Key points

• Automated data classification significantly reduces human error and streamlines sensitive data management in SaaS environments, like Google Drive and Slack.
• Automated systems improve data visibility for faster response, better enforcement, and simplified compliance, minimising audit risks
• Strengthen security, build stakeholder trust, and enhance overall data security posture within your organisation with Metomic's automated data classification capabilities
• To find out more about automating security withiin your SaaS, request a demo with one of our security experts today.

Data classification is crucial for IT and security teams to effectively identify and manage sensitive data. However, manual classification is often labor-intensive and error-prone, leaving organisations vulnerable to data breaches, compliance failures, and operational inefficiencies. Automated data classification offers a streamlined, efficient solution, enhancing visibility, and enabling swift application of security controls.

This article explores how automated data classification simplifies data management in SaaS environments and strengthens your organisation's security posture.

The Power of Automated Data Classification

Data classification involves organizing and labeling sensitive information for better management and security. Effective classification allows for targeted security measures and faster threat responses. However, incorrect classification can lead to substantial financial losses, as evidenced by the average cost of a data breach reaching $14.82 million.

Automated data classification mitigates these risks by using predefined rules and machine learning algorithms to categorize data as it's created or updated, ensuring consistency and accuracy across your SaaS applications.

1. Manual vs Automated classification

Manual classification requires your team to manually review and label data based on sensitivity. While this can be effective for small datasets, it’s labour-intensive and prone to human error.

In contrast, automated data classification software uses predefined rules and machine learning algorithms to automatically categorise data as it's created or updated. Leveraging automation reduces the chance of human error and ensures more consistent labeling is in place across the board.

2. Predefined labels vs Custom labels

Security professionals have the option of choosing between predefined or custom labels. Predefined labels often include categories such as, "Confidential," "Public," and "Restricted," while custom labels can be tailored to your organisation’s specific needs.

One of the main benefits of predefined labels is that they are quicker to implement and help standardise classification across teams overall. However, while custom labels may require more time to define , they allow more flexibility and can be designed to align with your company’s unique security and compliance requirements.

Developing a comprehensive data classification framework can be a lengthy process. By starting small and expanding, organisations can reduce the risk of data leaks and human error during implementation. Implementing third party solutions like Metomic can help speed up this process through flexible, automated workflows. Book a personalised demo today to see how we can help strengthen your company’s data security.

3. Automated Data classification in SaaS environments

Automated data classification is essential for protecting sensitive information in SaaS applications like Google Workspace and Slack. These tools enable seamless collaboration, with vast amounts of information shared on them daily.

While restricting data-sharing might seem like a good solution, doing so will only disrupt your team’s productivity.

Luckily, these widely used platforms often support both manual and automated classification, which makes it easier to categorise data as it’s created or shared within the environment.

For example, you could apply labels like "Confidential" or "Restricted" to documents, emails, or files in Google Drive or designate sensitive channels in Slack as "High Risk."

Security teams can then use these labels to identify which data needs additional protection or monitoring. This allows for swift action when a potential risk is detected, such as triggering alerts when sensitive data is shared outside the organisation.

While native tools provide some level of control, they often lack scalability. As organisations adopt more SaaS applications, managing classification and enforcement across multiple platforms become increasingly complex, making it essential to have a centralised data classification solution—like Metomic —for classifying data and setting security rules in one place.

As SaaS tools continue to evolve, automation is becoming a key trend. In 2024, 36% of SaaS management tasks were automated, a rise of 4% from the previous year. This shift towards automation makes it easier for organisations to maintain accurate data classification—as well as saving a lot of time—across all of their SaaS tools, ultimately enhancing their data security posture.

How does automated data classification provide better sensitive data visibility?

Automated data classification provides a clear overview of sensitive data storage, movement, and access.

By automatically labeling data based on sensitivity, security teams can quickly locate and track critical information, reducing the risk of mishandling.

1. Faster incident response and fewer security blind spots

With 30% of cloud assets containing sensitive data, lack of visibility can have a severe impact. Proper classification ensures immediate awareness, making data easier to monitor and manage, ultimately making threat detection and response time much faster.

Rather than sifting through vast amounts of unstructured information during an incident, security teams can hone in focus on high-risk areas.

2. Better monitoring and policy enforcement

Classification helps enforce security policies by automatically applying protections based on data type and risk level. For example, documents labelled as "Confidential" can be restricted from being shared externally, while "Internal Use Only" files may trigger alerts if accessed by unauthorised users.

