become more challenging in the new normal because employees are no longer bound within the supervised environment of their corporate network, and freely access, create, store and share sensitive business data from cloud applications through their personal networks or from third-party networks elsewhere.
The ever-growing reliance on cloud apps, whether sanctioned or unsanctioned, makes it harder for IT teams to monitor noncompliant data transfers made by employees working remotely. Securing both sanctioned apps that are approved for use by an organization and unsanctioned apps that make their way into the organization without approval is key to building a strong data security defense for every highly distributed cloud-first organization.
254
AVERAGE NUMBER OF SAAS APPS USED IN ENTERPRISES EVERY DAY
56%+
AVERAGE NUMBER OF UNMANAGED SAAS APPS
Data and SaaS security challenges
Your enterprise environment is modern and highly distributed with workers working from everywhere. In this situation, how do you discover unapproved apps that your remote employees are using to share sensitive data when working from remote sites? How do you ensure data is not being illegitimately shared from approved corporate apps to unauthorized sources? And, how do you protect your data, making sure it stays compliant? What enterprises like yours need today is a fresh approach to data and cloud security that stays in lockstep with the skyrocketing growth of SaaS, and the volume, variety and velocity of data within them.
Securing unsanctioned apps
A countless number of SaaS-based cloud applications are available today to get any task done. These can range from note taking apps to file-sharing apps to social media, collaboration tools and many others.
Why?
Companies don’t have a way to monitor and control which applications are being accessed and used and by whom. SaaS apps are growing exponentially in numbers and many of them make their way into user’s hands without the explicit knowledge and approval of IT. Shadow IT apps transfer sensitive data, creating security gaps that put organizations at high risk of a data leak. Shadow IT is a persistent problem because IT departments have no visibility into exactly which applications are being used and what sensitive data is being uploaded or downloaded through them.
Our Approach
We believe a best-of-breed SaaS inline approach is needed for complete visibility and control over all the SaaS apps in use, their shadow IT risks and to intelligently keep up with the unstoppable growth of SaaS. By using advanced risk scoring, analytics, reporting, and security policy rule authoring, your organization has the SaaS visibility and security controls to prevent the data security risks of unsanctioned SaaS app usage on your network.
Securing sanctioned apps
As your sanctioned IT applications are transitioned into the cloud, the risk of compromising sensitive data and propagating malware increases. What this means is that your IT teams lack necessary insights to help determine if your organization is at risk for any data exposure or compliance-related policy violations.
Why?
Modern collaboration apps like Slack®, Zoom®, Confluence®, Jira® and other sanctioned apps are where users spend most of their time today sharing sensitive information. These are typically not covered by their API protections. Moreover, each sanctioned application has its own settings to secure how users can store and share data – the settings and levels of enforcement vary by application.
Our Approach
A comprehensive SaaS API approach enables you to consistently define and enforce policy for securing data across all your sanctioned SaaS applications. Our API integrations with most collaboration apps identify sensitive data within the context of users’ conversations, thanks to natural language processing and AI-based models.
Overcoming data detection blindspots
Data detection, in order to be highly efficient and therefore best-in-class, must leverage a variety of detection techniques to identify both structured and unstructured data.
Why?
If a DLP solution can’t discover all sensitive data in a highly reliable way, its outcomes would matter very little because it would offer only partial protection, and most importantly, cause great amounts of false positives. False positives can interfere with standard business processes and cause frustrating and time-consuming incident triage processes for the incident response team. A false positive could also prevent a data exchange among legitimate users that doesn’t need to be stopped. A mediocre DLP solution that doesn’t offer accurate detection and creates too many false positives is not worth the investment.
Our Approach
We believe data detection, in order to be highly efficient and therefore best-in-class, must leverage a variety of detection techniques, such as powerful cloud detection engines, descriptive data profiles, data matching, and image recognition to identify both structured and unstructured data.
