Data-security and privacy vendor BigID announced a major new capability: Activity Explorer is designed to provide unified, granular visibility into how data is accessed and used across hybrid cloud, SaaS, and on-prem environments.
Enterprises are increasingly spanning multiple data stores, from cloud object stores such as AWS S3 to collaboration tools like Microsoft SharePoint, Google Drive, OneDrive, and legacy on-prem file shares. Due to this, the volume and complexity of data activity have exploded. According to BigID, this has left many organizations blind to critical questions such as: Who accessed or modified data, when, and what data was involved.
Activity Explorer aims to bridge that visibility gap. It consolidates fragmented logs and audit trails into a single, searchable interface to let security teams track data activity across identities-human users, service accounts, automated workflows, and AI agents alike across resources like cloud, SaaS, and on-prem via operations in download, deletion, modification, sharing, and over time.
This promises not only faster, more accurate investigations, for instance “Who deleted that file?”, “Which sensitive files were downloaded during this time window?”, but also richer context including data sensitivity classification, permissions, ownership, and exposure risk. Mixed with compliance requirements under such regulations as GDPR, HIPAA, CCPA, and others, the new tool provides audit-ready activity logs for enterprises.
According to its leadership, BigID considers this as the next evolution of its platform: delivering DSPM, data intelligence, cloud data loss prevention (DLP), and now activity monitoring in a single, unified way.
Why “Activity Explorer” Matters for Big-Data and Enterprise Data Management
1. Bridging the Visibility Gap in Distributed, Hybrid Data Architectures
Few modern data architectures, especially in enterprises, are monolithic. Organizations now have data strewn across a multitude of clouds, SaaS apps, on-premise servers, and hybrid storage systems. This complexity is the reason why traditional logging and monitoring are grossly inadequate. As BigID argues, many of these legacy tools were built in a world where data was centralized and the identity models were simple.
Blind spots in who accessed what and from where are a major risk for big-data enterprises that could be processing petabytes of structured and unstructured data across on-prem Hadoop or data-warehouse clusters and cloud data lakes. Unauthorized access, accidental data exposure, or insider threats are hard to detect without unified visibility. Activity Explorer correlation of data sensitivity with actual usage is a major step forward in data governance for big-data environments.
2. Supporting Compliance, Risk Management, and Audit Readiness
As the volume of personal or sensitive data collected, stored, and processed by businesses increases, so do the demands of regulation and compliance. Be it GDPR, HIPAA, CCPA, or any other region-specific data-privacy law, it is not only important to prove that data is stored securely but also that access to it is tracked and auditable.
It provides a single audit trail across hybrid data sources-cloud, SaaS, and on-prem-enriched with data classification and ownership context that will help businesses produce compliance-ready evidence. This reduces legal, compliance, and reputational risk-a big benefit for enterprises in regulated industries such as healthcare, finance, and retail.
3. Improving incident response, insider-threat detection, and data breach containment
Large-scale data incidents often arise not just from external attacks, but due to insider threats, misconfigured permissions, or misuse by service accounts or automated agents. For enterprises using big data for analytics, AI, or machine learning, automated workflows and AI agents are common yet poorly monitored.
Activity Explorer monitors activity from human and non-human identities alike-service accounts, AI agents, automation-that gives security teams clarity into what happened, but also how and by whom. In the case of a breach or suspicious behavior, teams can rapidly trace the “blast radius”-which files, datasets, or repositories were touched-and contain exposure.
4. Enable Smarter Data Governance & Least-Privilege Enforcement
Above all, over-permissioned user accounts, sprawling access rights, and unmonitored usage of data build up over time as an organization grows into teams and departments with numerous sources of data. Activity Explorer applies identity and permission context to the real-world activity observed and monitors how data is actually being accessed and consumed. This information can fuel “least-privilege” efforts by removing unnecessary or risky permissions and reinforcing zero-trust policies.
Also Read: AWS Launches Kiro and MCP (Model Context Protocol) Tools for SQL Server Professionals – A Big Shift for Big Data Operations
What this means for the greater big-data industry and businesses:
The introduction of Activity Explorer is indicative of the trend in the big-data and data-management world: from purely storing and processing volumes of data to governing, protecting, and making sense of data usage at scale. As generative AI, machine learning, and analytics adoption grows, data environments are becoming truly dynamic, distributed, and complex. With that complexity comes risk, and tools like Activity Explorer signal that security and compliance are becoming first-class citizens in big-data ecosystems.
But for businesses, especially those that are dealing with sensitive personal data, regulated industries, or complex data pipelines, this may raise the ante on data governance. We will likely see more people adopting unified DSPM, monitoring, and compliance platforms. This is better than using a mix of separate tools. Vendors providing big-data storage, analytics, AI insights, or data-lake services must enhance their audit and monitoring features. They might also consider partnering with companies like BigID.
As cloud migration grows, hybrid environments will become standard. This includes cloud, on-prem, SaaS, and AI workloads, even for large enterprises. Tracking data activity in a hybrid mix helps organizations gain better control and see risks more clearly. This will shape how companies design their data and AI systems. They will focus on built-in observability, compliance, and least-privilege access. The rise of tools like Activity Explorer shows that big data is maturing. It’s not just about size and speed anymore. Now, it’s about smart data, governance, and security. This is vital in a world where data is regulated, spread out, and used by AI.