How frontier AI companies could implement an internal audit function
- URL: http://arxiv.org/abs/2512.14902v2
- Date: Thu, 18 Dec 2025 09:34:41 GMT
- Title: How frontier AI companies could implement an internal audit function
- Abstract summary: Internal audit could play a central role in strengthening safety governance.<n>This paper examines how an internal audit function could be designed to provide meaningful assurance for frontier AI developers.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Frontier AI developers operate at the intersection of rapid technical progress, extreme risk exposure, and growing regulatory scrutiny. While a range of external evaluations and safety frameworks have emerged, comparatively little attention has been paid to how internal organizational assurance should be structured to provide sustained, evidence-based oversight of catastrophic and systemic risks. This paper examines how an internal audit function could be designed to provide meaningful assurance for frontier AI developers, and the practical trade-offs that shape its effectiveness. Drawing on professional internal auditing standards, risk-based assurance theory, and emerging frontier-AI governance literature, we analyze four core design dimensions: (i) audit scope across model-level, system-level, and governance-level controls; (ii) sourcing arrangements (in-house, co-sourced, and outsourced); (iii) audit frequency and cadence; and (iv) access to sensitive information required for credible assurance. For each dimension, we define the relevant option space, assess benefits and limitations, and identify key organizational and security trade-offs. Our findings suggest that internal audit, if deliberately designed for the frontier AI context, can play a central role in strengthening safety governance, complementing external evaluations, and providing boards and regulators with higher-confidence, system-wide assurance over catastrophic risk controls.
Related papers
- Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels [53.63220028820581]
Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction.<n>We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably.
arXiv Detail & Related papers (2026-07-28T17:50:44Z) - Exploring Systems-Thinking Approaches to Loss of Control Risk [0.0]
Internal deployment of agentic AI systems for coding and research creates a sociotechnical control problem that extends beyond model behaviour.<n>We treat internal-deployment Loss of Control as the inability to reliably constrain, audit, reverse, or halt AI-mediated changes.<n>We ask whether established systems-safety methods can identify risks that model-level evaluations may miss.
arXiv Detail & Related papers (2026-06-11T15:27:31Z) - From Frontier to Shadow AI: A Simmering Threat to Assurance and Security in Critical Infrastructure [8.167474882691755]
We present the first empirical study of shadow AI in critical infrastructure environments.<n>We analyse how shadow AI manifests in practice, how it interacts with existing technical and governance controls, and the resulting security, assurance, and compliance risks.
arXiv Detail & Related papers (2026-05-23T07:21:08Z) - Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security [57.35851886874902]
Agentic AI systems execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness.<n>This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments.<n>For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies.
arXiv Detail & Related papers (2026-05-17T10:26:37Z) - Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique [0.0]
This study presents a decision-negative, human-in-the-loop agentic system that incorporates an adversarial self-critique mechanism.<n>Within this system, a critic agent challenges the primary agent's conclusions prior to submitting recommendations to human reviewers.<n>The research develops a formal taxonomy of failure modes to characterize potential errors by decision-negative agents.
arXiv Detail & Related papers (2026-01-21T05:51:27Z) - Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies [57.521647436515785]
We define frontier AI auditing as rigorous third-party verification of frontier AI developers' safety and security claims.<n>We introduce AI Assurance Levels (AAL-1 to AAL-4), ranging from time-bounded system audits to continuous, deception-resilient verification.
arXiv Detail & Related papers (2026-01-16T18:44:09Z) - Internal Deployment Gaps in AI Regulation [4.575084788651121]
High-stakes applications can occur internally when companies deploy highly capable systems within their own organizations.<n>This paper examines how frontier AI regulations in the United States and European Union in 2025 handle internal deployment.
arXiv Detail & Related papers (2026-01-12T21:18:11Z) - Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
arXiv Detail & Related papers (2025-11-10T22:24:21Z) - RADAR: A Risk-Aware Dynamic Multi-Agent Framework for LLM Safety Evaluation via Role-Specialized Collaboration [81.38705556267917]
Existing safety evaluation methods for large language models (LLMs) suffer from inherent limitations.<n>We introduce a theoretical framework that reconstructs the underlying risk concept space.<n>We propose RADAR, a multi-agent collaborative evaluation framework.
arXiv Detail & Related papers (2025-09-28T09:35:32Z) - Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
arXiv Detail & Related papers (2025-08-12T09:42:56Z) - Securing External Deeper-than-black-box GPAI Evaluations [49.1574468325115]
This paper examines the critical challenges and potential solutions for conducting secure and effective external evaluations of general-purpose AI (GPAI) models.<n>With the exponential growth in size, capability, reach and accompanying risk, ensuring accountability, safety, and public trust requires frameworks that go beyond traditional black-box methods.
arXiv Detail & Related papers (2025-03-10T16:13:45Z) - AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.<n>The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
arXiv Detail & Related papers (2025-02-19T05:58:52Z) - A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management [0.0]
The recent development of powerful AI systems has highlighted the need for robust risk management frameworks.<n>This paper presents a comprehensive risk management framework for the development of frontier AI.
arXiv Detail & Related papers (2025-02-10T16:47:00Z) - Frontier AI developers need an internal audit function [0.2913760942403036]
Article argues that frontier artificial intelligence (AI) developers need an internal audit function.
In light of rapid progress in AI research and development, frontier AI developers need to strengthen their risk governance.
arXiv Detail & Related papers (2023-05-26T15:48:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.