AI governance is no longer a future problem; it's an operational crisis happening right now. While policymakers debate regulations, organizations are discovering that the real pressure to manage AI risks comes not from legislation, but from the incidents AI systems are already creating and the legal liability that follows.

Why Are AI Incidents Outpacing Regulations?

The gap between AI adoption and governance has become impossible to ignore. According to IBM's 2025 Cost of a Data Breach Report, 1 in 6 data breaches now involves attackers using AI, most often through AI-generated phishing and deepfake impersonation. What makes this shift particularly concerning is not just the presence of AI in attacks, but its scalability. AI enables attackers and internal systems to generate incidents at volume, transforming what were once isolated events into repeatable patterns that organizations must manage consistently at scale.

The problem is compounded by a fundamental ambiguity: there is no shared definition of what constitutes an "AI incident." Traditional incident frameworks, centered on data breaches or security failures, do not fully capture the realities of AI systems. AI introduces a broader category of "events": harmful outputs, hallucinations, biased decisions, or even market-moving misinformation. These may not trigger traditional incident thresholds, but they can still create real legal, reputational, and financial consequences.

Meanwhile, 63% of organizations reported they do not yet have formal AI governance policies in place, according to the same IBM report. This lack of preparedness underscores a broader issue: governance strategies often exist as principles rather than operational capabilities.

How Is Litigation Becoming the Primary Driver of AI Governance?

While global AI regulation continues to evolve, it remains fragmented and uncertain. Legal accountability, however, is already established. Organizations are responsible for the outcomes of the technologies they deploy, even when AI generates those outcomes. This shifts the urgency dramatically.

Zach Burnett, CEO of RadarFirst, and Kalinda Raina, Vice President and Chief Privacy Officer at Airbnb, explained this dynamic at the IAPP Global Summit 2026 in Washington, DC:

"Litigation risk is becoming the primary forcing function behind AI governance, accelerating the need for operational readiness today, not years from now."

Zach Burnett, CEO of RadarFirst, and Kalinda Raina, Chief Privacy Officer at Airbnb

Organizations cannot wait for regulatory clarity when liability already exists. In practice, the threat of lawsuits and board-level accountability is pushing companies to build incident management infrastructure faster than legislation ever could.

Steps to Build an Incident-First AI Governance Model

To close the gap between AI adoption and governance, organizations must build structured event and incident management programs that operationalize AI governance. Here are the key operational steps:

• Detection and Monitoring: Implement systems to detect AI-driven events and anomalies in real time, capturing outputs that may not fit traditional incident categories but could still create legal or reputational risk.
• Threshold Definition: Define clear escalation thresholds for different types of AI-generated events, distinguishing between minor anomalies and incidents that require immediate board or legal review.
• Standardized Triage: Establish standardized triage and risk assessment processes so that every AI-related event is evaluated consistently and documented for audit purposes.
• Auditable Response: Enable consistent, auditable response processes that create a clear record of how incidents were identified, assessed, and resolved, protecting the organization in litigation.

Incident management is no longer a downstream function. It is the mechanism through which AI governance becomes real and defensible in court.

What Role Is Government Playing in Pre-Release AI Testing?

While litigation drives internal governance, governments are also taking a more active role in overseeing AI development. Google, Microsoft, and xAI have agreed to share unreleased versions of their AI models with the U.S. government so that these systems can be tested before they become publicly available. The evaluations are conducted by the Center for AI Standards and Innovation (CAISI), part of the U.S. Department of Commerce.

According to CAISI Director Chris Fall, independent and technically rigorous evaluation methods are necessary to fully understand the impact of frontier AI on national security. Fall stated that the expanded collaboration with the AI industry enables the institute to conduct security reviews more quickly and on a larger scale, as AI technology evolves at breakneck speed.

The tests focus on national security risks, including cybersecurity, biosecurity, and the potential use of AI in chemical weapons. This gives government agencies access to models before they are commercially rolled out. The collaboration follows earlier agreements that OpenAI and Anthropic made with the Biden administration about two years ago. Since then, CAISI has conducted dozens of evaluations of advanced AI models, including systems that were not yet publicly available.

It is notable that this announcement comes at a time when the Trump administration has taken a cautious stance toward regulating artificial intelligence, wanting to prevent strict oversight from slowing innovation. However, concerns about AI risks appear to be growing within Washington. The New York Times reported that the Trump administration is working on a potential executive order regarding AI governance, under which technology companies and government agencies would jointly establish a formal review process for new AI models.

What Does This Mean for Organizations Right Now?

The convergence of litigation risk, incident management demands, and emerging government oversight creates a clear imperative: organizations cannot afford to wait for regulatory clarity. The incidents AI will create are already happening. The legal liability is already established. And the government is already testing models before release.

Companies that invest in incident-first governance models will be best positioned to manage risk, meet legal expectations, and maintain trust in an AI-driven world. Governance must be built for the incidents AI will create, not for the regulations that may eventually follow.