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What is AI TRISM?

AI Trust, Risk, and Security Management (AI TRiSM) is a framework defined by Gartner to ensure the safe, ethical, and compliant deployment of AI systems. It addresses the unique challenges posed by AI, such as data compromise, third-party risks, and harmful outputs. AI TRiSM operates across four critical layers: AI Governance, AI Runtime Inspection & Enforcement, Information Governance, and Infrastructure & Stack. These layers work together to protect AI systems throughout their lifecycle, ensuring trustworthy performance while mitigating risks. As AI adoption accelerates, AI TRiSM has become essential for organizations aiming to deploy AI responsibly and securely.

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What is the Gartner Market Guide for AI Trust, Risk, and Security Management?

The Gartner Market Guide for AI Trust, Risk, and Security Management (AI TRiSM) provides a comprehensive analysis of the emerging market dedicated to securing, managing risks, and ensuring the trustworthiness of artificial intelligence systems. Gartner defines the AI TRiSM market as comprising four layers of technical capabilities: AI Governance, AI Runtime Inspection & Enforcement, Information Governance, and Infrastructure & Stack. These layers support enterprise policies across all AI use cases, ensuring governance, trustworthiness, safety, and regulatory compliance

With AI becoming deeply embedded across industries, ensuring that AI systems perform as intended while mitigating risks is paramount. The guide helps AI leaders and enterprises identify critical technologies, best practices, and representative vendors for building robust AI TRiSM frameworks.

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What Are the Key Findings of the Gartner Market Guide for AI Trust, Risk, and Security Management?

The 2025 edition of the AI TRiSM Market Guide highlights several critical findings:

1. Primary AI Risks: Enterprises are most concerned with data compromise, third-party risks, and inaccurate or unwanted outputs.
2. Limited Malicious Attacks: While hacks against enterprise AI remain uncommon, incidents involving harmful chatbots and internal data oversharing are prevalent.
3. Layered Protection: AI TRiSM measures apply across all AI types, from embedded AI to agentic systems, complementing traditional security measures.
4. Rising Demand: User demand for generative AI (GenAI) TRiSM solutions is steadily increasing, with providers of all sizes competing for enterprise business.
5. Vendor Specialization: Some vendors focus on security and risk mitigation, while others specialize in ethics, safety, and compliance.
6. No Universal Solution: There is currently no single vendor that addresses all AI risk and threat aspects.
7. Organizational Silos: AI TRiSM initiatives often expose organizational silos, pushing enterprises to realign for cross-functional collaboration.

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What is the Gartner Market Guide?

Gartner's Market Guide is a research document that provides an overview of an emerging market, its key trends, and representative vendors. Unlike the Gartner Magic Quadrant, which evaluates leading vendors based on their ability to execute and completeness of vision, the Market Guide focuses on:

• Defining the market landscape
• Highlighting critical features and capabilities
• Identifying emerging trends and challenges
• Recommending strategies for enterprises

For AI TRiSM, the Market Guide defines the market’s four layers, identifies mandatory features, and provides vendor insights to help enterprises implement robust AI governance and security frameworks.

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What Are the 4 Layers in the Gartner Market Guide for AI Trust, Risk, and Security Management?

Gartner's AI TRiSM framework consists of four layers, with the top two layers representing new and consolidating market segments:

1. AI Governance: The foundation for AI TRiSM, ensuring visibility, traceability, and accountability across all AI assets. It includes AI catalogs, continuous assurances, and evaluations.
2. AI Runtime Inspection and Enforcement: Focuses on real-time monitoring of AI models, applications, and agent interactions to detect and mitigate risks, anomalies, and policy violations.
3. Information Governance: Ensures that AI systems access only properly permissioned and classified data, protecting sensitive information throughout its lifecycle.
4. Infrastructure and Stack: Involves traditional technology controls, including endpoint, network, and cloud security solutions, applied to AI workloads.

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These layers work together to provide end-to-end security, risk management, and governance for enterprise AI deployments.

