Mapping the AI Threat Landscape

by

Author: Denis Avetisyan

A new framework standardizes the identification and quantification of risks facing artificial intelligence systems, connecting technical vulnerabilities to business impact.

This review proposes a standardized AI threat taxonomy and quantification framework for improved governance, regulatory compliance, and risk assessment.

Despite the rapid integration of artificial intelligence across critical sectors, a disconnect persists between technical vulnerability assessments and quantifiable business risk. This research addresses this gap with the ‘Standardized Threat Taxonomy for AI Security, Governance, and Regulatory Compliance’, introducing a structured framework for translating AI-specific threats into measurable financial impacts. The resulting taxonomy categorizes 53 operationally-defined sub-threats across nine domains, linking technical vectors to business loss categories to facilitate robust Quantitative Risk Assessment. Will this standardized approach finally enable organizations to accurately assess, manage, and insure against the evolving landscape of AI-related risks?

The Algorithmic Imperative: Understanding AI Threat Vectors

Artificial intelligence systems, despite their increasing capabilities, inherit vulnerabilities that stretch far beyond the scope of conventional cybersecurity. These threats aren’t simply about hacking into a system; they encompass data poisoning, where malicious inputs corrupt the training data and skew the AI’s outputs, and adversarial attacks, which involve crafting subtle, often imperceptible, alterations to inputs that cause the AI to misclassify or malfunction. Furthermore, risks arise from model stealing – the unauthorized copying of a trained AI model – and backdoor attacks, embedding hidden triggers within the AI that can be activated later for nefarious purposes. Unlike traditional software, AI’s reliance on vast datasets and complex algorithms introduces entirely new attack surfaces, demanding a proactive and multifaceted approach to security that acknowledges these unique challenges.

Conventional threat models, designed for traditional software and networks, frequently fall short when applied to artificial intelligence systems. These models typically prioritize vulnerabilities in code and infrastructure, overlooking the distinct risks inherent in AI’s operational core. AI’s dependence on massive datasets introduces vulnerabilities like data poisoning and adversarial examples, where subtly manipulated inputs can cause misclassification or incorrect outputs. Furthermore, the complexity of AI algorithms-often “black boxes” even to their creators-makes it difficult to anticipate how a system will react to unforeseen circumstances or malicious attacks. The intricate interactions between AI components and external systems also create new attack surfaces not accounted for in standard security assessments, necessitating a re-evaluation of how risk is identified and mitigated in the age of intelligent machines.

Effective management of artificial intelligence risks necessitates a systematic approach to identifying and prioritizing potential threats. Recent work has established a structured taxonomy-the AI System Threat Vector Taxonomy-designed to categorize these vulnerabilities in a comprehensive manner. This framework moves beyond traditional cybersecurity concerns to address risks inherent in AI’s reliance on data integrity, algorithmic biases, and the complexities of system interactions. Validation of this taxonomy, through detailed analysis of 133 documented AI incidents, demonstrates its practical utility in pinpointing specific vulnerabilities and assessing their potential impact. Consequently, organizations can leverage this structured approach to proactively mitigate risks and build more resilient AI systems, ensuring responsible and secure deployment.

Organizations adopting artificial intelligence face substantial risk if vulnerability assessments remain fragmented and incomplete. A piecemeal approach to AI security frequently overlooks subtle yet critical weaknesses arising from the interplay between data quality, algorithmic bias, and system integration. Consequently, seemingly robust AI deployments can be susceptible to manipulation, leading to inaccurate predictions, compromised decision-making, and potentially severe repercussions ranging from financial losses and reputational damage to safety hazards and legal liabilities. Ignoring these interconnected vulnerabilities creates blind spots that adversaries – or even unintentional errors – can exploit, underscoring the necessity of a comprehensive and proactive security posture for all AI systems.

Quantifying the Improbable: A Rigorous Approach to AI Risk Assessment

Traditional risk assessment methodologies, designed for conventional systems, frequently fail to adequately address the unique characteristics of AI vulnerabilities. These methods typically focus on known threat actors and predictable failure modes, whereas AI systems present risks stemming from emergent behaviors, data dependencies, and susceptibility to adversarial attacks. The probabilistic and data-driven nature of AI introduces complexities not easily captured by static vulnerability assessments or checklists. Specifically, the potential for model drift, unintended consequences from complex algorithms, and the opacity of ‘black box’ models necessitate a shift towards dynamic, quantitative approaches capable of modeling these nuanced and often unpredictable risks. Existing frameworks often lack the granularity to assess the specific impacts of AI failures, particularly regarding reputational damage, legal liabilities, or systemic failures caused by cascading errors.

