Traditional Data Security
Relies on siloed tools, manual discovery, and incomplete visibility into where sensitive data lives.
BigID discovers sensitive, regulated, toxic, and high-value data across cloud, SaaS, hybrid, on-prem, structured, unstructured, and AI-connected environments.
Static Classification
Uses limited pattern matching that can miss context, relationships, identity, usage, and business meaning.
BigID combines advanced classification, ML, NLP, metadata, identity, ownership, lineage, access, and activity context to understand real data risk.
Access Blind Spots
Shows permissions in isolation without connecting access to data sensitivity, exposure, or business impact.
BigID connects sensitive data to identities, entitlements, ownership, activity, and exposure so teams can enforce least privilege and reduce risky access.
AI Exposure Risk
Was not designed to secure AI models, prompts, agents, copilots, training data, or shadow AI usage.
BigID helps discover AI assets, secure AI data, govern AI access, detect shadow AI, reduce prompt risk, and protect sensitive data behind AI workflows.
Reactive Controls
Stops at alerts and reports, leaving teams to manually investigate and remediate across disconnected systems.
BigID enables automated remediation: revoke access, delete toxic data, redact sensitive values, quarantine risk, enforce retention, and trigger workflows.
Point Tool Approach
Requires separate tools for discovery, classification, DLP, access governance, activity monitoring, privacy, compliance, and AI security.
BigID unifies data discovery, classification, DSPM, DDR, access governance, cloud DLP, privacy, compliance, AI security, and remediation in one platform.