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AI Governance

AI Governance forms the foundation of the AI TRiSM framework. It ensures visibility, traceability, and accountability across all AI assets within an enterprise. This layer includes creating an AI catalog that inventories all models, agents, and applications. Governance also involves continuous assurances, including predeployment and postdeployment evaluations, ensuring that AI systems meet defined performance, safety, and compliance standards. Effective AI governance includes robust documentation, such as model cards and audit trails, which facilitate transparency and accountability. Furthermore, AI governance helps organizations align AI deployments with business goals while adhering to ethical standards and regulatory requirements.

AI Runtime Inspection and Enforcement

AI Runtime Inspection and Enforcement focuses on real-time monitoring and enforcement of AI governance policies during AI operations. This layer involves inspecting AI models, applications, and agent interactions to identify anomalies, policy violations, or security threats. It supports transactional alignment with enterprise governance policies, ensuring that AI actions remain compliant with organizational standards. Runtime enforcement includes continuous monitoring, anomaly detection, and automatic remediation, with alerts forwarded to incident response teams when necessary. This layer plays a critical role in ensuring that AI systems operate within established parameters while protecting sensitive data and intellectual property.

Information Governance

Information Governance ensures that AI systems access only properly permissioned and classified data, protecting sensitive information throughout its lifecycle. This layer involves data classification, access controls, and policies that prevent data leakage or misuse. Effective information governance addresses the challenges of managing unstructured data, such as documents and communications, alongside structured datasets. It ensures that AI systems only access relevant data, reducing the risk of exposing confidential information. This layer also supports compliance with data privacy regulations and corporate governance standards.

Infrastructure and Stack

The Infrastructure and Stack layer encompasses traditional technology controls applied to AI workloads, including endpoint, network, and cloud security solutions. This foundational layer supports secure AI development, deployment, and operations by integrating security controls into the underlying infrastructure. It includes API key management, confidential computing, and workload protection, ensuring that AI environments remain secure. The infrastructure layer also facilitates portability across hosting providers, allowing enterprises to adopt best-fit AI solutions while maintaining governance and risk management standards.

These layers work together to provide end-to-end security, risk management, and governance for enterprise AI deployments.

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What is the AI TRiSM Market Definition?

Gartner defines the AI TRiSM (Trust, Risk, and Security Management) market as encompassing technical capabilities that enforce enterprise policies for AI governance, trustworthiness, safety, and security. These capabilities span the entire AI lifecycle, ensuring AI systems align with organizational goals, operate securely, and adhere to regulatory requirements.

The AI TRiSM market is structured around four core areas:

1. AI Inventory and Cataloging: This involves creating a comprehensive catalog of all AI entities within an organization. This includes models, applications, and agents, regardless of whether they are proprietary, third-party, or embedded. Effective cataloging ensures organizations have visibility into their AI assets, enabling better governance, risk assessment, and performance monitoring.
2. Data Mapping and Lineage Tracking: AI TRiSM frameworks must trace the lineage of data used for AI development, training, and deployment. This includes understanding how data flows through AI systems, ensuring proper data classification, and applying appropriate access controls. Data mapping reduces the risk of data compromise while maintaining privacy and regulatory compliance.
3. Continuous Assurance and Evaluation: AI systems must undergo ongoing evaluations to ensure performance, reliability, and adherence to governance policies. This includes predeployment testing, real-time monitoring, and postdeployment assessments. Continuous assurance ensures that AI models remain aligned with organizational objectives while identifying and mitigating potential risks.
4. Runtime Inspection and Enforcement: Real-time inspection of AI interactions is critical for ensuring that AI-driven decisions align with enterprise governance policies. This involves monitoring inputs, outputs, and system behavior, flagging anomalies, and initiating automated or manual remediation processes.

The AI TRiSM market is evolving rapidly as enterprises seek to address risks associated with generative AI, agentic systems, and embedded AI. The market is also expanding to cover ethical AI practices, regulatory compliance, and risk mitigation. As AI adoption grows, AI TRiSM frameworks will become essential for safeguarding enterprise AI ecosystems while enabling innovation and operational efficiency.

