AI Trism Adoption

AI TRiSM: Why Enterprises Must Prioritize AI Governance

AI is rapidly becoming an integral part of business operations, yet its widespread adoption brings heightened risks related to security, compliance, and ethical considerations. AI Trust, Risk, and Security Management (AI TRiSM) is Gartner’s framework for ensuring AI systems are governed effectively, safeguarding enterprises against issues like data breaches, unreliable models, and regulatory violations.

AI governance technology plays a critical role in operationalizing AI TRiSM by providing visibility, compliance enforcement, and continuous evaluation of AI models and applications. AI governance frameworks must support AI visibility and traceability, workflow approvals, and continuous risk and trust validation to ensure AI integrity and security which is how ModelOp approaches the AI Governance process.

The Four Layers of AI TRiSM

Gartner’s AI TRiSM is built on four core technology layers that collectively enforce governance and risk management across AI systems:

• AI Governance – Establishes accountability for AI use, ensuring alignment with regulatory, ethical, and business objectives.
• AI Runtime Inspection and Enforcement – Provides real-time oversight, detecting and mitigating risks during AI operations.
• Information Governance – Manages data integrity, classification, and access controls to prevent misuse and unauthorized exposure.
• Infrastructure and Stack – Supports AI operations across diverse environments, ensuring flexibility and security in AI deployment.

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Unlike traditional IT governance, AI TRiSM is uniquely designed to handle the dynamic risks of AI, requiring a distinct and structured approach.

Key Themes From Gartner’s AI TRiSM Framework

1. AI Governance as a Core Pillar
   AI governance provides a structured approach to managing AI systems, ensuring accountability, compliance, and ethical use. It involves defining policies, overseeing AI lifecycle management, and aligning AI practices with regulatory and business standards. Unlike traditional data governance, AI governance requires specialized oversight to address the unique challenges of AI decision-making and deployment.
   
   
2. Real-Time AI Oversight and Risk Detection
   Continuous monitoring and enforcement mechanisms are critical to managing AI risks. AI TRiSM includes runtime inspection to detect anomalies, assess model behavior, and enforce governance policies in real time. By applying layered security and risk controls, organizations can ensure that AI applications, models, and agents operate within defined ethical and operational boundaries.
   
   
3. Data Integrity and Information Governance
   AI governance extends beyond models to encompass data security and classification. Managing AI-related data effectively helps prevent unauthorized access, reduce exposure risks, and maintain compliance with evolving regulations. Organizations must adopt robust data access policies and classification frameworks to ensure AI models operate with secure and reliable information.
   
   
4. Independence from AI Model and Hosting Providers
   Enterprises should avoid dependence on a single AI model or provider to maintain flexibility, cost efficiency, and control over AI governance. AI TRiSM emphasizes the importance of portability, allowing organizations to choose the most suitable AI models, tools, and cloud environments without vendor lock-in. This ensures scalability and adaptability as AI markets evolve.
   
   
5. Cross-Functional AI Risk Management
   AI TRiSM requires collaboration across multiple business functions, including IT, security, compliance, and data science. Governance strategies should be integrated into enterprise-wide operations to align AI risk management with broader organizational objectives. A coordinated approach helps enterprises implement consistent oversight across AI initiatives.
   
   
6. Automated Governance and Compliance Controls
   To manage AI risks effectively, enterprises must implement automated policy enforcement mechanisms. AI TRiSM solutions enable continuous risk assessments, compliance tracking, and regulatory reporting. Automated governance tools reduce manual oversight burdens and ensure AI systems operate within established policies, enhancing security and trustworthiness.

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Why AI Governance is an Enterprise Imperative

Enterprises cannot rely on ad hoc risk management or assume existing IT controls suffice for AI governance. The AI landscape is evolving rapidly, with regulatory expectations rising and risks becoming more complex. As AI models become more deeply embedded in critical business processes, the need for structured governance becomes paramount to ensure compliance, security, and ethical AI operations. Without a comprehensive AI governance strategy, organizations risk exposure to regulatory penalties, reputational damage, and operational inefficiencies. Given these high stakes, enterprises must take a proactive approach to implementing AI governance strategies that maintain compliance, enhance security, and ensure AI’s ethical and operational integrity while mitigating risks effectively.

In Gartner’s view, AI governance has three critical functions:
1. AI visibility and Traceability

2. Workflow (internal and third-party)

3. Continuous assurances and evaluation (internally developed and third party)

AI visibility and traceability involve several critical components to ensure proper oversight and management of AI systems. Organizations must maintain a catalog that facilitates discovery, inventory, and risk scoring of all AI used within the enterprise. This includes automated (partly) documentation, such as a bill of materials, model cards, artifacts, regulatory reports, and explainability records.

