Video: How Bluecore protects PII, secrets, and credentials on Slack, Jira, and 1,500+ GitHub repos. Watch now ⟶

Data streaming from above in gold light
Blog 4 min read

How To Manage The Hidden Impacts of Data Leak With Cloud DLP

by Emily Heaslip Published Jan 11, 2022

Data leaks are a type of data loss threat that often fly under the radar — making them potentially more damaging than a malware or ransomware attack. Compared to data breaches, data leaks put customer information at risk accidentally. Data leaks can lead to credit card fraud, extortion, stolen IP, and further attacks by cybercriminals who seek to take advantage of security misconfigurations. 

According to one study by the University of Michigan, people were not aware of 74% of the breaches in which their personally identifiable information (PII) was accessed. Businesses aren’t doing enough to scan for and prevent data leaks, alert customers when something does go wrong, or implement data loss prevention tools to prevent leaks from worsening into breaches. Here’s how businesses can better serve customers and mitigate the increasing risk of data leakage threats. 

Data leak vs. data breach: what’s the difference?

Data leaks and data breaches are two threats under the broader category of data loss. A data breach takes place when sensitive information is accessed by an unauthorized party or hacker. A business is targeted by cybercriminals who use a combination of social engineering, malware, and/or ransomware to steal valuable information. Data breaches are expected to cost $6 trillion annually in 2021, doubling from $3 trillion in 2015. 

In contrast, a data leak is the unintentional exposure of sensitive information, such as an employee accidentally leaving an unlocked company smartphone containing IP on the subway. A data leak typically pertains to either data at rest or data in transit and could take place on the internet or on physical devices.  

The key difference between these two root causes of data loss is that data leaks are typically accidental. Data leakage threats are caused by security vulnerabilities, such as misconfigurations or user error. Data breaches are initiated by attackers. However, data leaks can quickly turn into data breaches. If a cyberattacker uncovers a data leak, they can further exploit the vulnerability to gain deeper access to important data. 

Common types of data leaks

Data leaks are usually unintentional — which makes them difficult to try to prevent. Some of the most common causes of data leaks include 

  • An accidental action by an employee — or insider threat 
  • A software misconfiguration
  • A system error
  • Poor, outdated, or incomplete data security practices

[Read more: Insider threat is one of the biggest challenges in cloud security]  

Data leaks are often more difficult to prevent than data breaches. This is because there are practically infinite ways for data to be improperly shared: an employee could misplace a USB with sensitive data, send an email to the wrong recipient, or leave a laptop on without password protection. Or, IT misconfigurations could lead to excessive permissions for files with sensitive information, vulnerabilities in third-party integrations, or outdated security settings. 

Insider threats can be especially difficult to deal with because they can stem from unexpected sources. Malicious insiders specifically might have knowledge of organization-wide security measures as well as physical access to hardware, making them especially difficult to prevent. These types of data leakage threats can be particularly dangerous if undetected — and lead to hidden impacts with serious consequences.

What are the hidden impacts of a data leak?

Perhaps what makes data leaks so alarming is that they often go undetected. Especially in instances of insider threat, a company may not realize that sensitive information has been exposed until a breach happens. Take, for instance, the July 2019 Democratic Senatorial Campaign Committee leak. Researchers discovered an unprotected S3 Bucket containing a spreadsheet titled “EmailExcludeClinton.csv” which was uploaded in 2010. The spreadsheet contained the email addresses of over 6 million Americans. It’s unclear how long the bucket was exposed for or if anyone had accessed the data. 

Unfortunately, most data leaks are detected when cybercriminals come across important data — turning the leak into a full-blown breach. The are both direct and indirect costs associated with a data breach, costing businesses with fewer than 500 employees an average of  $7.68 million per incident. These costs include legal fees, consumer credit monitoring costs, compensation for impacted customers, and the cost of auditing and correcting the source of the leak. 

There may also be hidden costs impacting long-term profitability/revenue projections. Loss of market share, the inability to retain valuable existing customers, and also the loss of trust that would prevent you from acquiring new customers are all indirect costs with direct business implications. “Forrester estimates that a company can expect to lose up to 20% of their customer base because of a data leak,” said one report. “For a business with net annual sales of $1 billion, with 80% of business coming from repeat customers, it can be devastating.”

How to prevent data leakage

User education is key to preventing the mistakes and insider threats that can lead to data leakage. However, accidents happen: and businesses need a failsafe for instances when proprietary data may be exposed.

Data loss prevention plays a critical role in preventing data leaks. Businesses that leverage cloud-native data loss prevention can detect and classify business-critical data in SaaS applications and cloud infrastructure. 

Nightfall is a cloud DLP solution that specifically uses machine learning detectors capable of object character recognition (OCR) and natural language processing (NLP) to classify strings, files, and images that contain PII, PHI, and a wide variety of sensitive data. Nightfall can continuously scan your cloud programs to detect instances where data may be shared inappropriately. We implement rules that let you monitor who has access to this data and control when to redact or remove it. 

To learn more about Nightfall, set up a demo using the calendar below. 

Subscribe to our newsletter

Receive our latest content and updates

Nightfall logo icon

About Nightfall

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.

 

Schedule a Demo

Select a time that works for you below for 30 minutes. Once confirmed, you’ll receive a calendar invite with a Zoom link. If you don’t see a suitable time, please reach out to us via email at sales@nightfall.ai.

call to action

See Nightfall in action.

Schedule a demo