The number of companies adopting generative AI is growing rapidly. According to ITWorld's 2026 IT Outlook Report, more than 85% of companies are expected to use generative AI in 2026, and 74% said their AI-related budgets are increasing. AI is already becoming a basic condition for competition rather than an option. However, fast adoption does not mean safe operation. Trust in generative AI is still low. Lack of trust in incorrect information and results (61.3%) and the risk of security and personal information leaks (53.3%) are the main concerns cited by Korean companies.
Gartner offers a more direct warning. It projects that by 2026, more than 80% of unauthorized AI transactions will stem not from malicious attacks but from internal policy violations, such as oversharing of information, inappropriate use, and misguided AI behavior. (Source: Gartner Market Guide for AI TRiSM, 2025) Threats come from inside before they come from outside.
The next step in AI adoption is clear: operating AI in a trustworthy way, in other words, AI governance. Based on proven frameworks, this article explains why AI governance is needed, how it should be structured, and what actually needs to be controlled.
☑️The Reality of AI Adoption: The Gap Between Rosy Forecasts and Conditions on the Ground
Many companies feel pressure to adopt AI. According to TechSurvey's 2025 IT Outlook Report, a majority of respondents (63.1%) said they feel pressure regarding generative AI, and this pressure tends to intensify as an organization's AI maturity increases.
Looking at the distribution of AI adoption maturity among Korean companies makes the reality clearer. Based on ITWorld's 2026 IT Outlook Report, maturity is distributed as follows.
Most companies are concentrated in Stages 1 and 2. They have started using AI but have not yet moved to the stage of operating it safely across the entire organization. And as they move into Stage 3 and beyond, they commonly hit the same wall: AI governance.
CIO.com puts it this way: "When a POC moves from the sandbox to production, the risk rises sharply." Things that seemed fine in an experimental environment appear as entirely different risks in real production. Governance is not something to put in place after AI adoption is complete; it must be designed alongside adoption from the very beginning.
☑️The Structure of AI Governance: The AI TRiSM 4-Layer Framework
How should AI governance be approached systematically? Gartner answers this with a framework called AI TRiSM (AI Trust, Risk, Security Management). According to the Gartner AI TRiSM Market Guide 2025, "organizations that operationalize AI TRiSM will achieve a 50% improvement in AI model adoption, achievement of business goals, and user acceptance."
AI TRiSM structures AI governance into four layers. Each layer does not operate independently but functions as part of an interconnected, integrated system.
Layer 1. AI Governance
This is the policy and management process layer that ensures AI complies with corporate policies, ethical guidelines, and regulatory requirements. Its core elements are building an AI asset inventory, documenting use cases, risk assessment and approval procedures, audit traceability, and compliance with legal policies and regulations. It serves as the "control tower" that transparently manages all AI activity within the organization.
Layer 2. Runtime Inspection & Enforcement
This is the security layer that monitors interactions between AI and users and blocks threats at execution time. The Gartner 2025 report places particular emphasis on the importance of this layer, because without real-time monitoring and automated guardrails, policies have little effect in production environments. Key areas of management include prompt injection, jailbreak attempts, exposure of PII and sensitive information, hallucination detection, and agent monitoring.
Layer 3. Information Governance
This layer is responsible for identifying, classifying, protecting, and controlling access to the data used by AI. It includes vector DBs, RAG (retrieval-augmented generation), data lineage management, DLP (data loss prevention) integration, and data localization. The key is ensuring the safety of the data that AI learns from or references.
Layer 4. Infrastructure & Stack Security
This layer addresses the security of the infrastructure on which AI workloads run and of the entire AI stack. It covers API security, workload protection, confidential computing, MLOps pipeline security, and support for multi- and hybrid cloud environments. No matter how well governance is established in the upper layers, it is useless if the infrastructure is vulnerable. Notably, Gartner projects that by 2028, 25% of large organizations will have dedicated AI governance teams. This is a dramatic change from less than 1% in 2023. (Source: Gartner Market Guide for AI TRiSM, 2025) AI governance is already becoming a core organizational capability at leading companies.
☑️In the Agentic AI Era, the Nature of Threats Fundamentally Changes
If AI TRiSM provides the structure for governance, OWASP shows the reality of actual threats. In December 2025, the OWASP GenAI Security Project officially released the "OWASP Top 10 for Agentic Applications 2026," completed through more than a year of research and review by over 100 security experts. This list matters not simply because it enumerates new security threats. It matters because, as AI evolves beyond a simple response tool into agentic AI that plans and executes tasks on its own, the nature of threats itself has changed. The question is no longer "What if the LLM gives a wrong answer?" but "What if the agent takes the wrong action?"
The top 10 security threats for agentic AI identified by OWASP are as follows.
In the Korean enterprise environment in particular, AI threats in network-separated environments are also an important consideration. Network separation provides security at the physical perimeter, but it cannot prevent integrity breakdowns caused by AI acting as an autonomous insider. The Financial Security Institute's AI Red Team calls this "the paradox of network separation." Because AI agents can generate large volumes of transactions in parallel and enable automated, large-scale propagation, existing network separation security frameworks alone have limits in responding.
