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Shadow Data

Shadow data is any data that is created, stored, or shared outside of an organization's formal IT environment and management policies.

Wiz Experts Team
6 min read

What is shadow data?

Shadow data is any data that is created, stored, or shared outside of an organization's formal IT environment and management policies.

Where shadow data comes from

Shadow data often comes from well-intentioned actions, such as:

  • Troubleshooting: A developer might download a data set to troubleshoot an issue, intending to delete it post-use but then forgetting to do so in the rush of project deadlines.

  • Collaboration: Teams might share project files on a cloud service that’s not officially sanctioned to streamline collaboration, creating data that's not monitored by IT.

  • Legacy data: Old data left on a server after a project concludes can turn into shadow data, lying dormant and unmonitored.

  • Third-party tools: Usage of external tools and applications not approved by the organization can generate data outside their official purview.

Over time, all of this accumulates, forming a reservoir of data beyond the sanctioned boundaries of the organization's IT infrastructure. Unlike the structured, well-managed data that flows through approved channels, shadow data is more chaotic:

  • Unstructured flow: Like an untamed river, shadow data flows outside defined pathways, potentially carrying along sensitive information into unauthorized domains.

  • Gray area: Shadow data embodies a realm of data management where standard data policies may not reach.

  • Security risks: Due to being unmonitored, shadow data can harbor risks, including potential security breaches and compliance violations.

The existence of shadow data indicates the necessity for robust data governance and security measures. It underscores the importance of having comprehensive data policies that not only address the official data channels but also where such data may traverse unchecked.

Shadow data vs. shadow IT

Though they might sound like distant cousins in the context of IT governance, shadow data and shadow IT are two distinct entities, each with its unique set of challenges.

Shadow IT 

This encompasses employees utilizing unapproved gadgets, applications, or cloud solutions without the consent or awareness of the company's IT team. It's like the Wild West of technology within the corporate walls, where employees go rogue with unsanctioned apps and devices to get their tasks done faster or according to their personal preferences. 

Shadow IT can lead to:

  • Security risks: It opens the door to potential security vulnerabilities and compliance violations since these tools aren't under the organization's security umbrella.

  • Loss of control: The organization loses control over data management and security whenever Shadow IT is in play, which can lead to data breaches and other security issues​.

Shadow data

Conversely, shadow data encompasses information generated, housed, and overseen beyond an organization's sanctioned IT framework. It’s the unauthorized data trail that often goes unnoticed, but its implications are far from negligible:

  • Informal data handling: This includes data handled through unapproved third-party applications, personal devices, or even old data sets left to gather digital dust on forgotten servers.

  • Security threats: Similar to shadow IT, shadow data poses risks like data breaches, compliance violations, and potential reputational damage to the organization (more on that in the next section).

The rogue tech toolkit of shadow IT often paves the way for creating shadow data, yet it can also exist independently of shadow IT. The dynamics between these two represent a complex challenge for organizations aiming to uphold robust IT governance and data security frameworks. Addressing one without the other is like fixing a leak while the faucet is still running.

Security implications of shadow data

The risks posed by shadow data are manifold and can significantly impact an organization's security posture and compliance status.

Management hurdles

Shadow data often exists outside the purview of traditional IT governance and security measures, making it a significant blind spot for organizations. It's tricky to track, control, and protect due to its clandestine nature​​.

Data leaks and breaches

Shadow data risks unauthorized access to data, especially when it includes personal customer information, financial data, or other forms of sensitive information​​. Such events can consequently lead to significant negative impacts, ranging from monetary setbacks and reputational damage to potential legal ramifications.

Increased data attack surface

The ease of spinning up new data storage assets without consulting security or IT personnel, especially in cloud environments, exacerbates the shadow data problem. This, in turn, increases the data attack surface and makes traditional data security strategies less effective​​.

Compliance challenge

Shadow data leads to compliance challenges, particularly when it contains regulated or sensitive information. It can cause organizations to fall foul of data protection laws and industry regulations, resulting in hefty fines and reputational damage​​.