To ensure robust oversight, audit trails must be maintained, documenting all state changes to AI artifacts. Organizations should also develop maps of AI integration with human and other system processes, including their configurations and complete life cycles. Furthermore, defining ownership of AI artifacts and lineage is essential to establish clear responsibility. Mapping of data related to AI usage and lineage further enhances traceability. Lastly, risks, regulations, and controls should be addressed through validations, risk assessments, content libraries, and reports.

In terms of workflow for internal and third-party interactions, organizations should establish approval processes for new AI while handling exceptions. Attestations of use are necessary to document AI adoption formally. Additionally, organizations must ensure effective communication with third-party providers, addressing aspects such as RFI (Request for Information), responses, compliance, and control mechanisms.

Continuous assurances and evaluation are critical for securing AI systems. This includes AI security testing, which involves red teaming and scanning all entities, including models, applications, and agents. Further, risk and trust control validation is essential, covering aspects like bias, leakage, trust, and use-case alignment.

Additionally, AI governance requires posture management to maintain the system's security stance over time and compliance reporting to meet regulatory and policy requirements.

AI Governance Is Separate from Data Governance

Gartner’s AI TRiSM pyramid highlights that AI Governance is a standalone functionality, distinct from traditional Data Governance. Many organizations mistakenly group AI governance under data governance, but AI governance encompasses a broader scope, including:

• Risk Tiering and Reporting
• Lifecycle Management
• Regulatory and Compliance Frameworks
• Ethical AI Policies

Treating AI governance as an independent discipline ensures enterprises can maintain flexibility, accommodate emerging regulations, and enforce responsible AI practices at scale.

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AI Governance Must Be Independent from AI Development and Executive To Be Effective and Enable Innovation Freedom

According to Gartner, ss AI technology rapidly evolves, “enterprises must retain independence from any single AI model or hosting provider to ensure scalability, flexibility, cost control, and trust, as AI markets rapidly mature and change.” Gartner’s AI TRiSM framework emphasizes that organizations should be able to select the most suitable AI models based on performance, risk, and cost rather than being locked into a single provider.

This independence is crucial as AI markets mature, with new models continuously emerging that may offer better accuracy, security, or efficiency. Enterprises that rely solely on AI functionality embedded in existing applications risk losing the ability to govern AI comprehensively across multiple use cases. To mitigate this, organizations should establish portable AI governance functions that apply consistently across different hosting environments, including proprietary, open-source, cloud-based, and third-party vendor models.

AI TRiSM solutions should integrate seamlessly with multiple AI systems to enforce governance policies universally, ensuring consistent oversight, risk management, and regulatory compliance across all AI investments. This strategic approach not only safeguards enterprises from vendor dependency but also enhances resilience and innovation by enabling AI adoption on a broader scale.

AI Governance is Separate and Distinct from that of AI Runtime Inspection and Executions

Gartner’s most recent AI TRiSM market guide reportf suggests that AI governance and runtime inspection are merging into a single market segment. While ModelOp generally agrees with and align with Gartner’s AI TRiSM framework, ModelOp asserts that governance and runtime inspection are separate functions and must remain so to ensure effective AI risk management and regulatory compliance.

AI governance is a policy-driven framework focused on accountability, compliance, and risk management across the AI lifecycle, while runtime inspection is an operational security function handling real-time enforcement and anomaly detection. Conflating these functions undermines governance integrity and limits enterprise flexibility, particularly given the rise of SaaS-based and externally managed AI solutions, where runtime enforcement is not always applicable.

Through our extensive work with Fortune 500 enterprises, we’ve seen firsthand that successful AI governance extends beyond runtime enforcement to address regulatory reporting, lifecycle management, and business-wide AI oversight. The following key distinctions highlight why AI governance must remain an independent function to support enterprise-wide AI risk management effectively:

