AI TRiSM

AI You Can Trust. Data You Can Defend. Risk You Can Manage.

Automatically detect AI models, copilots, and third-party tools in use — sanctioned or not
Maintain a centralized, continuously updated inventory of AI systems and associated data
Track where models are deployed, what data they use, and how they’re being applied

Define and enforce role-based access to training data, prompts, outputs, and models
Identify and mitigate over-permissioned users and shadow AI use
Limit AI access to only approved, compliant, and purpose-aligned datasets

Apply governance controls from data ingestion through model deployment and use
Monitor for AI-specific risks: bias, data leakage, policy violations, and unauthorized use
Automate compliance with global frameworks like NIST AI RMF, EU AI Act, and ISO 42001

How BigID Powers AI TRiSM

BigID operationalizes AI Trust, Risk, and Security Management with real capabilities - not vaporware:

AI Asset Inventory

Automatically discover and catalog your AI models, training sets, datasets, copilots, and AI-driven applications across cloud, SaaS, and on-prem environments.

Training Data Discovery and Classification

Identify and classify the sensitive, regulated, or critical data feeding into AI models – ensuring responsible AI from the ground up.

AI Access Management

Control and monitor who can access models, training datasets, and AI pipelines. Enforce least privilege and zero trust across AI environments.

Shadow AI Detection

Uncover unauthorized AI activity, rogue copilots, and hidden model deployments that could introduce risk or violate policies.

AI Security and Privacy Governance

Govern AI-specific policies for privacy, data residency, security, and regulatory compliance—including emerging frameworks like the EU AI Act, NIST AI RMF, and ISO/IEC 42001.

AI Risk Detection and Remediation

Surface exposures like overexposed training data, insecure access, or bias-prone datasets—and take action to remediate issues before they escalate.

Lineage and Explainability

Track the origin, evolution, and usage of training data and models—supporting transparent, explainable AI practices for audits, compliance, and governance.