Responsible AI Governance for Oracle Application Owners

Artificial Intelligence is rapidly reshaping enterprise operations. Some organizations are still experimenting, while others have embedded AI across workflows, products, customer interactions, and decision-making.

As adoption accelerates, governance is lagging—and in many cases, being overlooked.

For Oracle application owners, CIOs, and enterprise architects, this gap creates unmanaged risk at scale. Responsible AI governance is not optional; it’s a business requirement.

In this blog, we’ll explore the current state of enterprise AI governance, where companies are succeeding, where they are falling short, and best practices for implementing a strong governance program.

“The current generative AI adoption rate of 54.6% exceeds the 19.7% adoption rate of the personal computer three years after the first mass-market computer, and the internet's 30.1% adoption rate three years after the internet was opened to commercial traffic.” — Federal Reserve Bank of St. Louis, November 2025

AI adoption: A familiar pattern with higher stakes

AI may feel like a shiny new ball, but organizations have faced transformative technology waves before—ERP, client/server, Y2K, the Internet, BYOD, SaaS, GDPR compliance.

In each case, successful companies recognized that adoption was not “just another IT project.” It was a strategic business initiative requiring enterprise-wide oversight, executive sponsorship, and cross-functional alignment. Steering committees with departmental representation were key.

Organizations adopting AI need a formal AI Governance Committee to provide accountability and coordination for managing regulatory and operational risk. It operates like a steering committee but focuses on responsible AI governance, with representation from finance, IT, operations, product, sales, marketing, legal, and supply chain. For many companies—including those managing ERP, HCM, and supply chain systems—this approach mirrors how enterprise applications were successfully implemented.

Enterprises can also extend a Project Management Office (PMO) to oversee AI initiatives and report to the AI Governance Committee, providing structure for large-scale programs. Legal teams often take the initial lead, working with IT and business units to develop governance programs. However, governance must extend beyond policy to include processes for reviewing, approving, monitoring, and auditing AI systems.

Leading organizations are:

• Establishing a formal statement of responsible AI use policies
• Publishing internal and external statements of AI principles for review and approval
• Defining risk classification models for AI systems
• Assigning accountability for AI lifecycle oversight for ongoing monitoring

Visibility first: Addressing the rise of shadow AI

One of the most immediate governance challenges is visibility.

AI impacts all parts of your business: employees experimenting with tools, business units embedding AI into workflows, and vendors introducing AI capabilities by default. While AI enables efficiency, it also introduces risk.

In Delinea’s 2026 Identity Security Report, organizations reported a clear visibility gap. Nearly half of respondents (46%) acknowledged that their identity governance around AI systems was deficient. 90% of organizations reported gaps in identity visibility across their environment, and that gap was most pronounced in AI-related contexts where activity is harder to track.

This leads to the question: how can an organization enforce governance if it doesn’t know where AI is being used? And it has created a new risk category: shadow AI.

Remember shadow IT during the COVID-19 pandemic? During the shift to remote work, organizations struggled to secure unsanctioned SaaS tools adopted outside IT oversight, creating security and data privacy risks.

The lesson with shadow AI remains the same: you can’t govern what you can’t see.

For enterprise environments governance begins with visibility:

• Understanding where AI is used across enterprise applications
• Identifying AI-enabled workflows and integrations
• Assessing associated risks

Organizations addressing this challenge are deploying tools to inventory AI usage across applications, platforms, and AI agents. Visibility is the foundation for reducing risk and enforcing governance.

To learn more about the 5 steps in a successful framework for securing AI agents, read this related blog: Securing AI agents in business applications.

Training and awareness: Governance beyond documentation

Policy without awareness fails.

Once AI policies are defined, organizations must educate employees, partners, and customers to embed them into daily behavior—especially where AI is integrated into enterprise processes.

A strong reference point is data privacy. Regulations like GDPR and CCPA drove organizations to implement training programs that are now standard practice.

The same approach applies to AI governance.

Organizations should incorporate AI into existing training programs rather than building new ones. This includes:

• Annual employee training on responsible AI use
• Extending guidance to partners and vendors
• Embedding AI into existing security and compliance programs

For enterprise users—finance teams, HR system users, and supply chain planners—this ensures AI is used responsibly in processes that directly impact business outcomes.

Responsible AI governance enables growth

These elements represent core components of an enterprise AI governance program, but they are not the only requirements. For organizations running Oracle applications and other critical business applications, this means integrating AI governance into existing structures—not treating it as a separate initiative.

For Oracle application owners, CIOs, and GRC leaders, the focus should be on:

• Establishing cross-functional governance
• Gaining visibility into AI usage
• Extending existing training and compliance programs

Don’t be afraid to rely on past successful approaches when deploying new technologies. AI, no doubt, is a shiny ball, maybe the shiniest new technology ever, but its governance does not need to be designed from scratch to identity and mitigate AI risks.

Read Delinea’s recent whitepaper, Securing the Use of Generative AI Technologies, to learn more about the key elements of a strong AI governance framework and strong controls to implement.