Ultimately, what AI TRiSM and OWASP both emphasize converges on one thing: Observability, Tracing, Explainability, and Evaluation. If you cannot see what AI is doing, you can neither trust it nor control it.
☑️What Should Be Controlled in AI Services?
To actually execute AI governance, it must be clear what to control and how. There are six major areas that organizations must manage to operate AI in a trustworthy way.
1. AI Assets
"Is the inventory of AI assets within the organization being managed?" The starting point of governance is understanding the current state. If an organization does not know which AI models and services are being used internally, control itself is impossible. Detecting and managing Shadow AI adopted indiscriminately without IT department oversight is particularly important. Given Gartner's projection that more than 80% of unauthorized AI transactions will stem from internal policy violations by 2026, gaining visibility into AI assets is essential, not optional.
2. Access Control / Authentication / Authorization
"Who is using AI, and are their privileges controlled?" Users and roles that can access AI systems must be clearly defined, and access must be controlled according to the principle of least privilege. In particular, in environments where AI agents call external systems and APIs on behalf of people, identity authentication and privilege management for agents are essential. This is also why OWASP ASI03 (ID & Privilege Abuse) was identified as a key threat for agentic AI.
3. Data Security
"Is sensitive data leakage blocked, and is data protected from malicious attacks?" AI services process vast amounts of data. Personal or confidential information can be leaked when accessing internal documents through RAG pipelines or passing user inputs to LLMs. DLP (data loss prevention) and guardrail configuration are the key control measures, and organizations must also prepare for attacks that poison the data sources themselves, such as OWASP ASI06 (Memory & Context Poisoning).
4. Service Flow
"Is the AI processing flow observed and traced?" The entire process from when an AI service receives a user's query to when it generates a response must be traceable. Only by tracing which prompt was entered, which documents were referenced through RAG, and how the LLM generated its response can the cause be identified and improvements made when problems occur. AI governance begins with Observability and Tracing.
5. Performance / Quality
"Are the results of AI services accurate and trustworthy?" An AI model is not finished once it is deployed. Over time, performance can degrade (drift), or accuracy on certain types of questions can decline. A framework is needed to continuously evaluate and improve metrics such as summarization accuracy, Q&A accuracy, RAG relevance, and hallucination rates. The principle that "you cannot improve what you cannot measure" applies equally to AI operations.
6. Resources / Cost
"Is resource usage monitored, and is it operated with optimized costs?" LLM API call costs and GPU compute costs rise sharply as AI services scale. This is also why an IDC survey found that "unpredictable pricing is the biggest obstacle to implementing generative AI in the enterprise." Token usage, latency, cost analysis, and automatic scaling management determine the sustainability of AI operations.
☑️AI Governance Solutions from CloudNetworks
Realizing the six control areas above requires solutions specialized for each area.
CloudNetworks designs and implements AI governance frameworks together with organizations, based on proven partner solutions in the areas of AI observability and evaluation, authentication and authorization, and threat detection and blocking.
Arize AI — The Core Platform for Observability, Tracing, Explainability, and Evaluation
Arize AI is an AI engineering platform that realizes, in agentic AI environments, the four keywords commonly emphasized by major frameworks including AI TRiSM and OWASP: Observability, Tracing, Explainability, and Evaluation. Through tracing, it collects and visualizes AI app execution at the span level to identify bottlenecks, errors, and prompt issues, and it continuously measures the quality and performance of AI apps through automated evaluation using LLMs (LLM as a Judge). It provides a variety of evaluation criteria, including summarization accuracy, Q&A accuracy, RAG relevance, hallucination, and toxicity, and these can also be customized.
Auth0 — Secure Authentication and Authorization for AI Apps
Authentication and authorization are essential elements of AI app development. Auth0 is a Customer Identity Platform that enables AI to accurately identify users, ensures data access that takes user permissions into account, and securely handles even API calls and asynchronous tasks. In environments where AI agents interact with external systems on behalf of people, it precisely controls who accesses what and with which privileges.
Cisco AI Defense — AI Asset Discovery, Threat Detection, and Runtime Protection
Cisco AI Defense automatically identifies AI assets across cloud, SaaS, and on-premises environments, detects vulnerabilities in AI models and apps, and evaluates prompts and responses to block malicious prompts and protect sensitive information. With detection technology based on Cisco's AI models and training data, it responds in real time to threats arising in AI runtime environments.
☑️Why AI Governance Should Start Now
AI governance is not something to put in place after AI adoption is complete. There is no need to have every framework in place right away. A realistic approach is to start with controls suited to your current stage of AI adoption maturity and expand them step by step. Gartner projects that by 2028, 25% of large organizations will have dedicated AI governance teams. Today, the figure is less than 1%. Leading companies are already moving.
As an official partner of Arize AI, Auth0, and Cisco AI Defense, CloudNetworks helps organizations operate AI in a trustworthy way. For inquiries ranging from product adoption to building an AI governance framework, please contact CloudNetworks.
