To stay competitive, businesses are moving aggressively to adopt AI quickly and at scale.
Yet few organizations actually understand the additional complexity of risks AI introduces, or how ill-equipped their current risk methodology is to manage it.
This tension is the heart of the challenge: AI risks are increasing exponentially and cannot be managed well without effective AI governance. AI governance can't succeed unless it is built as a cross-functional discipline that unites security, privacy, risk, legal, engineering, and the business around shared objectives.
Organizations are trying to navigate this monumental shift, and leaders can (and should) learn from their peers who are already building responsible, scalable AI programs.
Why AI risk is different — and why governance is paramount
AI introduces new classes of uncertainty that traditional security or third-party risk processes were never designed to evaluate. Even identifying what AI exists inside an environment is hard enough. Employees adopt tools on their own, third parties quietly embed AI features into their platforms, and internal teams begin experimenting with models before formal processes ever begin.
Security teams are adapting by layering AI discovery into existing visibility tooling and asset management. They are cataloging AI systems the same way they would any business-critical technology and using contractual protections to reduce exposure where employees rely on external tools. But visibility is only the beginning.
The deeper challenge is that AI systems are dynamic, probabilistic, and continuously evolving. Models learn from new data, vendors release new features, and system behavior can drift over time. This demands a shift away from static, point-in-time assessments and toward continuous monitoring and programmatic control.
It also demands a clear organizational stance on risk appetite. What is acceptable use? What types of data can flow into AI systems? Which functions must remain human-reviewed? Without governance guardrails, every team evaluates these questions differently — and the risk surface becomes impossible to manage.
A strong AI governance foundation gives risk teams what they need to work effectively:
- Clarity on purpose and use cases
- Defined categories of risk
- Expectations for transparency and documentation
- A shared process for determining build vs. buy
- Policies for data, model selection, and acceptable use
With these in place, AI risk management becomes streamlined instead of overwhelming.
Building the operating model: The AI governance committee
Most organizations accelerating AI adoption are standing up an AI governance committee to unify oversight and decision making. The committee functions as the connective tissue between innovation and responsible control.
Its composition is intentionally cross-functional. Privacy, security, risk, compliance, legal, IT, and strategy teams form the core. Engineering, product, and data leaders join as needed. Depending on the business model, operations or finance may take a central role as well.
The committee’s responsibilities typically include:
- Defining enterprise AI strategy and guardrails: This covers policies, acceptable use, model selection, and alignment with regulatory frameworks such as NIST, OECD, ISO standards, and the emerging global patchwork of AI regulations.
- Reviewing high-risk use cases: While ~80% of AI initiatives may move through standard procurement and technical assessment, the top tier requires deeper cross-functional evaluation of intended use, potential harm, customer impact, and organizational risk tolerance.
- Guiding build vs. buy decisions: Teams evaluate whether AI capabilities should be developed in-house or licensed from vendors, considering skill sets, security implications, and long-term cost.
- Ensuring transparency for customers and stakeholders: As organizations release AI-powered features, they must document purpose, models, data handling, and human-review expectations in clear, accessible artifacts such as AI transparency reports.
This structure not only safeguards the organization but also accelerates responsible innovation. Teams gain confidence because the rules are clear, the process is consistent, and the business understands how to move ideas from experimentation to production.
What does this mean for third-party risk management?
As organizations adopt AI, it is increasingly intersecting with third-party risk management in ways traditional frameworks were never designed to handle. Many vendors now embed AI into their platforms without prominently signaling it, which means risk teams must look beyond tools that explicitly market themselves as AI and identify where AI is operating within existing applications.
Security and privacy leaders emphasize the importance of updated intake and assessment processes that capture how vendors use AI, what data flows into their models, whether customer information is used for training, and how new features are introduced over time.
Because AI systems change continuously, the “set it and forget it” model of vendor evaluation falls extremely short. AI governance programs can provide the guardrails TPRM programs need: clear oversight, contractual protections, updated policies, and continuous monitoring that allows organizations to balance innovation with responsible risk management.
Turning governance into growth, step-by-step
Once governance structures are in place, organizations can safely operationalize AI adoption. The life cycle increasingly mirrors third-party risk management, but with important modifications.
Intake: This early-on part of the process now requires much richer context. Teams must capture inputs, outputs, data sensitivity, model lineage, expected outcomes, and potential for algorithmic drift or bias. The goal is not only to evaluate security and privacy exposure but to map how AI aligns to business purpose and regulatory expectations.
Assessment: This procedure expands in both breadth and depth. Legal, privacy, and security teams collaborate to determine whether vendor contracts include appropriate protections, whether data may be used for model training, and how updates will be communicated.
Monitoring: Always-on oversight. AI systems change rapidly, which means risk exposure can change in tandem. Organizations are embedding new AI-specific questions into reassessments, updating policies as models evolve, and maintaining continuous visibility into how vendors modify their AI features.
When organizations get this right, governance stops being seen as a blocker and instead becomes a growth enabler. Learn more from OneTrust’s CISO Tim Mullen and Head of Privacy and AI Governance Brett Tarr in this on-demand webinar.
