BigID announced its new Activity Explorer. This tool revamps data-activity monitoring. It helps organizations track and analyze data movement across cloud, SaaS, and on-premises systems. The tool combines logs from platforms like AWS S3, SharePoint, OneDrive, Google Drive, NetApp, and file shares into one clear audit view. This improves visibility in diverse data environments.
As organizations adopt hybrid ecosystems with human users, automated workflows, and AI agents, traditional audit logs fall short. They often have fragmented logs and incomplete trails. This creates blind spots due to a lack of insight into non-human identities. Very often, security teams cannot answer basic questions like “who touched which data, when, and how?” in urgency or under investigation and compliance pressure.
Activity Explorer addresses this with a single, searchable interface that adds context to each event. It connects data activity to sensitivity classifications, identity information, permissions, data ownership, and exposure levels. Users can filter by identity type: whether human users, service accounts, workflows, or AI agents; by operation type, data resource, time range, or data sensitivity. What might have taken hours of manual investigation can now be done in seconds.
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With full visibility across data movement in the form of downloads, deletions, sharing, modifications, Activity Explorer enables fast forensic investigations, breach containment, and compliance-ready audit trails supporting regulations like HIPAA, GLBA, GDPR, and CCPA.
“Organizations can’t protect what they can’t see — and they can’t investigate what they can’t trace,” said Nimrod Vax, Chief Product Officer and Co-Founder at BigID. “With Activity Explorer, we’re giving security teams unified visibility into user, service account, and automated activity across their hybrid environment, helping them investigate insider risks, support compliance, and strengthen their data security posture.”
Activity Explorer empowers enterprises to move away from fragmented logging towards a single source of truth by combining deep data context with extensive audit coverage, thereby improving overall data governance, security, and trust in AI-driven analytics environments.