The integration of generative artificial intelligence into financial cyber threat intelligence systems has unlocked unprecedented capabilities for the rapid synthesis and analysis of emerging attack vectors. These technologies enable faster aggregation, correlation, and dissemination of threat data, enhancing the ability of financial institutions to respond to evolving cyber risks. As reliance on automated intelligence systems grows, generative AI is becoming a central component in modern financial cybersecurity operations.
However, the deployment of generative AI also introduces significant risks, including the generation of hallucinated intelligence, adversarial synthesis, and cross-border data exposure. These risks are compounded by the increasing dependence on automated systems to produce and distribute threat intelligence, where inaccuracies or manipulated outputs can propagate rapidly across interconnected networks. Organizations must therefore proactively identify and monitor emerging vulnerabilities such as adversarial inputs, prompt injection, and AI jailbreaking techniques.1 In the absence of comprehensive regulatory oversight, these challenges create exploitable gaps that adversaries can leverage.
Despite the maturity of existing centralized information-sharing hubs, they currently lack cohesive mechanisms to effectively govern the integration and oversight of generative AI outputs. This limitation underscores the need for a structured regulatory approach that can address both technological and operational risks. Consequently, there is an urgent need for a robust regulatory framework that harmonizes generative AI integration with established cybersecurity protocols, ensuring the integrity and resilience of global financial security ecosystems.
Existing AI governance policies, financial threat intelligence frameworks, and regulatory precedents provide a strong foundational structure for managing cybersecurity and AI-related risks. However, these frameworks remain insufficient in addressing the distinct challenges introduced by generative AI integration, particularly in areas such as automated threat synthesis, provenance validation, and cross-border data governance. While each framework contributes valuable guidance within its domain, their combined application does not fully resolve the complexities associated with generative AI–driven intelligence workflows.
From a regulatory perspective, the EU AI Act establishes a critical baseline through its risk-tiering approach, categorizing AI systems based on their potential impact on fundamental rights and safety.2 Complementing this, the US NIST AI Risk Management Framework provides operational guidance for risk identification, assessment, and auditing throughout the AI lifecycle.3 In the financial sector, the Singapore MAS FEAT Principles offer a domain-specific benchmark, emphasizing fairness, ethics, accountability, and transparency in AI deployment.4 Together, these frameworks define essential legal and ethical expectations but do not explicitly address the dynamic and generative nature of AI-driven threat intelligence.
On the technical side, established threat intelligence standards such as STIX/TAXII and the FS-ISAC intelligence exchange frameworks serve as primary mechanisms for structured data sharing and inter-organizational collaboration.5,6 However, these protocols were not designed to accommodate generative AI outputs, particularly those involving synthetic data generation and automated intelligence production. As a result, significant gaps persist in securely integrating generative AI into existing ecosystems, especially in relation to cross-border data governance, validation of machine-generated intelligence, and the prevention of propagation of unreliable or manipulated outputs.
To summarize the roles and limitations of these key frameworks in the context of generative AI integration, Table 1 provides a comparative overview.
Table 1. Summary of key frameworks and limitations for generative AI integration
The theoretical basis for a generative AI regulatory framework is grounded in the principles of transparency, accountability, and the mitigation of dual-use risks. These principles are essential to ensure that AI-generated threat intelligence remains trustworthy, auditable, and aligned with established cybersecurity practices. In particular, the increasing use of generative systems in financial intelligence workflows necessitates mechanisms that can clearly distinguish between human-verified and machine-generated outputs.
A central requirement of this foundation is robust provenance tracking. Addressing synthetic data attribution and mitigating dual-use risks inherently depends on the ability to trace the origin and transformation of generated artifacts, including code and network telemetry.7 Provenance tracking must be consistently applied across all generative modalities, rather than being limited to Large Language Models (LLMs). While LLMs used for threat report synthesis introduce risks such as hallucinated intelligence, other modalities—such as synthetic telemetry generation (e.g., synthetic Indicators of Compromise) and adversarial code analysis—pose equally significant operational risks.1,7
Ethical auditing and synthetic data attribution therefore form the core components of this conceptual framework. These mechanisms ensure that generative AI outputs can be systematically evaluated, validated, and contextualized within existing intelligence pipelines. By embedding accountability into both the generation and dissemination processes, the framework enables organizations to reliably differentiate AI-generated intelligence from human-verified threat data, thereby preserving the integrity and usability of shared cybersecurity information.