What Are Gartner’s AI TRiSM Mandatory Features?

Gartner identifies four mandatory features for AI TRiSM solutions:

1. AI Catalog: An inventory of all AI entities (models, agents, and applications) used in the organization.
2. AI Data Mapping: Comprehensive mapping of data used for AI training, tuning, and contextualization.
3. Continuous Assurances and Evaluation: Ongoing performance, reliability, and security assessments.
4. Runtime Inspection and Enforcement: Real-time monitoring and enforcement of AI governance policies.

These features ensure that AI systems align with organizational goals, maintain security, and support regulatory compliance.

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What Direction is the AI TRiSM Market Headed?

According to Gartner, four key trends are shaping the future of the AI TRiSM market:

1. AI TRiSM Teams: Cross-functional teams combining AI engineering, security, risk, and compliance functions are emerging to manage AI TRiSM operations. These teams break down organizational silos, ensuring unified efforts in AI governance and risk management. Gartner expects AI TRiSM oversight and operations teams to report directly to enterprise AI leadership, enabling faster decision-making and more effective collaboration.
2. Unified Runtime Systems: Consolidated systems for runtime inspection and policy enforcement will replace siloed solutions, ensuring consistent AI governance. These systems provide real-time analysis of AI events, merging risk scores from various detection engines into a unified framework. This approach reduces latency, enhances accuracy, and ensures that policy enforcement aligns with organizational standards. Companies like Palo Alto Networks and Cisco are already investing in unified AI TRiSM platforms.
3. Expanded AI Hosting Services: Frontier model providers like Microsoft, Google, and AWS are expanding TRiSM services to attract enterprise customers. These services include AI-specific controls, such as guardrails for prompt injections, real-time data protection, and runtime anomaly detection. However, Gartner emphasizes that organizations must retain independence from any single AI model or hosting provider to ensure flexibility and cost control.
4. Market Consolidation: AI governance and runtime enforcement vendors are merging, while traditional security vendors like Palo Alto Networks and Cisco expand into AI TRiSM. This consolidation is driven by the increasing demand for comprehensive solutions that integrate governance, risk management, and security controls. Gartner predicts that AI TRiSM as a service will emerge as a viable option for small and midsize enterprises, reducing the burden of system integration and maintenance.

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In addition to these trends, Gartner highlights the growing importance of agentic AI controls, which will become essential as AI systems evolve to operate more autonomously. Enterprises will need robust frameworks to manage agentic AI risks while ensuring alignment with organizational policies and regulatory requirements.

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Is the Gartner Magic Quadrant Different from the Gartner Market Guide?

Yes, the Gartner Magic Quadrant and Market Guide serve different purposes:

• Market Guide: Provides an overview of an emerging market, including trends, challenges, and representative vendors. It helps enterprises understand and navigate new technologies.
• Magic Quadrant: Evaluates established vendors based on their ability to execute and completeness of vision, placing them in one of four quadrants: Leaders, Challengers, Visionaries, and Niche Players.

Since AI TRiSM is an emerging market, Gartner publishes a Market Guide rather than a Magic Quadrant.

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What’s the Future of Gartner AI TRiSM Analysis?

The future of AI TRiSM analysis will focus on three key areas:

1. Agentic AI Controls: As AI systems become more autonomous, TRiSM solutions will expand to cover agentic AI risks.
2. AI TRiSM as a Service: Outsourced TRiSM services will emerge to support small and midsize enterprises lacking in-house resources.
3. Consolidation and Innovation: The market will continue consolidating, with larger vendors integrating TRiSM capabilities into existing platforms.

Gartner expects AI TRiSM to become a critical component of enterprise AI strategies, ensuring that AI deployments remain secure, trustworthy, and compliant.

