AI and Quantum Trading Agents: Cybersecurity, Governance, and Regulatory Challenges in Financial Decision-Making

Technical Summary: AI and Quantum Trading Agents: Cybersecurity, Governance, and Regulatory Challenges in Financial Decision-Making

Problem Statement
The financial sector is undergoing a dual transformation: the deployment of agentic AI (autonomous systems capable of reasoning, tool use, and multi-step orchestration) and the integration of quantum computing (specifically quantum machine learning and hybrid algorithms) into financial decision-making pipelines. While these technologies promise enhanced optimization, risk assessment, and trading speed, they create a critical governance vacuum. Existing frameworks treat AI, quantum technologies, and financial regulation as independent domains. This fragmentation leaves institutions without a unified approach to manage the systemic risks of AI and Quantum Trading Agents—autonomous systems that may rely on quantum-enhanced models, operate over post-quantum cryptographic infrastructure, and execute financial actions with limited human intervention. The core problem is the lack of integrated governance mechanisms to address the intersection of agent autonomy, quantum opacity, adversarial vulnerabilities, and prudential regulation.

Methodology
The paper employs a Systematic Mapping Review following the PRISMA 2020 guidelines to synthesize a fragmented, interdisciplinary literature.

• Data Sources: The review aggregated 198 records from academic databases (IEEE Xplore, ACM, arXiv, SSRN), regulatory repositories (NIST, U.S. Treasury, FINRA, SEC, Federal Reserve), and international standards bodies (FSB, IOSCO, BIS).
• Selection Process: After removing 80 duplicates, 118 unique records were screened. 22 were excluded for lacking multi-dimensional relevance (e.g., purely technical proofs without governance implications). The final corpus consists of 96 studies (39 government reports, 17 standards, 13 journal articles, 21 preprints, and 5 white papers).
• Inclusion Criteria: Records must address at least one core theme (quantum finance, agentic AI, cybersecurity, financial regulation) and contribute substantively to governance, security controls, or risk management.
• Quality Assessment: A dual-track approach was used:
  • Track A (Empirical): Assessed on methodological transparency, reproducibility, and relevance to financial governance.
  • Track B (Regulatory/Standards): Assessed on institutional authority, sector specificity, and enforceability.
• Analysis: Thematic classification was applied across 12 domains (e.g., Quantum Finance, Agentic AI, Post-Quantum Security, AI Auditability) using multi-label coding to capture cross-cutting relationships.

Key Contributions

1. Integrative Conceptualization: The paper formally defines "AI and Quantum Trading Agents" as a distinct governance object. This definition extends beyond "algorithmic trading" to include delegation, adaptability, and multi-step agency, and beyond "quantum finance" to center the agent as the unit of governance rather than just the algorithm.
2. Systematic Evidence Map: It provides the first systematic mapping of the intersection between quantum finance, agentic AI, cybersecurity, and financial regulation, revealing that while individual domains are well-studied, their convergence is largely unaddressed.
3. Gap Identification: The review identifies five critical gaps:
   • Unified Object Definition: No Tier-1 regulator currently defines quantum-enabled financial agents as a distinct supervised entity class.
   • Regulatory Scope Mismatch: Banking model-risk guidance (e.g., SR 26-2) often narrows scope away from generative/agentic AI, creating supervisory gray zones.
   • Security and Auditability: Post-quantum migration playbooks focus on cryptographic algorithms but lack protocols for agent identity, delegation chains, and tool-use authorization.
   • Human Oversight: "Human-in-the-loop" requirements are rarely operationalized for low-latency agentic trading.
   • Cross-Domain Validation: Hybrid quantum-classical agent prototypes lack standardized red-teaming or governance validation protocols.
4. Research Agenda: It proposes a phased agenda:
   • Near-term (2026–2027): Mapping Treasury/NIST AI RMF controls to agentic architectures.
   • Medium-term (2027–2029): Piloting post-quantum signed decision logs and adversarial evaluation suites.
   • Long-term (2029–2032): Harmonizing international guidance (FSB/IOSCO) with national PQC migration milestones.

Results

• Publication Trends: The field is rapidly accelerating; 75.0% of the included studies were published in 2024 or later, driven by regulatory responses to agentic AI and post-quantum standardization.
• Thematic Distribution: The corpus is dominated by Financial Regulation (35 sources), Risk Management (31), and Cybersecurity Governance (14). In contrast, Quantum Finance (10) and Autonomous Financial Agents (3) are comparatively sparse, indicating a lag in empirical research on the specific convergence of these technologies.
• Source Composition: The evidence base is policy-heavy, with government reports and standards constituting the majority of sources, reflecting the field's current state as a normative and regulatory challenge rather than a purely technical one.
• Critical Intersections: The research gap matrix (Figure 10) highlights that intersections between Agentic AI, Post-Quantum Security, and Auditability are critically under-researched (fewer than three co-occurring studies).

Significance and Claims
The paper claims that the emergence of AI and Quantum Trading Agents creates a new class of socio-technical systems that cannot be governed by existing, isolated frameworks. Its primary significance lies in shifting the focus from evaluating individual algorithms or trading strategies to establishing governance as the central challenge for future autonomous financial systems.

The authors argue that without a coordinated approach spanning artificial intelligence, cybersecurity, quantum computing, and financial regulation, institutions risk "governance failure by fragmentation," where undetected agent misbehavior, unexamined quantum-model risk, and cryptographic transition gaps compound rather than offset one another. The review serves as a foundational step to make the combined risk surface visible, auditable, and actionable for regulators, financial institutions, and researchers, emphasizing that trust in next-generation financial decision-making requires multidisciplinary collaboration rather than siloed technical solutions.