AI TRiSM stands for AI trust, risk, and security management. It's a framework for governing artificial intelligence responsibly across the enterprise. The concept was developed by Gartner as a structured approach to govern, monitor, and control AI systems so they operate securely, reliably, and in alignment with enterprise policies and regulatory expectations.
The AI TRiSM framework provides a unified method to manage trust, risk, and security throughout the AI lifecycle. With this framework, organizations can better identify potential vulnerabilities, enforce safeguards against misuse or data exposure, and maintain consistent oversight as models evolve.
The five pillars of AI TRiSM
Gartner defines AI TRiSM around five foundational pillars that together address how AI systems are built, deployed, and governed in enterprise environments. These include:
Explainability: AI systems need to produce outcomes that humans can review, understand, and interrogate. Explainability focuses on making model decisions interpretable for security teams, auditors, regulators, and business owners. This makes it possible to analyze the root causes of problems, and builds trust in AI-driven decisions.
ModelOps: Model operations, or ModelOps, governs how models are deployed, monitored, updated, and retired after they move into production. It includes performance monitoring, drift detection, version control, and rollback processes. Without ModelOps, organizations struggle to manage risk as models evolve or degrade over time.
AI-specific security: AI introduces new attack surfaces beyond traditional application security, including models, data, APIs, infrastructure (cloud/on-prem), prompts, agents and copilots. This pillar addresses threats such as prompt injection, data poisoning, model inversion, and model theft. Controls focus on protecting models, inputs, outputs, and integrations from manipulation or abuse.
Privacy: The ways in which organizational data is collected, processed, stored, and reused during AI training and inference have huge impacts on customer privacy. Techniques such as anonymization, minimization, and differential privacy help reduce exposure of sensitive or personal data. Privacy failures often translate directly into legal and reputational risk.
Regulatory compliance: Like all production systems, AI models must align with regulations, including GDPR, HIPAA, and emerging AI-specific laws. This pillar focuses on policy enforcement, documentation, auditability, and evidence collection. Compliance requirements increasingly shape how and where AI can be deployed.
Implementing AI TRiSM in your organization
The best way to implement an AI TRiSM framework is as a phased effort that builds on existing security, data, and governance programs rather than replacing them outright. Here are five ways you can incorporate AI TRiSM best practices within your organization:
Audit current AI assets: Start by inventorying all AI models and agents in use, including third-party and embedded systems. Document usage scenarios, business owners, and training or inference data sources. This visibility establishes the foundation for governance and risk assessment.
Establish a governance framework: Governance provides consistency as AI usage expands across teams. Define clear ownership for model development, deployment, and ongoing oversight and set policies for acceptable use, data handling, model updates, and exception management.
Apply risk assessments: Evaluate exposure points across the AI lifecycle, from data ingestion and training to inference and downstream integrations. Assess risks related to data leakage, bias, model drift, and misuse. Risk assessments should be revisited as models and use cases change.
Deploy monitoring and controls: Implement monitoring for model behavior, AI agents, and integrations to detect anomalies or policy violations. Apply identity and access management for agents, validate inputs, and track outputs. Continuous monitoring allows issues to be identified early.
Incorporate data security: Protect sensitive data throughout training, inference, and storage with strong access controls and encryption. By discovering and labeling your sensitive data, you reduce the likelihood that AI systems will expose regulated or proprietary information.
Common AI TRiSM use cases across industries
AI TRiSM is applied differently depending on industry context, but the underlying goal is the same: reduce risk while maintaining trust, security, and accountability. Table 1 below summarizes common use cases, the primary risk addressed in each, and the AI TRiSM components most directly involved.
Table 1. Typical TRiSM use cases
Industry | Use case | How AI TRiSM mitigates the risk | TRiSM component addressed |
Healthcare | AI-assisted diagnostics and clinical decision tools | Applies data governance, access controls, and monitoring to limit exposure of protected health information during training and inference, while supporting audit trails for clinical and regulatory review. | Privacy, regulatory compliance |
Finance | Credit risk scoring and loan approval models | Requires explainability and continuous monitoring so decision logic can be reviewed, tested for bias, and corrected as models drift or regulations change. | Explainability, model operations, compliance |
Retail | Customer behavior and recommendation models | Secures training data, models, and outputs to reduce the risk of data leakage, model theft, or manipulation through unauthorized access or adversarial inputs. | AI-specific security, privacy |
Government | Public-facing AI services and decision systems | Enforces transparency, documentation, and policy controls that support accountability, public trust, and compliance with procurement and regulatory requirements. | Explainability, compliance |
Across these sectors, an AI TRiSM framework can provide a consistent structure for addressing industry-specific risks while supporting scalable and accountable AI adoption.
How Rubrik can help implement AI TRiSM for secure, scalable AI adoption
AI TRiSM is essential for organizations building and deploying AI systems at scale, particularly in regulated, data-rich environments where trust, security, and accountability matter. As AI adoption accelerates, managing risk must be built into how models are developed, deployed, and monitored.
Data protection and governance play a central role in making AI TRiSM practical. Rubrik’s data security and governance capabilities can be extended to support secure AI operations by protecting sensitive data, improving visibility into risk, and supporting audit and compliance requirements across the AI lifecycle.
For organizations looking to future-proof their AI initiatives and operationalize AI TRiSM, connecting data security with AI governance is a critical next step. To explore how Rubrik can support secure and responsible AI adoption, contact the Rubrik team.