This section presents a structured regulatory framework designed to govern the development, deployment, and integration of generative AI within financial threat intelligence systems.
The Governance Layer establishes policy rules governing model training, auditing, and compliance. The NIST AI Risk Management Framework provides the operational auditing standards required for this layer, ensuring that AI models are rigorously evaluated before deployment.3 Furthermore, specialized security control overlays can be utilized to manage AI-specific risks such as data poisoning, prompt injection, and model extraction, tailoring established cybersecurity guidelines to the unique vulnerabilities of generative AI.12
The Operational Layer mandates real-time monitoring, access controls, and explainability requirements. It ensures that generative AI tools deployed within financial networks are continuously scrutinized for behavioral drift and adversarial manipulation. By implementing strict access controls, financial institutions can restrict model querying capabilities to authorized personnel, maintaining a secure operational environment.
The Collaborative Layer defines data-sharing protocols, anonymization standards, and inter-agency coordination mechanisms. This layer leverages established technical protocols like STIX/TAXII and the FS-ISAC intelligence exchange frameworks to facilitate secure information sharing.5,6 It guarantees that generative AI outputs can be disseminated across organizations without violating privacy regulations or exposing sensitive proprietary data.
Developing a comprehensive taxonomy of risks associated with generative AI in financial threat intelligence contexts requires a systematic evaluation of potential threat vectors and their operational impact. The EU AI Act’s legal risk-tiering framework provides a foundational basis for this taxonomy by classifying AI systems according to their potential to cause harm to systems, organizations, and broader financial ecosystems.2 This structured approach enables the identification and prioritization of risks within generative AI–driven intelligence workflows.
The proposed risk taxonomy must account for all generative modalities rather than focusing solely on text-based systems. While large language models introduce risks such as hallucinated intelligence, other modalities—particularly synthetic telemetry generation—pose significant dual-use and operational threats. These include the potential for threat feed poisoning, where artificially generated Indicators of Compromise (IoCs) may be introduced into intelligence pipelines, undermining their reliability.7 A comprehensive taxonomy must therefore capture both content-level and system-level risks across diverse generative outputs.
To address these risks, robust compliance mechanisms are required to ensure accountability and traceability. These include continuous risk scoring and standardized disclosure requirements to support ongoing monitoring and governance. In addition, techniques such as AI watermarking should be mandated across all data formats to enable automated systems to distinguish between authentic and synthetic intelligence artifacts.8 Such measures are critical to preserving the integrity of financial threat intelligence ecosystems and ensuring that generative AI outputs can be safely integrated into existing operational workflows.
Implementing the proposed regulatory framework across banking consortia, regulatory sandboxes, and cross-border intelligence networks requires a carefully designed architectural approach. This architecture must balance the need for secure collaboration with regulatory compliance, particularly in environments where sensitive financial data is distributed across jurisdictions. As such, implementation strategies must align both technical infrastructure and governance requirements to support scalable and compliant intelligence sharing.
A decentralized, federated intelligence-sharing model forms the core of this architecture. Within this model, centralized clearinghouses—such as FS-ISAC—function as governance overlays rather than primary data repositories.6,9 This approach enables financial institutions, including Tier-1 banks, to collaboratively train and query generative AI models using localized threat data without exposing raw proprietary information. By keeping sensitive data within institutional boundaries, federated architectures mitigate cross-border data exposure risks while still enabling collective intelligence generation.9
To further enhance the security and resilience of these systems, integration with existing cybersecurity infrastructures should incorporate advanced cryptographic techniques. In particular, the use of post-quantum cryptography can help secure federated learning processes against emerging computational threats.10 By safeguarding model updates and inter-organizational exchanges, these measures reinforce trust in collaborative environments and ensure that generative AI can be deployed safely within complex financial ecosystems.