1. End-to-End Lifecycle Management is Fundamental
   Governance at scale requires end-to-end lifecycle management across all stages of AI development, execution, and deployment. Runtime inspection and enforcement serve an essential but narrow function within this lifecycle and cannot substitute for comprehensive governance practices, which encompass intake, inventory, risk tiering, risk and control management, model/asset/evidence management, syncing with the multitude of different enterprise systems (ITSM, GRC, Data Governance, Security, IT CMDB’s, CMS, etc.) involved in the lifecycle, testing and monitoring, documentation, traceability, audits, and compliance management.
2. Runtime Inspection is a Security Operations Function, Not Governance
   While runtime inspection provides critical security operations (SecOps) functions such as real-time input/output guardrails, these do not fulfill governance requirements. Governance is a process-driven approach that ensures organizational accountability, compliance, and risk management over time—not just at runtime. Real-time guardrails, even when risk-scored or tied to an AI inventory, are tools for runtime enforcement— and do not equate to governance. Governance ensures the appropriate design, deployment, and use of AI through policies, processes, and accountability frameworks that extend across vendors, models, and applications.
3. Regulatory Reporting and Human Oversight Requirements
   Jurisdictions such as the EU mandate detailed reporting on AI usage and emphasize human-in-the-loop oversight. These requirements extend far beyond runtime activities, demanding robust governance structures to provide auditability and ensure compliance.
4. Runtime Enforcement is Not Universally Applicable And Doesn’t Cover External Vendor Models
   Enterprises require freedom of choice to leverage a multitude of different AI solutions across a variety of different internal runtime environments (open source, cloud-based, proprietary), as well as closed, externally-managed runtime environments (e.g. SaaS-based AI solutions). In fact, most enterprises are using substantially more SaaS-based AI solutions, for which runtime enforcement is completely handled by the SaaS vendor, such that the customer cannot use their own runtime enforcement. Combining governance with runtime inspection assumes a level of coupling that limits flexibility and contradicts the modularity needed in enterprise AI systems across disparate business units and teams, which are increasingly choosing vendor and embedded AI systems, including agentic examples such as Salesforce Agentforce 2.0. Runtime enforcement mechanisms are often incompatible with external vendor models or certain deployment scenarios, making it impractical to assume that runtime functions will always tie back to governance capabilities.
5. Governance and Business Teams Should be Separate Lines of Defense
   Students should not grade their own papers, so it’s important that a governance team is separate and distinct from the business/AI owning teams. Governance tools are owned by the governance teams (called "2nd line of defense" in financial services) and runtimes are owned by the business teams (called "first line of defense"). We believe these functions should remain separate concerns to better manage risk.

In summary, while Runtime Inspection and Enforcement functions are vital for securing real-time AI interactions, they are separate and distinct capabilities from AI Governance. Conflating these categories risks oversimplifying the market landscape and undermining enterprise efforts to address AI risks comprehensively.

Expect the Value of AI Governance to Increase: Lessons from Gartner and Fidelity Investments

At Gartner’s Data and Analytics Summit in Orlando, Florida on March 3, 2025, Gartner announced that Fidelity Investments had successfully transformed its AI model operations by implementing a scalable AI governance framework that enhances efficiency, compliance, and business oversight. Like many large enterprises, Fidelity faced challenges in deploying AI models at scale, integrating disparate tools across teams, and ensuring compliance with regulatory and internal policies.

To address these challenges, Fidelity adopted a structured AI governance approach that:

• Increased the speed of AI model production by 2x
• Reduced time to manage models in production by 67%Ensured new governance capabilities that:
  
  • Ensure models adhere to regulatory standards
  • Ensure models work effectively
  • Optimize the model portfolio by eliminating redundant models

The implementation of AI governance has provided a replicable framework for scaling AI responsibly while ensuring compliance, operational efficiency, and effective business oversight. This case study demonstrates that AI governance is not merely a regulatory necessity but a strategic enabler for innovation and long-term AI success.‍

ModelOp Aligns with Gartner’s AI TRiSM Framework

Given ModelOp’s recognition in Gartner’s 2025 Market Guide for AI TRiSM, ModelOp’s AI Governance platform is strategically aligned with Gartner’s AI TRiSM framework, delivering robust capabilities that map directly to its four key layers:

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• AI Governance: ModelOp establishes an AI governance framework that ensures enterprises can enforce policies for risk management, compliance, and ethical AI across all AI initiatives, including generative AI and third-party models.
• AI Runtime Inspection and Enforcement: ModelOp integrates runtime inspection capabilities that enhance governance by providing visibility into AI operations while ensuring security measures are enforced in real-time.
• Information Governance: ModelOp automates AI model inventory and risk tiering, ensuring enterprises maintain comprehensive AI tracking and risk management.
• Infrastructure and Stack: ModelOp is technology agnostic and integrates with existing enterprise IT and AI infrastructure, enabling seamless AI governance across cloud, on-premise, and hybrid environments.

By providing an enterprise-wide AI governance solution, ModelOp aligns with Gartner’s recommendation that enterprises maintain independence from any single AI model or hosting provider.

ModelOp allows organizations to flexibly manage AI models across different platforms and providers, ensuring visibility, security, and compliance at scale.

Conclusion: AI TRiSM is Essential, and ModelOp Provides the Solution

As AI adoption accelerates, enterprises must proactively manage risks related to security, compliance, and trust. Gartner’s AI TRiSM framework highlights the need for comprehensive governance, runtime enforcement, and information governance to ensure AI operates securely and ethically. A key takeaway is the importance of remaining independent from any single AI model or provider, allowing flexibility in choosing AI solutions based on performance, risk, and cost rather than vendor constraints.

Beyond compliance, AI governance must ensure transparency, ethics, and automated policy enforcement across all AI models. While runtime inspection secures AI operations, governance provides the foundation for AI accountability, ensuring consistent risk and compliance standards. ModelOp aligns with AI TRiSM by delivering centralized AI inventory management, real-time monitoring, structured model tracking, and technology-agnostic integration across AI systems. By embracing AI TRiSM principles, enterprises can innovate responsibly, mitigate risks, and ensure compliance at scale, creating a trusted AI ecosystem for the future.

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