One misplaced line of code. One strategy document dropped into ChatGPT. One vendor cutting corners with an untested AI model. Shadow AI, the unauthorized or irresponsible use of AI, is all it takes for an enterprise to garner bad press, either from its own mistakes or those of its suppliers.
Regulators and customers don’t draw distinctions between a company and its supply chain. If a partner mishandles AI, the accountability falls back on the enterprise and can cascade into compliance failures, reputational damage and operational breakdowns.
AI is not just another office tool. It is a new category of risk that today’s third-party risk programs weren’t built to handle. Its rise is forcing risk leaders to rethink governance, oversight and accountability, both internally and across vendor ecosystems, to contain misuse before it becomes tomorrow’s crisis.
AI becomes its own risk domain
Every new era of enterprise risk forces companies to adapt. Data privacy rewrote the rules for collecting and storing information. Cybersecurity demanded constant vigilance against breaches. ESG extended accountability deep into supply chains.
Each of those domains matured gradually, with guardrails built over years. AI is different. It has arrived fully embedded in daily operations, often without governance. The result looks much like the early days of software as a service (SaaS), when ‘shadow IT’ spread unchecked. Everyone from sales to human resources gained access to SaaS tools, without having to involve the IT team because it was so accessible. Today’s version is ‘shadow AI’: Employees and vendors freely adopting generative AI tools with little oversight.
Generative AI tools, AI-enabled apps, AI agents and agentic AI all promise great benefits without revealing the potential risks, and the risks compound quickly. An employee pastes sensitive material into a chatbot, unintentionally exposing the company’s intellectual property (IP). A vendor uses AI to manage customer data without guardrails, sending trade secrets into public models. Outputs go unchecked and algorithms hallucinate or embed bias, skewing decisions in industries as critical as finance and health care. And the risk flows downstream, with enterprises facing potential regulatory, reputational or financial fallout.
AI’s hidden and dangerous risks don’t always involve bad actors. They often stem from human error and misunderstanding, which makes them harder to anticipate and easier to underestimate.
Governance in reset mode
AI has effectively reset the maturity of internal and third-party risk management programs. Unlike past technologies, where adoption preceded controls, AI demands both at once. Companies must evaluate risk while usage is already underway.
This is where many risk leaders are struggling. They ask: How do we evaluate the deployment of AI solutions inside our company and among our vendors? How do we gauge risks that are still evolving?
The short answer: Strategy must move in lockstep with safeguards. If adoption races ahead of controls, the enterprise absorbs unnecessary exposure. If controls stifle experimentation entirely, opportunities for growth are lost.
This balance is the crux of the challenge. Companies can’t afford to wait for perfect clarity, yet they also can’t leave adoption unchecked. The path forward is to confront the risks directly while still enabling innovation. That starts with six practical steps:
- Map internal AI usage. Identify where generative AI tools and AI-enabled apps are already in play, which platforms employees use, and for which tasks they’re used. Clarify what qualifies as sensitive information and ensure the staff understands the stakes.
- Evaluate vendors. Add responsible AI assessments to due diligence. Go beyond ‘Are you using AI?’ questions to queries about policies, ownership and safeguards. Smaller suppliers may use AI casually; larger ones may have AI policies in place. Standards differ widely. These assessments protect the enterprise and push suppliers to treat AI responsibly.
- Unify leadership. IT and business leaders must align on oversight. IT brings security and compliance expertise. Business leaders tie adoption to strategic goals. Shared accountability keeps innovation aligned with protection.
- Take a business-first approach. Encourage experimentation, but ground it in clear objectives and expectations. Define what the organization hopes to achieve, test use cases and reassess continuously. AI isn’t a ‘set and forget’ technology.
- Educate employees and vendors. Establish rules for safe use and require training, even short sessions like a quick 10-minute video. Employees should know which data must never leave the enterprise, and vendors should align with documented policies on acceptable AI practices.
- Set guardrails. Be prepared for human error. Reinforce training with technical controls. Block sensitive content from leaving secure environments, and clearly designate which AI tools are approved and which are prohibited.
AI creates opportunity, but it also creates accountability. Extending oversight into supply chains and embedding governance into business strategy determines whether AI becomes a catalyst for growth or a source of reputational damage. The question isn’t if the extended enterprise is using AI. It’s how and for what. Once you have that information, the right guardrails can be put in place to shape AI into a source of resilience rather than vulnerability.
