#USA #New York #AI Security #BigID #Enterprise Risks

Major Gap Between AI Adoption and Security Readiness

A recent report from BigID, a leader in data security and AI management, unveils concerning findings regarding the relationship between enterprise AI adoption and their preparedness for associated security risks. The report, titled AI Risk & Readiness in the Enterprise 2025, indicates that a staggering 64% of organizations lack comprehensive visibility into AI risks, heightening their vulnerability to potential security failures and compliance issues.

Alarming Findings

The study encompassed leaders in security, compliance, and data across various industries, revealing critical gaps in AI security strategies. Notably, only 6% of organizations reported having advanced AI security frameworks, exposing significant oversight in the corporate landscape as AI technologies rapidly gain traction. Key findings from the report include:

• - Widespread Vulnerability: Nearly 69% of organizations point to AI-driven data leaks as their top concern, yet 47% admit to having no AI-specific security measures implemented.
• - Regulatory Preparedness: A concerning 55% of organizations feel unprepared to meet emerging AI regulatory requirements, putting them at risk of severe penalties and damage to their reputations.
• - Data Protection Woes: Approximately 40% of respondents revealed a lack of appropriate tools to safeguard AI-accessible data, signifying a dangerous gap between the advancement of AI and the necessary protective measures.

The increase in Shadow AI, which refers to unauthorized AI tools utilized within organizations, has further exacerbated these issues, leading to greater exposure to data misuse and regulatory infringements.

Industry-Specific Challenges

Different industries are facing unique challenges regarding AI risk management. For example:

• - In the finance sector, only 38% of firms have adopted AI-specific data protection strategies, despite handling sensitive data.
• - Healthcare organizations face considerable challenges with over half struggling to comply with AI regulations.
• - Retailers lack oversight, with 48% admitting insufficient visibility into how AI models process customer data.
• - Even technology companies, often associated with AI innovation, find themselves poorly prepared, with 42% lacking an AI risk management strategy.

Urgent Recommendations for Improvement

To enhance their AI risk posture, organizations should take immediate steps to bolster governance frameworks, including:

• - Deploying proactive AI risk monitoring and response mechanisms to swiftly address vulnerabilities.
• - Establishing awareness strategies around AI-related data governance to bridge the visibility gap.
• - Implementing strict access controls to manage the use of Shadow AI and prevent unauthorized interactions with sensitive data.
• - Aligning AI security strategies with evolving regulations through a comprehensive AI Trust, Risk, and Security Management (TRiSM) framework.

Dimitri Sirota, CEO of BigID, emphasizes the urgency of addressing this critical security lapse, stating, "Organizations must rethink their approach to data in the age of AI. Implementing robust AI governance isn't just about compliance; it's about safeguarding essential assets and harnessing the power of innovation."

Methodology of the Study

The report is based on feedback from security, compliance, and data professionals across a diverse range of industries. Representation includes technology (34%), financial services (21%), government (8%), healthcare (5%), retail (5%), and others (27%). The surveyed group encompassed a variety of company sizes, including small and mid-sized enterprises (54%), mid-market companies (26%), and large enterprises (20%) across multiple regions including North America, Europe, Asia-Pacific, the Middle East, Africa, and Latin America.

With AI adoption expected to accelerate, addressing the security risks associated with this technology should be paramount for organizations seeking to innovate safely and effectively.