How to Build Your Own BigID

Continuously map, measure, and enforce compliance controls across data, AI, and regulatory frameworks

Monitor compliance status, generate reports, and automate remediation for violations

Leverage OWASP, NIST, and DOJ X-Border Rules to simplify reporting and compliance checks

Find and flag data that moves across regions or jurisdictions to reduce compliance and sovereignty risk

Simplify testing and reporting for emerging AI frameworks like the EU AI Act and NIST AI RMF

Categorize and label structured and unstructured data by business, domain, or semantic meaning to streamline governance

Automatically classify files, emails, chats, and code at scale using advanced machine learning and AI classification models

Label data by sensitivity, taxonomy, or business context across buckets, folders, tables, and vector databases

Apply flexible, automated labeling for sensitivity, taxonomy, or business context to streamline management and compliance

Label data by type, sensitivity, and AI readiness with automated, identity-aware tagging for policy enforcement and governance accuracy

Simplify cookie consent and tracking compliance across websites and mobile apps

Deliver comprehensive privacy management spanning cookies, universal consent, data mapping, assessments, vendor management, data flow, RoPA, privacy preferences, DSR, and more

Automate DSAR, deletion, and portability workflows with full audit trails and compliance assurance

Give users control over how their data and AI interactions are used and processed

Centralize and enforce consent preferences for data and AI use across all channels

Discover, assess, and take action on data and AI risk across every cloud, SaaS, and on-prem environment at enterprise scale

Track, assess, and prioritize regulatory and control risks across data and AI environments

Evaluate privacy and AI risks with continuous monitoring and actionable insights

Monitor, assess, and govern vendor data and AI sharing to reduce third-party risk

Track and restrict access to genAI data by user, model, or agent to maintain security and accountability

Gain full visibility and control into who accesses what data and monitor activity

Role-based access control scoped by person, team, or domain

Assess AI risk posture (AISPM), detect model and agent vulnerabilities, control access, and flag sensitive data use within AI systems

Detect when AI agents access sensitive data, enforce permissions, monitor behavior, and flag unusual agentic activity before it leads to exposure

Find unsanctioned models and copilots, detect unauthorized data use, assess vendor AI risk, and flag sensitive data exposure across MCPs

Extract, infer, and enrich metadata automatically using AI to improve accuracy, lineage, and ownership context

Identify and delete redundant, obsolete, or trivial data to reduce risk and storage cost across all environments

Enrich metadata with AI-driven inference for ownership, domain, and lineage to deliver a complete governance view

Assess AI program risk, manage security posture, detect vulnerabilities and unusual access, and enforce responsible, compliant AI use

Track and control employee AI data sharing, label data for AI, apply guardrails, intercept risky prompts, and enforce user and role-based access controls