A Journey Guide to Manage AI Governance, Trust, Risk and Security
3 March 2026 - ID G00821153 - 21 min read
By Deepak Seth, Svetlana Sicular, and 3 more
Successfully managing AI governance, trust, risk and security is key to realizing value from AI investments. This guide enables AI leaders to assess their maturity and execute strategic actions that drive AI success.
Strategic Planning Assumptions
- By 2027, AI governance will become a requirement of all sovereign AI laws and regulations worldwide.
- Through 2027, most AI projects will fail to meet expectations due to their failure to focus on data governance.
- By 2027, AI governance and responsible AI capabilities will be part of 75% of AI platforms, making them the main area of competition.
- By 2028, 60% of CPOs will fail to realize the anticipated value of advanced analytics due to poor D&A governance.
Analysis
Gartner’s Journey Guides for data and analytics (D&A) leaders offer step-by-step insights and recommendations to help them address challenges facing them. These guides adapt to varying factors, such as tenure, influence and organizations’ D&A culture. To reflect latest guidance, tools and recommended content, these guides are refreshed every quarter. Revisit regularly to stay aligned with evolving trends and opportunities.
Updates in this release:
- Newer recommended readings across steps
Note: Some recommended content may not be available as part of your current Gartner subscription.
AI governance is a significant challenge for leaders responsible for AI (CDAO, AI leader, etc.) due to its rapidly evolving nature and potential to amplify human biases. Beyond governance, leaders responsible for AI must tackle critical risks, such as AI’s lack of transparency, privacy concerns, security vulnerabilities, environmental sustainability and the unpredictability of AI-related operational costs.1 Addressing these multifaceted risks is essential for maximizing AI benefits while minimizing potential downsides. Effective AI governance, trust and risk management are thus crucial for realizing value from AI investments.
A successful governance framework balances enforcement, enablement and engagement in governance. This journey guide outlines the critical steps on the AI governance journey: gaining foresight, building the value case, executing strategies and scaling to maximize value.
The steps outlined in Table 1 ensure your organization is on the right path to transparent, accountable, compliant, secure, sustainable and ethical AI deployment, aligning with societal demands while facilitating progress of AI use cases. Use the links in the sections below to navigate the questions on the AI governance journey path.
While this journey is presented as a sequential set of steps, it is iterative. You can begin navigating this journey guide from any step based on your organization’s current state and future ambition.
AI Governance, Trust, Risk and Security Management Journey Steps
Step 1: Gain Foresight and Get Grounded | Step 2: Build the Value Case and Gain Executive Buy-In | Step 3: Execute and Implement | Step 4: Scale Up and Manage Change |
Source: Gartner
Research Highlights
Some recommended content may not be available as part of your current Gartner subscription.
Step 1: Gain Foresight and Get Grounded
The journey begins with gaining foresight and grounding your organization in the fundamentals of AI governance. This includes understanding how to get started, identifying the dimensions of AI governance, and discussing AI risk, trust and governance imperatives with the council.
What is required to manage AI governance, trust, risk and security?
Effectively managing AI governance, trust, risk and security requires a structured approach to ensure that the organization’s strategic AI ambitions are aligned to the organization’s responsible AI policy and addresses secure deployment using trust, risk and security management (TRiSM) controls. Without such a structure, organizations risk mismanaging AI initiatives and exposing themselves to security vulnerabilities.
Key steps include:
- Establish an AI council: Form an AI council under the supervision of an AI leader that aligns with C-suite executives’ priorities and includes members with expertise in business strategy, data, AI, risk, ethics and IT.
- Develop foundational security: Ensure foundational security in cloud, data and applications before implementing AI-specific controls.
- Implement AI-specific controls: Use tools like AI governance platforms and AI usage control that implement AI TRiSM controls to support trusted and secure use of AI.
- Secure AI application use: Block unapproved apps and use guardrails to secure and protect AI application inputs and outputs.
- Deploy and monitor security controls: Implement practical security and trust controls, such as sensitive prompt filtering and content safety filters, and stay updated on academic research and evolving trust and security measures.
The journey begins with gaining foresight and grounding your organization in the fundamentals of AI governance. This includes understanding how to get started, identifying the dimensions of AI governance and discussing AI risk, trust and governance imperatives with the council.
How do I build an AI governance framework?
Effective AI governance hinges on the ability to ask and answer critical questions across various dimensions. Without this capability, organizations may struggle to make informed decisions, leading to misaligned AI initiatives and potential risks.
Establishing effective AI governance involves three key steps:
- Identify dimension owners: Assign owners to organizational, societal, customer-facing and employee-facing AI dimensions.
- Differentiate decision rights: Leverage the unique expertise and perspectives of these owners to differentiate their decision rights.
- Address key dilemmas: Align each governance decision with the expertise needed to resolve the underlying dilemma.