Evaluating the proposed framework against established governance models requires a systematic assessment based on key criteria, including adaptability, interoperability, and ethical integrity. These criteria determine whether the framework can effectively integrate into existing financial threat intelligence ecosystems while maintaining regulatory compliance and operational reliability. A rigorous evaluation must therefore consider both governance alignment and technical feasibility.
The comparative analysis and gap identification should be grounded in a hybrid baseline that combines AI governance regulations with established threat intelligence protocols. Frameworks such as the EU AI Act, the NIST AI Risk Management Framework, and the MAS FEAT Principles provide the regulatory and ethical benchmarks, while technical standards such as STIX/TAXII and FS-ISAC define the operational context for intelligence sharing.2,3,4,5,6 Within this baseline, the analysis must evaluate how generative AI–specific capabilities—particularly provenance tracking and synthetic data attribution—can be integrated into existing systems.
The evaluation focuses on the following key dimensions:
A critical aspect of this evaluation is determining whether generative AI can be integrated without degrading interoperability or violating cross-border data regulations. This includes assessing how provenance metadata and attribution mechanisms can be embedded within STIX objects and transmitted through FS-ISAC pipelines.5,6 Recent advancements demonstrate that fine-tuning large language models with curated text-STIX pairs can improve the fidelity of cyber threat intelligence extraction, indicating that generative AI can be integrated into standardized formats when supported by rigorous evaluation frameworks.11
The integration of generative AI into financial threat intelligence systems necessitates coordinated and proactive action from policymakers and regulatory bodies. As generative technologies become increasingly embedded in intelligence workflows, regulatory approaches must evolve to address both their operational benefits and associated risks. This requires not only the adaptation of existing governance models but also the development of new mechanisms tailored to the unique characteristics of generative AI.
A key policy priority is the establishment of federated compliance monitoring as a core mechanism for ensuring regulatory adherence across decentralized banking networks.9 Such an approach enables continuous oversight without compromising data sovereignty, aligning with the distributed nature of modern financial ecosystems. In parallel, there is a critical need to develop synthetic intelligence certification standards that can validate the integrity and origin of AI-generated outputs. This reinforces the requirement for regulatory frameworks to encompass the full spectrum of generative AI modalities—including text, code, and telemetry—rather than focusing narrowly on individual system types.1,7
Future directions should focus on advancing governance models that are both adaptive and technically grounded. In particular, the following areas warrant further development:
Adaptive governance models that incorporate these elements will be essential to maintaining trust, ensuring accountability, and preserving resilience in financial threat intelligence ecosystems as adversarial tactics continue to evolve.
The integration of generative artificial intelligence into financial threat intelligence systems presents both transformative opportunities and significant risks. While generative AI enhances the speed and scale of intelligence synthesis, it also introduces challenges related to data provenance, synthetic content reliability, and adversarial manipulation. Existing governance frameworks and technical standards provide a necessary foundation, but they remain insufficient to fully address the complexities introduced by generative AI–driven intelligence workflows.
This study has proposed a structured regulatory framework composed of governance, operational, and collaborative layers to address these gaps. By incorporating mechanisms such as provenance tracking, risk classification, continuous monitoring, and secure information-sharing protocols, the framework enables the responsible integration of generative AI into financial ecosystems. The development of a comprehensive risk taxonomy and corresponding compliance mechanisms further ensures that both technical and ethical risks are systematically managed across diverse generative modalities.
Through comparative analysis, the study demonstrates that alignment with existing regulatory and technical standards—such as the EU AI Act, NIST AI Risk Management Framework, MAS FEAT Principles, and STIX/TAXII protocols—is both feasible and necessary. However, effective integration requires targeted enhancements, particularly in embedding provenance and attribution mechanisms within established intelligence-sharing infrastructures. These adaptations are critical to maintaining interoperability while ensuring regulatory compliance and data integrity.
Ultimately, the findings underscore the need for adaptive, multi-layered governance models that can evolve alongside advancements in generative AI. By prioritizing transparency, accountability, and cross-organizational coordination, the proposed framework contributes to strengthening trust and resilience within global financial threat intelligence ecosystems. As generative AI capabilities continue to expand, sustained collaboration between policymakers, financial institutions, and technical stakeholders will be essential to ensuring secure and responsible deployment.