These controls ensure that sensitive information remains protected without relying solely on employees to follow security protocols manually.

🎙️Interview: Everything You Need To Know About Automated Data Classification

In this interview with Metomic's VP of Engineering, Artem Tabalin, we dig deep into how data classification can transform your business' data security

What risks and vulnerabilities can security teams monitor and mitigate effectively with data classification?

Poor data visibility leaves organisations open to risks from accidental exposure to compliance violations. A lack of clear classification can make it difficult for security teams to track sensitive data increasing the risk of unnoticed data breaches.

1. Identifying and prioritising risks

Data classification helps security teams focus their efforts where they matter most. By labelling sensitive data based on its level of risk and location, teams can apply stricter controls to high-risk assets while also allowing lower-risk data to be managed with fewer restrictions. This targeted approach prevents overexposure and makes security processes more efficient.

2. The impact of poor visibility

Misclassified or unclassified data is a major contributor to security incidents. In the public sector, 63% of organisations that don’t classify data at creation take weeks or months to detect misuse. In contrast, 67% of organisations who have implemented data classification processes can spot abnormalities within days or even minutes. This contrast highlights how data classification has a direct impact on response time significantly reducing the window of opportunity for attackers.

3. Learning from data breaches

Several high-profile data breaches, including those involving AT&T, Marriott, and Samsung, have been caused by poor data visibility. Misplaced sensitive files, mislabeled customer records, and unrestricted access to confidential data have all contributed to serious security incidents.

By implementing clear classification policies, organisations can minimise these risks and strengthen their overall security strategy—potentially saving millions, as companies using security AI and automation, including data classification, reduce breach costs by an average of $2.22 million.

Ready to improve your data visibility and security? Book a personalised demo today to see how data classification can help your team identify and mitigate risks effectively.

Automated data classification: Secure, compliant, trusted

Data classification plays a crucial role in strengthening your organisation’s security posture, ensuring compliance with various regulations, and fostering trust with stakeholders.

Here’s how it works:

1. Maintaining a strong security posture

Data classification helps organisations identify and protect sensitive data. By categorising data based on its sensitivity, businesses can apply appropriate security controls, reducing the risk of breaches.

For example, as of 2024, GDPR-related fines have reached nearly €5 billion, with a significant portion of these penalties stemming from companies that failed to protect personal data adequately. This highlights the importance of having proper classification in place to prevent data mishandling which can result in costly mistakes.

2. Simplifying compliance

Proper data classification makes it easier to comply with regulations like GDPR, CCPA, and ISO 27001. By knowing what types of data you have, you can quickly identify what’s subject to specific compliance requirements. For instance, GDPR requires stricter controls over personal data, particularly sensitive information.

Data classification helps businesses determine which data falls under these strict requirements, streamlining compliance processes. Organisations that fail to meet these obligations risk substantial fines – such as Meta’s €1.2 billion penalty in 2023 for data protection violations. Clear data classification ensures compliance and reduces the chance of facing similar penalties.

3. Reducing audit and compliance risks

When data is clearly classified, organisations can reduce the risk of compliance failures during audits. By understanding where sensitive data resides and how it’s protected, businesses can avoid penalties and minimise the likelihood of data breaches.

Furthermore, a well-structured data classification system demonstrates proactive compliance efforts during audits, reassuring regulators that the organisation is actively managing its data in line with industry standards.

4. Building trust with stakeholders

Demonstrating strong data security and compliance practices enhances trust with customers, partners, and stakeholders. Businesses that take data protection seriously are seen as more reliable and trustworthy.

By implementing clear data classification practices, organisations can show they are committed to safeguarding sensitive information, which builds confidence among stakeholders. This transparency not only helps reduce risks but also strengthens relationships and reputation in the market.

Conclusion

Automated data classification is essential for organisations seeking to enhance security, ensure compliance, and build trust in today's SaaS-driven environment.

By automating the identification and labeling of sensitive data, businesses can streamline data management, reduce risks, and improve overall security posture.

🔒Automate, Classify & Protect Sensitive Data in SaaS: Request a Metomic Demo Today

When you integrate your SaaS applications with Metomic, you’ll have access to out of the box classifiers that detect sensitive data such as credit card numbers, bank account numbers, email addresses, and more.

You’ll also have the option to create your own custom classifiers to protect sensitive data that matters to your organisation.

To find out more about how Metomic can automate your SaaS security, request a demo with one of our security experts today.