AI Risk Is Outrunning Governance—and Enterprises Are Struggling to Keep Pace

AI is transforming enterprise operations—but not without consequence. According to BigID’s 2025 “AI Risk & Readiness in the Enterprise” report, the adoption of AI is accelerating faster than organizations’ ability to govern it. The data paints a stark picture: while companies are eager to integrate AI, they are largely unprepared to manage its risks.

Nearly every organization surveyed—93%—lacks full confidence in securing AI-driven data. And despite growing concerns about AI-powered data leaks, 47.2% of enterprises still haven’t implemented any AI-specific security controls. This disconnect is especially troubling given that only 6.4% of respondents say they’ve deployed advanced AI security strategies.

AI Exposure, No Safety Net

The top threats in 2025 aren’t theoretical—they’re already here. AI-powered data leaks top the list, with nearly 70% of organizations flagging it as their biggest security concern. Unstructured data exposure and the rise of Shadow AI—unauthorized or unmonitored AI tools—follow closely behind.

Yet many organizations have no effective mechanisms for monitoring how AI systems interact with sensitive data. Without visibility into model behavior, enterprises are exposed to risks like regulatory violations, unauthorized access, and data misuse.

Compliance Lagging Behind

AI-specific regulations are ramping up globally, from the EU’s AI Act to executive directives in the U.S. Despite this, over 80% of organizations remain unprepared or unclear on how to comply. A quarter of those surveyed admit they’ve done nothing to get ready.

Noncompliance is no longer just a technical problem—it’s a legal and reputational liability. Organizations that don’t build auditability and transparency into their AI pipelines risk serious fallout.

No One Steering the Ship

One of the most significant challenges is leadership fragmentation. In 21.9% of cases, no team or individual owns AI security, governance, or compliance. Elsewhere, responsibility is scattered across IT security, data governance, privacy, and compliance teams—creating silos and stalling progress.

This lack of coordination is particularly dangerous in highly regulated industries like finance and healthcare. In financial services, for instance, only 38% of firms report having AI-specific data protections in place—even as they push forward with AI adoption.

A Trust Deficit

The report also highlights growing concerns about trust and transparency in AI systems. More than half of organizations suspect their AI models may be using unauthorized or sensitive data. Confidence in existing controls is low, with only 12.4% describing their oversight as strong and well-enforced.

AI TRiSM—Trust, Risk, and Security Management—remains an underdeveloped practice. Yet it’s essential for ensuring AI systems behave ethically, operate within defined boundaries, and remain accountable across their lifecycle.

Strategy vs. Execution

Organizations claim that governance and compliance are top priorities. But when it comes to action, few are implementing the controls needed to enforce policies at scale. Less than 15% are focused on preventing AI-driven data leaks or applying robust security controls to AI workflows.

Closing this gap will require a shift from governance-on-paper to governance-in-practice. Real-time monitoring, automated policy enforcement, and unified tooling across teams are necessary steps.

The Road Ahead

The message is clear: AI’s risks aren’t waiting for enterprises to catch up. Companies must rapidly adopt tools and frameworks that bring visibility, control, and compliance to their AI programs. That means inventorying model behavior, enforcing least-privilege access, classifying AI-accessible data, and building oversight into every step of the AI pipeline.

As AI continues to scale, so do the stakes. Without proactive security and governance, enterprises may find themselves vulnerable not just to technical failure—but to the collapse of trust itself.