Shadow data examples in the real world

Below are some real-world scenarios illustrating how shadow data can create significant problems for organizations.

Nissan

In a disclosure made in 2022, Nissan North America unveiled a data breach episode where the personal information of nearly 18,000 customers was left exposed due to a third-party mishap. This situation arose when customer data was shared with a third-party software development company for testing purposes. However, a misstep led to the temporary storage of this data in a public cloud, where it stayed from June 21, 2022, until its discovery on September 26, 2022. 

The data of 17,998 North American customers ended up being compromised, including their names, dates of birth, and NMAC account numbers. 

Nissan consequently fortified its data security measures to avoid such occurrences in the future. It collaborated with a third-party contractor to rectify the misconfiguration in the public cloud repository and addressed other security loopholes.

Roblox Developers

Unveiled on July 18, 2023, a data breach incident impacted about 4,000 accounts belonging to individuals who participated in the Roblox Developer Conference from 2017 to 2020. The breach, attributed to a "third-party security issue," exposed various personally identifiable information including phone numbers, usernames, IP and email addresses, and even physical addresses. 

Although the breach didn’t compromise financial information, it elevated the risk of targeted phishing attacks against the affected developers. 

This episode highlights the broader challenge of managing shadow data, which, if not handled or secured appropriately, could lead to similar security incidents. This is especially true when it involves legacy data, data residing in testing environments, or orphaned backups.

Best practices for minimizing the risk of shadow data 

Organizations should follow a few best practices for minimizing the risks associated with shadow data.

Maintain visibility and awareness

Having clear visibility into every cloud-based environment and software-as-a-service (SaaS) application that potentially houses an organization’s sensitive data is pivotal. This awareness acts as the initial stepping stone toward reining in shadow data. 

Know where your data resides, including unofficial channels that often go unnoticed.

Control data access privileges

Having a tight rein on who gets to create and access shadow data is a crucial mitigation measure. Implementing stringent access control measures, setting a baseline for typical access for privileged users, and implementing alert systems for deviations can significantly curb the inadvertent creation of shadow data. 

Utilizing machine learning analytics to ascertain which data is crucial for business and who can access it is also beneficial.

Implement regular auditing and monitoring

Maintaining a continuous vigil on unusual behavior is key, as many threats associated with shadow data manifest through atypical behavior patterns. 

Detecting multiple failed login attempts or irregular data access and sharing patterns can be instrumental in identifying and mitigating risks tied to shadow data.

Employ essential security measures

Adopting fundamental security measures like virtual private networks (VPNs), multi-factor authentication (MFA), antivirus software, encryption, backup solutions, and patch management can bolster your shadow IT policy. These ensure that data, official or shadow, remains secure. 

Embracing a zero-trust security model, which mandates verification of each user before they connect to the network, can also be a significant step toward minimizing shadow data risks.

Final thoughts

By understanding shadow data's origins, recognizing its presence, and employing best practices to manage it, companies can significantly mitigate the risks associated with shadow data, ensuring a more secure and compliant data environment. 

This is where Wiz's data security posture management (DSPM) solution comes in. 

Data Security Posture Management (DSPM) tools help mitigate shadow data by:

  • Discovering and classifying all data assets. DSPM tools can scan cloud environments and on-premises datastores to locate and catalog all data assets, including shadow data. This visibility into all data assets is essential for understanding the data landscape and implementing appropriate security controls.

  • Identifying sensitive data. DSPM tools can identify sensitive data across all data assets. This identification allows organizations to prioritize security efforts and focus on protecting the most valuable data.

  • Monitoring data access and activity. DSPM tools can monitor data access and activity across all data assets. This monitoring helps to identify and mitigate unauthorized access and other data security risks.

  • Providing insights into data security posture. DSPM tools provide insights into data security posture by aggregating data from various sources, such as data discovery, classification, access monitoring, and vulnerability scanning. These insights help organizations to identify and address data security gaps and risks.

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