AI TRiSM
Gartner's framework for AI Trust, Risk and Security Management — the integrated set of controls that keep AI systems safe, trustworthy and compliant.
Full Definition
AI Trust, Risk and Security Management (AI TRiSM) is a Gartner coinage that bundles together the AI-adjacent control stack: AI model governance (lifecycle controls, inventory, approvals), AI-SPM (security posture of models and model-data), AI runtime enforcement (prompt filtering, output moderation, abuse detection), explainability tooling, data protection (training-data provenance, inference-time personal-data handling), and bias / fairness controls. The framework is more useful as a shopping list than as an architecture — organisations typically assemble pieces from different vendors rather than buying 'AI TRiSM' as a single product.
For regulators the four pillars line up with specific obligations: AI governance maps to the EU AI Act Articles 9–17 and the DPDP Section 10 SDF duties; AI-SPM evidence supports GDPR Article 35 DPIAs; explainability is the technical satisfaction of the Article 22 right to meaningful information about the logic involved; and bias / fairness controls support both EU AI Act Article 10 data-governance and non-discrimination duties across jurisdictions. Mature programmes bind AI TRiSM into the privacy and security stacks rather than treating it as a parallel vertical.
Related terms
AI Governance
The organisational framework of policies, roles, controls and audit practices that keeps an organisation's use of AI systems lawful, ethical and accountable.
AI Security Posture Management (AI-SPM)
A tooling category that discovers an organisation's AI models and the data flows into and out of them, assesses security risk and surfaces policy violations.
DPIA
Data Protection Impact Assessment — a systematic process to identify and minimise privacy risks in new processing activities.
Relevant regulations
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