Quantifying Security Vulnerabilities: A Metric-Driven Security Analysis of Gaps in Current AI Standards

TL;DR

This paper develops a metric-driven approach to assess security gaps in AI standards, revealing significant vulnerabilities and providing targeted recommendations to improve AI security compliance.

Contribution

It introduces four novel metrics for quantifying security risks in AI standards and applies them to identify critical gaps and weaknesses in existing frameworks.

Findings

NIST fails to address 69.23% of risks

ALTAI has the highest attack vector vulnerability (AVPI=0.51)

ICO Toolkit has 80% unresolved high-risk concerns

Abstract

As AI systems integrate into critical infrastructure, security gaps in AI compliance frameworks demand urgent attention. This paper audits and quantifies security risks in three major AI governance standards: NIST AI RMF 1.0, UK's AI and Data Protection Risk Toolkit, and the EU's ALTAI. Using a novel risk assessment methodology, we develop four key metrics: Risk Severity Index (RSI), Attack Potential Index (AVPI), Compliance-Security Gap Percentage (CSGP), and Root Cause Vulnerability Score (RCVS). Our analysis identifies 136 concerns across the frameworks, exposing significant gaps. NIST fails to address 69.23 percent of identified risks, ALTAI has the highest attack vector vulnerability (AVPI = 0.51) and the ICO Toolkit has the largest compliance-security gap, with 80.00 percent of high-risk concerns remaining unresolved. Root cause analysis highlights under-defined processes (ALTAI RCVS = 033) and weak implementation guidance (NIST and ICO RCVS = 0.25) as critical weaknesses. These findings emphasize the need for stronger, enforceable security controls in AI compliance. We offer targeted recommendations to enhance security posture and bridge the gap between compliance and real-world AI risks.