How AI and Big Data Transform Information Security Risk Management

Overview

The paper addresses information‑technology security risk, grounding its analysis in national standard GB/T20984 and international standard ISO27001, and explores how big‑data and computing power enable advanced risk assessment, prediction, and decision‑support methods, especially with emerging AI techniques.

What Is Risk?

Risk research began in economics; Frank H. Knight (1921) defined risk as measurable uncertainty. Modern information‑security standards (ISO Guide 73:2009) describe risk as the uncertain impact on objectives. The article cites Murphy’s Law and entropy concepts to illustrate that uncertainty and disorder are inherent in systems.

Quantitative Control and Management

James H. Harrington’s principle—"quantify to understand, control, and improve"—highlights the necessity of measurement. Management is framed as solving objective problems through quantification, iteration, and division of labor, with big data serving as the quantitative engine of the current AI era.

Development of Information‑Security Risk Management

International guidelines such as ISO/IEC TR 13335, NIST SP 800‑30, OCTAVE, and ISO/IEC 27005 have shaped risk‑assessment practices. In China, a timeline of standards (2001‑2018) culminated in the publication of GB/T20984, establishing a comprehensive risk‑management framework that extends to cloud computing, IoT, and big‑data environments.

Current Research Landscape

Today, nine national standards cover risk management, assessment, treatment, and implementation guides, including sector‑specific standards for ICT supply chains and industrial control systems.

Key Concepts and Definitions

Information Security (CIA) : confidentiality, integrity, availability (ISO/IEC 17799:2005). Risk : probability of an event and its consequences (ISO Guide 73:2002). Threat : potential cause of harm. Vulnerability : weakness exploitable by a threat.

Risk Assessment Process

Risk assessment comprises assessment, analysis, and evaluation. Subsequent steps include residual risk, risk treatment, acceptance, and overall risk management as defined by ISO/IEC Guide 73.

Risk Calculation Model

The risk model is expressed as R = F(A, T, V), where R is risk, A is asset, T is threat, and V is vulnerability.

Comprehensive Risk Analysis Methods

Risk SWOT analysis

Risk assessment records

Security risk assessment analysis

Security risk management (5W1H)

Security planning schemes

AI‑Driven Quantitative Risk Prediction

Bayesian theorem provides a framework for updating risk probabilities with new evidence. Despite its theoretical appeal, practical tools remain scarce. The article discusses the rise of generative AI (AIGC) and its potential for risk quantification.

Transformer and Attention Mechanisms

The 2017 "Attention is All You Need" paper introduced the Transformer, enabling parallel processing of sequences and spurring the development of large language models (GPT‑2, GPT‑3). These models, powered by massive compute, have advanced capabilities but still face limitations.

Future Benefits of Risk Research

Integrating AI with sufficient compute can enhance risk assessment, prediction, and decision support across finance, engineering, and infrastructure domains, offering both loss mitigation and revenue growth opportunities.

Conclusion and Outlook

Current Transformer‑based models demonstrate strong generative abilities, yet the next breakthrough may require "epiphany" beyond attention. The authors anticipate future research that leverages superhuman compute to achieve deeper knowledge emergence.