Managing risky user behavior
Monitoring remote employee behaviors that pose risks to sensitive data can be challenging for IT teams and hard to control. Any transfer of data due to their noncompliant behavior can result in serious security violations, putting the organization at high risk of data loss.
Why?
Data breaches occur when an ill-intentioned insider from the organization ends up exfiltrating data for personal gain or disruption. Well-meaning yet negligent employees are also an important vehicle for data loss. They can unintentionally expose sensitive data by transferring it through company data loss, as they unintentionally expose sensitive data by transferring it through company-unsanctioned SaaS applications by oversharing it on cloud storage repositories or by sending it to untrusted third parties.
Our Approach
We believe every user must be authenticated to control access to specific applications and data at any given time. Analyzing user behavior and clearly defining and enforcing role-based data access and usage policies pinpoints any anomalous behavior or instances when there are deviations from “normal” patterns of cloud app and data usage.
Legacy CASB and DLP solutions are riddled with pitfalls
When it comes to securing cloud-based applications and the sensitive data that flows through them, security teams have typically turned to cloud access security brokers (CASBs). However, current first-generation CASB solutions are broken due to numerous architectural and operational inefficiencies.
Incomplete app visibility
First-generation CASBs focus on HTTP/S, missing over half of all traffic of non-web applications. They rely only on static databases of application signatures and reactive support requests for app discovery. This approach hinders their ability to identify or contain new SaaS apps before they become a risk. They also lack APIs to secure modern collaboration applications used by the distributed workforce.
Inadequate data protections
Legacy data protection methodologies struggle to keep pace with the volume and sprawl of sensitive data. Their data loss prevention relies mainly on regex-based and traditional data fingerprinting methods, resulting in slow and inaccurate protection. They have not adapted to detect data leakage through modern collaboration apps like Slack, Teams® and Zoom, which use new ways of communicating with short and unstructured messages.
Poor security
A majority of CASB vendors provide very limited efficacy into high-priority threats, unknown malware and breaches – using third-party sandboxing tools as a threat detection method most of the time. Moreover, their inline proxy approach only gives visibility into HTTP/S – leaving customers only partially covered.
Inconsistent coverage
Siloed CASB solutions force organizations to apply different delivery methods and technology to cover HQ, branch and remote workers, leaving huge consistency gaps in protection. Disjointed from the rest of the organization's security infrastructure, they also require network changes and complex deployments.
Next-Gen CASB and Enterprise DLP explained
See how our Next-Generation CASB and Enterprise DLP work to protect sensitive data and cloud apps.
ENTERPRISE DLP
Cloud-delivered data protection
Preventing the risks of data loss and noncompliance in highly distributed modern enterprises is tricky. A modern and comprehensive approach to data loss prevention trumps a conventional and partial one.
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$4.35M
Average total cost of a data breach
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45%
Breaches are cloud based
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19%
Breaches due to stolen or compromised credentials
SAAS SECURITY
Comprehensive cloud app security
Securing SaaS applications, sensitive data and your growing hybrid workforce with legacy, outdated approaches is daunting and riddled with risk. What organizations need today is a Next-Generation CASB as part of their SASE strategy.
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80%
Employees use collaboration SaaS apps
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Up to 99%
Cloud security failures are caused by human error
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470%
YoY increase in SaaS being used to deliver attacks
Secure your data and cloud apps with our solutions
As core elements of Palo Alto Networks SASE, SaaS Security, and Enterprise DLP play a key role in enabling organizations to consistently protect their data, applications, and users across networks and clouds while avoiding the complexity of multiple point products, significantly simplifying adoption and saving resources – technical, human and financial.
Enterprise DLP
The industry’s first cloud-delivered Enterprise DLP that consistently protects sensitive data across all networks, clouds and users.
SaaS Security
See and secure all applications automatically, accurately protect all sensitive data and all users everywhere and prevent all known and unknown threats with the industry’s first-ever Next-Generation CASB fully integrated into SASE.