How do I identify and mitigate top AI risks based on use cases?
AI reshapes enterprise risk landscapes and transforms operations, necessitating swift adoption of technical and operational controls. Without proactive risk management, organizations face sanctions, immediate financial damage and significant reputational risks.
Identifying and mitigating top AI risks based on use cases involves the following steps:
- Adopt a proactive AI risk management framework: Account for the evolving AI regulations, which demand a framework to prevent harm to people and individuals.
- Identify key risks: Focus on behavioral risks (accuracy, bias, outdated information), transparency risks (explainability, disclosure), and security and data risks (privacy, misuse).
- Anchor projects in core principles: Uphold fairness, transparency, explainability, reliability, privacy and security in AI projects.
- Align AI governance with use cases: Categorize use cases based on cost, complexity, risk, value and competitive impact, tailoring governance levels to each category’s risk profile.
Recommendations:
- Assemble diverse stakeholders to form an AI council under an AI leader for strategic direction.
- Develop a security framework to protect data and ensure system integrity.
- Create tailored controls to address unique AI risks and ensure fairness.
- Integrate security practices throughout the AI life cycle.
- Use automated tools and regular reviews for continuous security updates.
- Assign ownership based on expertise to ensure informed decisions.
- Foster collaboration and knowledge sharing for comprehensive decision making.
- Implement proactive risk identification and mitigation.
- Prioritize critical risk areas.
- Embed transparency and fairness into policies.
- Customize governance to manage specific use-case risks effectively.
Step 2: Build the Value Case and Gain Executive Buy-In
Step 3: Execute and Implement
Step 4: Scale Up and Manage Change
Finally, scale and realize value by managing AI risk at scale. Prepare for regulatory risks, establish scalable AI ethics and responsible AI practices, and set up an AI council led by an AI leader with defined roles and responsibilities. Additionally, adapt your D&A governance to support AI and leverage AI literacy programs to bolster AI governance.
What technology approaches, practices and tools are emerging to manage AI risks?
Organizations are increasingly adopting AI technologies, but with this adoption comes the challenge of managing associated risks effectively. Without a structured approach to managing AI risks, organizations face issues such as ethical breaches, security vulnerabilities and misaligned AI initiatives that could undermine the overall success of their AI strategies.
To manage AI risks effectively, organizations must take a multifaceted approach:
- Strategic alignment and vision: Begin by understanding your strategic context and defining your AI vision and success measures.
- Governance and principles: Establish AI principles and governance frameworks to ensure alignment between business and AI capabilities.
- Technology reference model (TRM): Build an AI TRM to guide the deployment and integration of AI technologies.
- Capability roadmap: Design a tailored AI capability roadmap to outline the steps needed to achieve your AI vision.
- Ethical standards: Set responsible and ethical standards to guide AI development and deployment.
In addition, enforce these principles and frameworks:
- Tech stack implementation: Deploy the necessary technology stack to support AI initiatives.
- AI TRiSM: Implement AI TRiSM technology to enforce enterprise AI policies.
- Decision-making framework: Use Gartner’s five-phase approach to create an AI decision tree, guiding stakeholders through consistent and informed decision making.
Finally, scale and realize value by managing AI risk at scale. Prepare for regulatory risks, establish scalable AI ethics and responsible AI practices, and set up an AI council led by an AI leader with defined roles and responsibilities. Additionally, adapt your D&A governance to support AI and leverage AI literacy programs to bolster AI governance.
How can AI literacy programs be used to support AI governance?
Leveraging AI literacy programs to educate and train stakeholders on AI fundamentals, benefits, risks and the importance of responsible and ethical use is essential for robust AI governance. Without such programs, organizations risk misinformed use of AI systems, leading to potential ethical and operational issues.
Key steps include:
- Introduce AI literacy programs: Educate stakeholders on AI fundamentals, benefits, risks and the importance of responsible and ethical use.
- Comply with regulatory requirements: Address Article 4 of the EU AI Act, which mandates AI literacy given users’ ability to instruct AI systems conversationally.
- Introduce the AI governance framework: Detail personas based on roles and responsibilities and provide training on AI-specific technology and data policies, such as data security, bias mitigation and model accuracy.
- Use hands-on workshops and case studies: Demonstrate practical applications through interactive sessions.
- Establish ongoing initiatives: Keep stakeholders updated on AI advancements and regulatory changes through continuous education efforts.
Recommendations:
- Drive strategic alignment in managing AI risks.
- Implement robust AI governance.
- Uphold ethical standards in AI practices.
- Establish a solid technological foundation.
- Develop comprehensive risk management frameworks.
- Provide comprehensive education on AI fundamentals.
- Secure compliance with regulatory requirements.
- Offer detailed governance training.
- Conduct practical workshops on AI.
- Maintain ongoing initiatives for workforce AI literacy.
- Support robust and responsible AI governance through education.