BigID Introduces AI TRiSM to Govern, Assess, and Trust AI Models and Data

Built-in AI Security Posture Management, Risk Assessment, and Data Trust – in one powerful platform
BigID, the leading platform for data security, privacy, compliance, and AI governance, introduced AI TRiSM (Trust, Risk, and Security Management) – a new, integrated set of controls that empowers organizations to govern AI usage, detect emerging threats, and validate the integrity of the data fueling their models.
As AI adoption scales, organizations face new threats and obligations across model behavior, access, and data quality. BigID’s AI TRiSM unifies three essential capabilities in a single platform:

AI Data Trust: validate that training and inference data is compliant, accurate, and appropriate
AI Risk Assessment: quantify exposure across infrastructure, data, usage, and vendors
AI Security Posture Management (SPM): detect unauthorized GenAI use, prevent data exfiltration, and mitigate prompt injection attacks

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Unlike tools that stop at visibility, BigID is built for action. AI TRiSM lets teams continuously monitor AI risk, trigger remediation workflows, and enforce policies based on model behavior, data sensitivity, and organizational requirements.
As part of BigID’s end-to-end visibility and control platform, AI TRiSM delivers the depth and reach teams need to govern AI across the enterprise—bringing trust, control, and accountability into every AI workflow.
Key Takeaways

Detect risky AI behavior with AI Security Posture Management (SPM)
Automate AI Risk Assessments across usage, vendors, and infrastructure
Validate training and inference data with AI Data Trust verification
Trigger remediation workflows and enforce policy-driven controls
Operationalize AI governance across data, models, and pipelines

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“AI risk isn’t static – and it isn’t theoretical. It’s real, it’s evolving, and it’s actionable,” said Dimitri Sirota, CEO and Co-Founder at BigID. “With AI TRiSM, we’re giving organizations a unified way to detect unauthorized AI use, assess model risk, and verify data trust so they can govern AI with confidence.”

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