Quantitative Risk Assessment (QRA) for AI systems moves beyond qualitative descriptions of risk by assigning numerical values to both the likelihood and impact of potential threats. This process allows for the calculation of an expected loss, expressed as $Expected\,Loss = Likelihood \times Impact$, enabling prioritization of mitigation efforts based on quantifiable metrics. Unlike traditional assessments which often rely on subjective scales (e.g., high, medium, low), QRA demands the definition of specific, measurable criteria for assessing both the probability of a threat occurring and the resulting damage. This approach facilitates a more objective and consistent evaluation of AI risk, enabling organizations to compare risks across different AI systems and allocate resources effectively. The resulting quantitative values also support cost-benefit analysis of various security controls and risk reduction strategies.

Quantitative Risk Assessment (QRA) benefits from the application of established risk modeling techniques to improve both accuracy and reliability. Factor Analysis of Information Risk (FAIR) provides a structured, probabilistic approach to modeling risk scenarios, focusing on observable and quantifiable factors such as threat frequency and vulnerability. Convolved Monte Carlo simulation further refines QRA by enabling the propagation of uncertainty through complex models; this method allows for the calculation of probability distributions for potential loss magnitudes, accounting for the interdependencies between different risk factors. By integrating these techniques, QRA moves beyond qualitative assessments to provide a data-driven, numerical understanding of AI-related risks, facilitating more informed decision-making and resource allocation.

Analysis of 133 documented AI incidents reveals that the proposed risk taxonomy achieves complete coverage of observed events. The predominant threat category identified was Misuse, accounting for 61% of incidents, indicating intentional malicious or unauthorized application of AI systems. A further 27% of incidents were attributed to Unreliable Outputs, stemming from errors, biases, or unexpected behavior in AI-generated results. These two categories collectively represent 88% of the analyzed incidents, demonstrating their significant contribution to the overall AI risk landscape and highlighting areas requiring focused mitigation strategies.

Establishing Order: Governing AI Risk Through Standards and Regulations

ISO/IEC 42001 and the NIST AI Risk Management Framework (AI RMF) are both voluntary consensus standards designed to aid organizations in implementing effective AI governance. ISO/IEC 42001 provides a management system approach, specifying requirements for establishing, implementing, maintaining, and continually improving an AI management system. The NIST AI RMF, conversely, offers a more flexible and customizable framework consisting of four functions – Govern, Map, Measure, and Manage – which organizations can tailor to their specific needs and risk profiles. Both standards address the need for organizations to systematically identify, assess, and manage risks associated with AI systems, covering areas such as bias, fairness, transparency, and accountability. While differing in structure, both frameworks aim to promote trustworthy and responsible AI by providing a common language and set of practices for governing AI technologies.

The European Union’s AI Act introduces a tiered regulatory approach to artificial intelligence, with the most stringent requirements applied to “high-risk” AI systems. These systems, defined by their potential to cause significant harm to health, safety, or fundamental rights, are subject to mandatory conformity assessments before deployment. Crucially, the Act mandates comprehensive and documented risk management processes, including data governance, technical documentation, transparency requirements, human oversight mechanisms, and robust cybersecurity measures. Non-compliance can result in substantial fines-up to €30 million or 6% of global annual turnover, whichever is higher-and prohibitions on the deployment of non-compliant AI systems within the EU market. The Act’s focus on pre-market conformity assessments and ongoing monitoring represents a departure from largely voluntary frameworks and aims to establish a legally enforceable standard for responsible AI development and deployment.

AI risk management frameworks consistently prioritize a lifecycle approach, demanding continuous risk identification, assessment, and mitigation from initial design and data sourcing through development, testing, deployment, and ongoing monitoring. This includes evaluating potential harms related to accuracy, robustness, fairness, and security at each stage. Frameworks require documentation of risk assessments, implemented mitigation strategies, and performance monitoring to demonstrate ongoing risk control. The scope extends beyond technical performance to encompass societal and ethical considerations, necessitating interdisciplinary input and iterative refinement of risk management processes as the AI system evolves and interacts with its intended environment.

Compliance with established AI standards and regulations, such as ISO/IEC 42001, the NIST AI RMF, and the EU AI Act, signals an organization’s dedication to addressing potential harms associated with artificial intelligence. This commitment extends beyond legal requirements, demonstrating a proactive approach to risk management throughout the AI system lifecycle – from design and development to deployment and monitoring. Documented adherence to these frameworks provides stakeholders – including users, customers, and regulatory bodies – with increased confidence in the system’s safety, reliability, and ethical considerations. This, in turn, fosters trust and accountability, essential components for the widespread and sustainable adoption of AI technologies.