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How Mindgard Fits into Gartner's AI TRiSM Framework

Mindgard aligns closely with Gartner's AI TRiSM framework by offering comprehensive AI red teaming, security and governance solutions for enterprise AI systems. As AI continues to drive business innovation, organizations face increasing challenges in managing AI-related risks, ensuring data privacy, and maintaining compliance with evolving regulations. Mindgard addresses these challenges by providing capabilities that span the four TRiSM layers defined by Gartner: AI Governance, AI Runtime Inspection & Enforcement, Information Governance, and Infrastructure & Stack.

AI Governance

Mindgard enables organizations to establish robust AI governance by offering comprehensive visibility into their AI inventory. This includes cataloging all AI models, applications, and datasets used across the enterprise. Mindgard's platform supports continuous evaluation of AI systems, ensuring alignment with organizational policies, ethical standards, and regulatory requirements. By automating AI documentation, including model cards and risk assessments, Mindgard simplifies governance while enhancing transparency.

AI Runtime Inspection and Enforcement

Mindgard's runtime inspection capabilities ensure real-time monitoring and enforcement of AI policies. The platform continuously inspects AI interactions, identifying anomalies, policy violations, and potential threats. This proactive approach helps organizations maintain control over AI-driven decisions while protecting sensitive data and intellectual property. Automated remediation and incident escalation ensure timely responses to emerging risks.

Information Governance

Mindgard strengthens information governance by providing dynamic data mapping, access controls, and policy enforcement. It ensures that AI systems only access properly classified and permissioned data, reducing the risk of data leakage or misuse. Mindgard's solutions also support compliance with data privacy regulations, such as the EU AI Act and GDPR, by maintaining detailed audit trails and ensuring proper data handling practices.

Infrastructure and Stack

Mindgard integrates seamlessly with existing infrastructure, providing security controls that protect AI workloads across cloud, on-premises, and hybrid environments. The platform includes API key management, confidential computing, and workload protection, ensuring that AI environments remain secure and resilient.

By addressing all aspects of AI TRiSM, Mindgard empowers enterprises to deploy AI confidently, knowing their systems are secure, trustworthy, and compliant.

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Frequently Asked Questions

What is Gartner TRiSM

Gartner AI TRiSM (Trust, Risk, and Security Management) is a framework designed to ensure the secure, ethical, and compliant deployment of AI systems. It encompasses four layers: AI Governance, AI Runtime Inspection & Enforcement, Information Governance, and Infrastructure & Stack. These layers work together to manage AI risks, enforce policies, and maintain operational integrity across AI use cases.

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What is the failure rate of Gartner AI projects?

According to Gartner, nearly 50% of AI projects fail to progress from prototype to production, often due to inadequate risk management, poor governance, and lack of cross-functional collaboration. AI TRiSM addresses these challenges by providing a structured approach to identify risks early, enforce governance, and ensure AI models align with organizational policies and business objectives.

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What is an example of AI TRiSM?

An example of AI TRiSM is an AI-driven fraud detection system in banking. Through AI TRiSM, the system would undergo continuous monitoring for biases, security vulnerabilities, and data anomalies. Real-time runtime inspection would identify unusual transactions while ensuring that customer data remains protected, policies are enforced, and the AI's decision-making aligns with regulatory and organizational standards.

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What is a key factor in achieving success with AI TRiSM?

A critical factor for AI TRiSM success is cross-functional collaboration. Effective implementation requires alignment between IT, security, risk, compliance, and business units. Gartner emphasizes the need for unified AI governance and runtime enforcement, ensuring all stakeholders contribute to AI lifecycle management, risk mitigation, and continuous policy enforcement for trustworthy and compliant AI deployment.

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Conclusion

The Gartner AI TRiSM Market Guide provides a comprehensive framework for managing AI trust, risk, and security. As AI adoption accelerates, enterprises must implement robust TRiSM measures to protect sensitive data, mitigate risks, and ensure AI systems operate as intended. By leveraging Gartner’s insights and adopting solutions that cover all four TRiSM layers, organizations can build secure, trustworthy, and compliant AI ecosystems.

Gartner customers can download the full market guide here.