Enterprise AI Analysis
Mapping AI Risk Mitigations
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI risk mitigation frameworks is fragmented, uses inconsistent terminology, and has gaps in coverage. This paper introduces a preliminary AI Risk Mitigation Taxonomy to organize AI risk mitigations and provide a common frame of reference. The Taxonomy was developed through a rapid evidence scan of 13 AI risk mitigation frameworks published between 2023–2025, which were extracted into a living database of 831 distinct Al risk mitigations. The mitigations were iteratively clustered & coded to create the Taxonomy. The preliminary AI Risk Mitigation Taxonomy organizes mitigations into four categories: (1) Governance & Oversight: Formal organizational structures and policy frameworks that establish human oversight mechanisms and decision protocols; (2) Technical & Security: Technical, physical, and engineering safeguards that secure AI systems and constrain model behaviors; (3) Operational Process: processes and management frameworks governing AI system deployment, usage, monitoring, incident handling, and validation; and (4) Transparency & Accountability: formal disclosure practices and verification mechanisms that communicate Al system information and enable external scrutiny. These categories are further subdivided into 23 mitigation subcategories. The rapid evidence scan and taxonomy construction also revealed several cases where terms like 'risk management' and 'red teaming' are used widely but refer to different responsible actors, actions, and mechanisms of action to reduce risk. This Taxonomy and associated mitigation database, while preliminary, offers a starting point for collation and synthesis of AI risk mitigations. It also offers an accessible, structured way for different actors in the AI ecosystem to discuss and coordinate action to reduce risks from AI.
Executive Impact & Key Findings
Our analysis reveals the foundational elements and strategic implications of effective AI risk mitigation, offering clear pathways for enterprise leaders.
831
Distinct AI Mitigations Identified
4
Primary Categories
23
Subcategories
13
Foundational Documents Scanned
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This section outlines the context, definitions, and methods used for the AI Risk Mitigation Taxonomy. It highlights the fragmented landscape of AI risk mitigation frameworks and the need for a common frame of reference. The approach involved a rapid evidence scan of 13 foundational documents (2023-2025), extracting 831 distinct mitigations, and iteratively clustering them into a preliminary taxonomy. Large language models were experimented with as assistive tools but human authors conducted the final extraction and classification due to mixed results. The taxonomy organizes mitigations into four categories and 23 subcategories.
98%
Mitigations Classified by Taxonomy
Enterprise Process Flow
Extract mitigations from 4 documents
→
Cluster mitigations into initial taxonomy
→
Extract & classify from 1 additional document
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Update taxonomy for novel mitigations
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Extract & classify from all other documents
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Iterate until convergence
| Approach | Description | Key Features |
|---|---|---|
| Thematic Synthesis | A 'bottom up' method where concepts are iteratively analyzed to identify patterns and structure. |
|
| Framework Synthesis | A 'top down' method where concepts are coded against a pre-existing structure. |
|
The preliminary AI Risk Mitigation Taxonomy organizes 831 distinct mitigations from 13 documents into four primary categories and 23 subcategories. The categories are: Governance & Oversight, Technical & Security, Operational Process, and Transparency & Accountability. These provide a structured way to understand and discuss AI risk reduction actions.
4
Primary Categories in Taxonomy
Operational Process Controls Dominance
The largest share of mitigations (36%) fell under Operational Process Controls, with Testing & Auditing, Data Governance, and Post-deployment Monitoring being the most prominent subcategories. This highlights a strong emphasis in existing literature on practical, hands-on control over AI system lifecycle.
| Category | Description | Example Mitigations |
|---|---|---|
| Governance & Oversight | Formal organizational structures and policy frameworks. |
|
| Technical & Security | Technical, physical, and engineering safeguards. |
|
| Operational Process | Processes and management frameworks governing AI system lifecycle. |
|
| Transparency & Accountability | Formal disclosure practices and verification mechanisms. |
|
The analysis revealed that 'Risk Management' is widely discussed but inconsistently defined, leading to fragmentation. Many mitigations do not specify the risks they address, making implementation difficult. Future research should map mitigations to specific risks, explore organizational safety culture, and consider how mitigations differ across various actor types in the AI ecosystem. This taxonomy is a starting point for better coordination.
15%
Mitigations classified as Risk Management
Inconsistent Definition of 'Risk Management'
Despite 'Risk Management' being a widely referenced subcategory (15% of all mitigations), its definition and operationalization vary significantly across documents. This inconsistency highlights a critical gap in shared understanding and poses challenges for effective coordination in AI risk mitigation.
Future Research Directions
Map mitigations to risks
→
Explore organizational safety culture
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Examine actor-specific mitigation differences
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Improve coordination & accountability
Calculate Your Potential AI Risk Mitigation ROI
Estimate the impact of implementing a structured AI risk mitigation framework on your operational efficiency and cost savings.
Your Enterprise AI Risk Mitigation Roadmap
A structured approach to integrating the AI Risk Mitigation Taxonomy into your organization, ensuring robust safety and governance.
Phase 1: Initial Assessment & Gap Analysis
Conduct a thorough review of existing AI systems and practices against the new AI Risk Mitigation Taxonomy to identify current controls and critical gaps. Establish a dedicated cross-functional AI Risk Committee.
Phase 2: Strategy Development & Prioritization
Develop a tailored AI risk mitigation strategy, prioritizing actions based on identified risks, potential impact, and resource availability. Define clear ownership and KPIs for each mitigation.
Phase 3: Implementation & Integration
Implement selected mitigation controls across governance, technical, operational, and transparency domains. Integrate new processes into existing enterprise workflows and IT infrastructure, leveraging third-party expertise as needed.
Phase 4: Monitoring, Review & Iteration
Establish continuous monitoring mechanisms for AI systems and mitigation effectiveness. Conduct regular internal and external audits. Iterate on the strategy and controls based on performance data, emerging risks, and regulatory changes.
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