Beyond Current Horizons: Addressing Emerging AI Threat Landscapes

Existing cybersecurity frameworks, while valuable, are increasingly challenged by the sophistication of modern attacks, particularly those targeting the software supply chain and exploiting data integrity issues. These attacks move beyond traditional perimeter defenses, compromising systems through vulnerabilities embedded in third-party components or manipulating the data upon which critical decisions are based. A reactive posture is no longer sufficient; frameworks must evolve to incorporate proactive supply chain risk assessments, continuous verification of software dependencies, and robust data quality controls. This requires a shift toward zero-trust principles, emphasizing validation at every stage and minimizing implicit trust, alongside investment in technologies that can automate the detection of compromised components and anomalous data patterns. Ignoring these emerging threats risks cascading failures and substantial damage to organizations and their customers.

The convergence of artificial intelligence with established technologies-like cloud computing, the Internet of Things, and 5G networks-is creating complex, interconnected systems that present novel attack surfaces. This interplay isn’t simply adding AI as a layer of security or risk; rather, it’s generating entirely new classes of vulnerabilities. For example, compromised data streams feeding AI models can lead to biased outputs and flawed decision-making, while vulnerabilities within the AI algorithms themselves can be exploited to bypass traditional security measures. Consequently, continuous monitoring and in-depth analysis are crucial-not just of network traffic and system logs, but also of the AI models’ behavior, data provenance, and output patterns. A static, point-in-time assessment is insufficient; instead, a dynamic, adaptive approach is required to detect and mitigate risks as these interconnected systems evolve and interact.

Current cybersecurity threat taxonomies, while valuable, are proving insufficient to capture the unique vulnerabilities introduced by artificial intelligence, particularly large language models. Initiatives like the OWASP Top 10 for LLMs represent an initial step, but they often address symptoms rather than root causes, and struggle to keep pace with the rapidly evolving threat landscape. Existing frameworks frequently focus on traditional attack vectors, overlooking AI-specific risks such as data poisoning, model evasion, and the exploitation of emergent behaviors. A truly comprehensive understanding requires moving beyond simply adapting existing categories and instead developing new taxonomies that account for the probabilistic nature of AI, the complexities of model training, and the potential for subtle, yet impactful, manipulations of AI systems. This necessitates ongoing research into AI safety and security, coupled with a collaborative effort to define, categorize, and ultimately mitigate these novel threats.

Successfully navigating the future of artificial intelligence necessitates a shift from reactive security measures to a proactive and adaptive risk management framework. This approach recognizes that AI’s rapidly evolving capabilities continuously introduce new vulnerabilities and attack vectors, demanding constant vigilance and refinement of security protocols. Rather than simply responding to incidents, organizations must anticipate potential threats through continuous monitoring, threat modeling, and the implementation of resilient systems designed to withstand sophisticated attacks. Crucially, this includes establishing robust data governance practices, ensuring the integrity of training data, and developing mechanisms for ongoing model evaluation and refinement – all essential steps for mitigating the long-term consequences of AI-related risks and fostering responsible innovation.

The pursuit of a standardized threat taxonomy, as detailed in the article, echoes a fundamental principle of computational rigor. Robert Tarjan once stated, “The most effective algorithms are often the simplest, and the simplest algorithms are often the most elegant.” This sentiment applies directly to the framework proposed; by distilling complex AI vulnerabilities into quantifiable risks-bridging the gap between technical flaws and financial impact-the work strives for algorithmic elegance in risk management. The article’s focus on moving beyond merely ‘working’ solutions toward provable resilience demonstrates a commitment to the mathematical purity that underpins truly robust systems, particularly crucial in addressing concerns like model drift and prompt injection.

What Lies Ahead?

The presented taxonomy, while a necessary step toward quantifiable AI risk, merely establishes a framework – a skeleton upon which the musculature of predictive accuracy must be built. The current reliance on observed vulnerabilities – prompt injection, model drift – feels akin to cataloging symptoms while neglecting the underlying pathology. A truly robust system demands a shift from reactive identification to a priori prediction of emergent threats, grounded in the mathematical properties of the models themselves.

The challenge is not simply to enumerate risks, but to formally define the boundaries of model behavior. Current quantification efforts, while pragmatic, often rely on empirical observation – a precarious foundation. The pursuit of provable safety properties – guarantees against adversarial manipulation or unintended consequences – represents the logical, albeit difficult, next step. Until AI risk assessment moves beyond correlation and embraces causation, it remains, fundamentally, an exercise in informed guesswork.

Future work must address the inherent limitations of categorizing complex systems. The very act of defining a threat taxonomy introduces a bias, obscuring threats that fall outside the predefined boundaries. Perhaps the ultimate goal is not a comprehensive taxonomy, but a minimal, mathematically elegant framework capable of detecting any deviation from specified, verifiable behavior – a system that prioritizes correctness over completeness, and predictability above all else.

Original article: https://arxiv.org/pdf/2511.21901.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-01 12:01