PARK CITY, Utah, Sept. 14, 2026 (GLOBE NEWSWIRE) -- The transition from data governance to AI governance will be a central theme at the fourth BIIA Technology Forum, taking place virtually on September 16, 2026 at 10:00 a.m. UK/1:00 p.m. Dubai/5:00 p.m. Singapore. ModelOp CEO Dave Trier will address the Governance of AI and examine how organizations can establish the controls, accountability and operational discipline necessary to safely scale AI and Agentic AI.
BIIA represents companies in the information services, credit reporting and data analytics industries across Asia Pacific and the Middle East. Its members operate in an industry where data quality, governance and trust have long been essential to delivering information products that support consequential financial and commercial decisions.
From Data Governance to AI Governance
For credit bureaus and business information providers, data governance has long been fundamental to product quality, regulatory compliance and customer trust. As governed data is increasingly used to train, ground and inform AI models and agents, however, the governance challenge extends beyond the data itself.
Organizations must now understand and control how AI systems use information, what decisions they influence, what actions agents are authorized to take, who is accountable for those actions and how AI systems are monitored throughout their lifecycle.
Just as data governance became both a business necessity and an opportunity for an industry built around trusted information, AI governance has the potential to become a new source of trust, differentiation and value.
“The credit and business information industry understands something fundamental about governance: trust doesn't happen by accident,” said Dave Trier, CEO of ModelOp. “This industry built its value on trusted data and developed the governance discipline necessary to protect that trust. Now AI agents are beginning to reason over that data, interact with enterprise systems and take actions with increasing autonomy. The next challenge is extending that same discipline from governing the data to governing what AI actually does. Done right, AI governance isn't simply about reducing risk. It can become a foundation for trust, faster innovation and new business opportunities.”
150,000 AI Agents Raise the Governance Stakes
The urgency is increasing as enterprises move from AI systems that primarily generate information toward agents capable of planning, reasoning and taking actions on behalf of people and organizations.
Gartner predicts that by 2028, the average Global Fortune 500 enterprise will have more than 150,000 AI agents in use—up from fewer than 15 in 2025. Yet only 13% of organizations believe they currently have the right AI-agent governance in place.
That scale fundamentally changes the governance problem.
Enterprises cannot realistically govern tens of thousands of autonomous agents through spreadsheets, manual inventories and periodic review committees. Organizations need to know which agents exist, what each is authorized to do, which systems and information they can access, which policies apply, who approved them and whether their behavior remains within acceptable boundaries in production.
For the credit and business information industry, where AI may interact with sensitive data and influence financial, credit, risk and commercial processes, establishing those controls is particularly important.
Singapore Advances a Framework for Agentic AI Governance
The BIIA Technology Forum also comes as APAC takes a leading role in developing practical approaches to governing increasingly autonomous AI.
Singapore's Infocomm Media Development Authority (IMDA) has developed a Model AI Governance Framework for Agentic AI to help organizations manage the risks associated with deploying Agentic AI while enabling responsible innovation.
The framework addresses organizations developing their own agents as well as those adopting third-party agentic solutions. It focuses on four areas:
- Assess and bound risks upfront, including defining agent autonomy and limiting access to tools, systems and data;
- Make humans meaningfully accountable, including establishing appropriate human oversight and approval;
- Implement technical controls throughout the agent lifecycle, including identity and access management, testing and monitoring; and
- Enable end-user responsibility through transparency, controls and education.
The framework also addresses emerging challenges involving third-party agents and multi-agent systems.
For BIIA's audience across APAC and the Middle East, Singapore's approach highlights a significant evolution in AI governance: moving from high-level principles and policies toward operational controls over what AI systems and agents are actually permitted to do.
Governance Must Become Part of AI Delivery
Trier's presentation will examine why Agentic AI requires organizations to rethink governance as an operating discipline rather than a compliance checkpoint.
As enterprises deploy growing portfolios of ML models, GenAI applications, AI agents and third-party AI systems, governance based on periodic reviews, disconnected approvals and manual processes becomes increasingly difficult to scale.
The challenge is no longer simply whether an organization has an AI policy. It is whether that organization has an operating model capable of translating policy into controls and consistently applying those controls across the AI lifecycle—from initial use case and risk classification through testing, approval, deployment, monitoring and retirement.
