Security Barriers to Trustworthy AI-Driven Cyber Threat Intelligence in Finance: Evidence from Practitioners

• URL: http://arxiv.org/abs/2603.23304v1
• Date: Tue, 24 Mar 2026 15:10:58 GMT
• Title: Security Barriers to Trustworthy AI-Driven Cyber Threat Intelligence in Finance: Evidence from Practitioners
• Abstract summary: Financial institutions face increasing cyber risk while operating under strict regulatory oversight.<n>To manage this risk, they rely heavily on Cyber Threat Intelligence (CTI) to inform detection, response, and strategic security decisions.<n>However, evidence of trustworthy production use in finance remains limited.
• Score: 0.15293427903448023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial institutions face increasing cyber risk while operating under strict regulatory oversight. To manage this risk, they rely heavily on Cyber Threat Intelligence (CTI) to inform detection, response, and strategic security decisions. Artificial intelligence (AI) is widely suggested as a means to strengthen CTI. However, evidence of trustworthy production use in finance remains limited. Adoption depends not only on predictive performance, but also on governance, integration into security workflows and analyst trust. Thus, we examine how AI is used for CTI in practice within financial institutions and what barriers prevent trustworthy deployment. We report a mixed-methods, user-centric study combining a CTI-finance-focused systematic literature review, semi-structured interviews, and an exploratory survey. Our review screened 330 publications (2019-2025) and retained 12 finance-relevant studies for analysis; we further conducted six interviews and collected 14 survey responses from banks and consultancies. Across research and practice, we identify four recurrent socio-technical failure modes that hinder trustworthy AI-driven CTI: (i) shadow use of public AI tools outside institutional controls, (ii) license-first enablement without operational integration, (iii) attacker-perception gaps that limit adversarial threat modeling, and (iv) missing security for the AI models themselves, including limited monitoring, robustness evaluation and audit-ready evidence. Survey results provide additional insights: 71.4% of respondents expect AI to become central within five years, 57.1% report infrequent current use due to interpretability and assurance concerns and 28.6% report direct encounters with adversarial risks. Based on these findings, we derive three security-oriented operational safeguards for AI-enabled CTI deployments.

Related papers

• 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)
• 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)
• Standardized Threat Taxonomy for AI Security, Governance, and Regulatory Compliance [0.0]
  "Language barrier" currently separates technical security teams, who focus on algorithmic vulnerabilities, from legal and compliance professionals, who address regulatory mandates.<n>This research presents the AI System Threat Vector taxonomy, a structured ontology designed explicitly for Quantitative Risk Assessment (QRA)<n>The framework categorizes AI-specific risks into nine critical domains: Misuse, Poisoning, Privacy, Adrial, Biases, Unreliable Outputs, Drift, Supply Chain, and IP Threat, integrating 53 operationally defined sub-threats.
  arXiv  Detail & Related papers  (2025-11-26T20:42:46Z)
• Are Companies Taking AI Risks Seriously? A Systematic Analysis of Companies' AI Risk Disclosures in SEC 10-K forms [9.295655764985318]
  Regulators are pushing for greater transparency in how companies identify, report and mitigate AI-related risks.<n>In the US, the Securities and Exchange Commission repeatedly warned companies to provide their investors with more accurate disclosures of AI-related risks.<n>In the EU, new laws - like the AI Act and Digital Services Act - introduced additional rules on AI risk reporting and mitigation.<n>This study presents the first large-scale systematic analysis of AI risk disclosures in SEC 10-K filings.
  arXiv  Detail & Related papers  (2025-08-26T10:19:29Z)
• CIA+TA Risk Assessment for AI Reasoning Vulnerabilities [0.0]
  We present a framework for cognitive cybersecurity, a systematic protection of AI reasoning processes from adversarial manipulation.<n>First, we establish cognitive cybersecurity as a discipline complementing traditional cybersecurity and AI safety.<n>Second, we introduce the CIA+TA, extending traditional Confidentiality, Integrity, and Availability with Trust.<n>Third, we present a quantitative risk assessment methodology with empirically-derived coefficients, enabling organizations to measure cognitive security risks.
  arXiv  Detail & Related papers  (2025-08-19T13:56:09Z)
• 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)
• Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition [101.86739402748995]
  We run the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios.<n>We build the Agent Red Teaming benchmark and evaluate it across 19 state-of-the-art models.<n>Our findings highlight critical and persistent vulnerabilities in today's AI agents.
  arXiv  Detail & Related papers  (2025-07-28T05:13:04Z)
• Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs [0.6990493129893112]
  Explainable AI (XAI) holds significant promise for enhancing the transparency and trustworthiness of AI-driven threat detection.<n>This study re-evaluates current explanation methods within security contexts and demonstrates that role-aware, context-rich XAI designs aligned with SOC can substantially improve practical utility.
  arXiv  Detail & Related papers  (2025-03-03T21:39:15Z)
• Position: Mind the Gap-the Growing Disconnect Between Established Vulnerability Disclosure and AI Security [56.219994752894294]
  We argue that adapting existing processes for AI security reporting is doomed to fail due to fundamental shortcomings for the distinctive characteristics of AI systems.<n>Based on our proposal to address these shortcomings, we discuss an approach to AI security reporting and how the new AI paradigm, AI agents, will further reinforce the need for specialized AI security incident reporting advancements.
  arXiv  Detail & Related papers  (2024-12-19T13:50:26Z)
• A Safe Harbor for AI Evaluation and Red Teaming [124.89885800509505]
  Some researchers fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. We propose that major AI developers commit to providing a legal and technical safe harbor. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
  arXiv  Detail & Related papers  (2024-03-07T20:55:08Z)

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.