1. Discover Shadow & Native Data Across Clouds
Enterprises often struggle with shadow data—unmonitored or unnoticed information that can include a high percentage of ROT (Redundant, Obsolete, or Trivial) data. While fully managed services (e.g., Amazon RDS, Amazon S3) are easier to track, self-managed systems running on VMs or containers can go undetected, creating hidden risk exposure.
Securiti solves this challenge by automatically discovering both cloud-native and shadow data assets at scale, quickly revealing unmonitored databases, orphaned volumes, and forgotten file shares. Through its automated discovery of disks attached to virtual machines or containers, Securiti uncovers any software packages signaling a hosted data system—providing a holistic, cloud-wide view of unknown repositories. Armed with this insight, enterprises can minimize ROT data, strengthen security, and maintain continuous compliance across their entire cloud footprint.
2. Centralize Data Inventory Across Hybrid Multi-Cloud & SaaS
Modern enterprises increasingly rely on a complex ecosystem of data sources—ranging from on-premises environments and private clouds to public clouds, data lakes, and SaaS platforms like Snowflake, Databricks, and Microsoft 365. Managing the sprawling data landscape can be overwhelming, limiting visibility into where data resides and how it’s used.
Securiti addresses this challenge by offering a unified platform to maintain a centralized inventory of all structured and unstructured data systems across hybrid, multicloud, and SaaS ecosystems in one view.
By continuously discovering, mapping, and inventorying all data assets, Securiti provides a consolidated perspective of the entire data footprint.
3. Flag Obsolete Data Based on Age & Activity Criteria
Over time, vast amounts of enterprise data remain untouched, offering no ongoing operational or analytical value. Securiti’s contextual data intelligence precisely identifies files and datasets that were created before a specific date or have not been modified for a defined period—whether months or years.
By enforcing time-based or activity-based policies, Securiti pinpoints stale or outdated assets, enabling organizations to confidently retire them. This proactive identification and removal of obsolete data helps reduce storage costs while simultaneously curtailing security risks.
4. Detect Redundant Data by Identifying Duplicate Content
A large portion of ROT data within an enterprise stems from duplicate file copies. Overtime, employees frequently create multiple versions of the same document or store them in multiple repositories, resulting in data duplication.
To address this challenge, Securiti’s automated solution applies:
- Checksums to detect exact duplicates. During discovery scans, Securiti generates a unique checksum for each file in scope, and files with the same checksums are flagged as duplicates.
- Advanced Cluster Analysis for near-duplicates, by analyzing characters of a file’s parsed text. Organizations can fine-tune similarity thresholds and cluster sizes to spot significantly similar files.
Once the scan completes, Securiti’s File Cluster Analysis Dashboard displays a consolidated view of detected duplicates and near-duplicates across diverse environments—ranging from public cloud storage (e.g., Amazon S3, Azure Files) to enterprise SaaS platforms (e.g., Microsoft 365 SharePoint Online) and private clouds.
5. Classify Sensitive Data to Address Retention Violations and Risks
Once data is flagged as redundant or obsolete, the next crucial step is determining whether those files contain sensitive or regulated information that warrants immediate attention. By scanning and labeling each file’s content, Securiti pinpoints whether it includes sensitive data such as confidential intellectual property information, financial identifiers, or personal data governed by frameworks like GDPR, CPRA, PCI DSS, etc.
With these classification insights in hand, Securiti then enables enterprises to define and enforce custom policies that compare each file’s creation or last-modified date against the relevant retention policies. For instance, if a file is identified as containing PCI data but has surpassed the allowable retention window (e.g., seven years), it is automatically flagged as a retention violation. Rather than sifting through countless files, teams can concentrate on the toxic combinations of outdated files that also hold regulated content—streamlining compliance efforts and mitigating security risks.
When Securiti flags ROT data, its automated remediation steps immediately kick in. First, the solution alerts file owners via preferred collaboration tools (Slack, ServiceNow, Jira, etc.) so they can review the flagged content. If files pose a higher risk, administrators can quarantine them–or flag the files so that they can be moved or archived to a low-cost storage option–to minimize exposure until the owner approves further action. Once removal is approved, Securiti orchestrates deletion workflows aligned with relevant regulatory mandates.
Throughout this process, detailed reports and exportable results provide stakeholders with clear, auditable evidence of each remediation effort. This policy-driven approach ensures that ROT data minimization remains consistent, timely, and aligned with regulatory mandates—delivering granular insights even as data environments evolve.
Best Practices & Tips from Real Data Minimization Projects
Securiti’s step-by-step approach ensures comprehensive coverage, yet some organizations customize it to focus on securing their most critical data assets first, according to their specific risk priorities and compliance demands.
Below are practical insights derived from real-world ROT Data minimization projects that illustrate how organizations can adapt these steps to their immediate needs:
- Prioritize actions based on compliance drivers: If the driver for data minimization is a compliance regulation, for example, PCI-DSS, teams can first prioritize data minimization efforts around systems that contain PCI-DSS data and then come back later to holistically identify data minimization opportunities across the broader data real estate.
- Classify Data Selectively: Scanning every file in every repository can be expensive and time-consuming. To speed up data minimization projects, enterprises can first prioritize sensitive data classification for high-risk data systems based on applications subject to the most stringent regulations. This ensures the biggest gains where they matter most.
- Scalable Rollout: Start small for rapid wins, such as prioritizing data minimization efforts for a business unit before expanding to other parts of the organization. Incremental successes build momentum toward a comprehensive ROT data minimization program while enabling your teams to address mistakes and apply lessons learned across the project's later phases.
Enterprises aiming to maintain a secure, compliant, and efficient data environment can rely on Securiti’s automated, policy-driven framework to tackle ROT data. By discovering hidden assets, pinpointing obsolete or duplicate files, and classifying sensitive information, organizations rapidly reduce unnecessary data at scale. Whether focusing on urgent compliance needs first or incrementally broadening a cleanup effort to the entire enterprise, Securiti offers the flexibility and actionable insights needed to minimize ROT. The result is a leaner data footprint, strengthened security posture, streamlined regulatory alignment, and more reliable outcomes for analytics and AI initiatives.
Ready to Tackle ROT Data?
Request our on-demand ROT Data Minimization demo now and learn how Securiti can help your organization eliminate unnecessary files, safeguard sensitive information, and optimize data-driven operations.