For Agentic AI in particular, enterprises need visibility and accountability as agents interact with data, applications, external tools, people and other agents.
Trier will discuss governance by design, an approach that embeds governance directly into AI delivery rather than adding controls after development. This means creating repeatable processes to discover and inventory AI; classify systems according to business context and risk; translate policies into controls; establish permissions and accountability; orchestrate testing and approvals; maintain auditable evidence; and continuously monitor AI after deployment.
The objective is to allow enterprises to achieve two priorities simultaneously: business agility and operational maturity.
Rather than forcing organizations to choose between innovation and governance, governance by design makes controls part of the mechanism through which AI is delivered—allowing enterprises to move faster because governance is systematic rather than slower because it is added at the end.
ModelOp Provides an Enterprise Operating Layer for AI Governance
ModelOp provides an Enterprise AI Command Center that serves as the system of record for enterprise AI operations across ML, GenAI, Agentic AI and vendor AI.
Rather than replacing existing MLOps, GRC, ITSM or data management technologies, ModelOp operates above and across those systems, connecting AI use cases, models and agents with the workflows, controls, approvals, evidence and operational intelligence required to manage them throughout their lifecycle.
Powering the Enterprise AI Command Center is ModelOp's AI Delivery Engine (MADE™), an agentic-powered engine that manages AI information, workflows and decisions across teams, systems and the AI lifecycle.
MADE helps organizations discover enterprise AI, classify risk and translate policy into applicable controls, orchestrate reviews and approvals, automate testing and validation, streamline evidence collection and audits, support governed deployment, and monitor AI performance, risk and business value.
Together, these capabilities enable governance to become part of AI delivery rather than a separate process imposed after development.
About the BIIA Technology Forum
The fourth BIIA Technology Forum which is a members only event will be held virtually on September 16, 2026 and will focus on the Governance of AI.
The forum brings BIIA members together to examine global technology developments, their implications for the credit reporting and business information industries, and regulatory and governance issues affecting emerging technologies.
Date: September 16, 2026
Time: 10:00 a.m. UK / 1:00 p.m. Dubai / 5:00 p.m. Singapore
Format: Virtual
Frequently Asked Questions
Why is AI governance important to the credit and business information industry?
Credit bureaus and business information providers have historically relied on strong data governance to protect data quality, integrity and trust. As that data increasingly powers AI models and agents, organizations must extend governance to how AI systems use information, influence decisions and take actions. AI governance represents a natural next frontier for an industry built around trusted data.
Why does Agentic AI create new governance challenges?
AI agents can plan, reason and take actions with varying degrees of autonomy. They may access sensitive data, interact with enterprise systems and communicate with other agents. This increases the importance of identity, permissions, testing, monitoring, runtime enforcement, human oversight and accountability.
What is Singapore's Model AI Governance Framework for Agentic AI?
Singapore's IMDA developed the framework to provide practical guidance for organizations developing or deploying AI agents. It focuses on bounding agent risks, maintaining meaningful human accountability, implementing technical and lifecycle controls, and enabling responsible use through transparency and education.
What will Dave Trier discuss at the BIIA Technology Forum?
Trier will examine the credit and business information industry's transition from data governance to AI governance, the implications of rapidly expanding Agentic AI adoption, and why organizations need governance embedded throughout AI delivery rather than applied as a separate compliance process.
About ModelOp
ModelOp provides an Enterprise AI Command Center for enterprise AI leaders and serves as the system of record for enterprise AI operations. It combines workflows and operational intelligence in one operating layer to industrialize ML, GenAI, Agentic AI and vendor AI above existing MLOps, GRC, ITSM and data management systems without replacing them.
ModelOp is used by complex and regulated organizations across financial services, insurance, healthcare, defense, manufacturing and consumer products. Forrester and IDC have recognized ModelOp for its AI lifecycle management and governance capabilities.
ModelOp has also received multiple industry awards, including the Netty Awards' Best AI Governance Software Award, Business Intelligence Group's Artificial Intelligence Excellence Award, and the Pinnacle Awards for Artificial Intelligence Diamond Award for Responsible AI Platform.
Follow ModelOp on LinkedIn or visit ModelOp.com to learn more or schedule a demo.
Media Contact
Ria Romano, Partner
RPR Public Relations, Inc.
786-290-6413
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