From Claude to Daybreak: What’s next for AI policy in finance?

Posted On Date: June 4, 2026

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Introduction

If one were to survey financial sector AI policy developments over the past year in the country, policymakers have been worried about customer risks arising from the use of AI, such as discrimination, privacy infringement, hallucinations and unsuitable AI-based decisions. More recently, however, policymakers have also been compelled to confront another category of AI risks, i.e. the use or abuse of AI as a tool to accomplish malicious tasks.[1]

The advancements in Agentic AI, especially since the debut of Claude’s Mythos Preview, may have lowered the barriers for attackers by making cyber-attacks cheap, scalable, swift and accessible.[2] In such cases, the AI system itself becomes the attack vector, capable of autonomously exposing vulnerabilities in operating systems, browsers and network services. In a deeply interconnected financial system that is heavily dependent on third-party vendors and shared cyber-infrastructure, such vulnerabilities are unlikely to remain confined to individual institutions and may instead cascade across the broader financial system.

In February this year, India hosted the AI Impact Summit, which brought together stakeholders from over a hundred countries, across governments, industry and civil society. The Summit appeared deliberately optimistic about the use of AI across sectors, emphasising the impact that AI has already had or is capable of. It concluded with a Summit Declaration that reiterated the need for AI systems to be trustworthy if they are to deliver meaningful societal and economic benefits.[3]

However, wherever risks were acknowledged, the focus appeared to be on risks arising from the use of AI.[4] To this end, the Declaration appeared to prefer light-touch AI governance in the form of voluntary codes, industry best practices, over detailed and binding regulations, in line with the country’s domestic stance. In this article, we trace the emerging contours of India’s domestic approach to governing AI in finance and identify the priorities that should shape the next phase of policy thinking, in light of alarm bells triggered by Mythos, and OpenAI’s cybersecurity initiative, Daybreak.

Reserve Bank of India (RBI) sets the direction for AI governance in finance

At the sectoral level, the RBI recognises that risks arise across the AI lifecycle, from design development to deployment and downstream uses. It has adopted a lifecycle approach to governing AI, with governance interventions envisioned across each stage. This orientation is visible in the Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE AI) Committee Report[5] released in August 2025.

The regulator’s vision is clear: that AI systems driving the financial sector remain safe and trustworthy. The FREE AI Framework operationalises this vision through two core building blocks. First, it articulates the seven guiding “sutras” or guiding principles that should shape AI development and deployment in finance: Trust, People First, Innovation over Restraint, Fairness and Equity, Accountability, Understandable by Design and Safety, Resilience and Sustainability. Second, it organises its recommendations across six levers of: Infrastructure, Policy, Capacity, Governance, Protection and Assurance, in service of the regulator’s larger goal to balance innovation with risk mitigation.

In doing so, the FREE AI Framework takes a principle-over-prescription approach and gives regulated entities room to contextualise recommendations based on their risk profile and use case. The recommendations lean heavily on self-regulatory and voluntary measures, including transparency reports, self-certifications and third-party audits rather than imposing enforceable compliance obligations.

AI Governance Guidelines converge with the RBI’s approach

Alongside the sectoral efforts, the Ministry of Electronics and Information Technology (MEITY) issued the AI Governance Guidelines (Governance Guidelines) in November 2025.[6] These Governance Guidelines position themselves as the broader, cross-sectoral counterpart to the FREE AI Framework, effectively extending its logic beyond the financial sector. While not finance-specific, the Governance Guidelines and their recommendations have clear implications for financial sector stakeholders as well.

The Guidelines refrain from proposing a standalone AI law and instead lean on existing legal frameworks, such as the Information Technology Act, 2000, the Digital Personal Data Protection Act, 2023, to address AI-related risks. Beyond this, they recommend a comprehensive review of legal frameworks to identify and address gaps emerging from AI systems. Within the Governance Guidelines architecture, the RBI is envisioned to lead the monitoring of AI-related risks in finance.

Recalibrating the RBI stance to recent developments in AI capabilities

RBI’s FREE AI Committee Report and the AI Governance Guidelines, alongside the broader signals from the AI Impact Summit, reflect a growing consensus that responsible design and deployment of AI can deepen financial inclusion and enhance the relevance of financial services at a population scale. However, they continue to focus predominantly on risks arising from the use of AI systems by regulated entities. The policymaker’s primary concern is that AI systems should not produce harmful customer outcomes.

This conceptualisation underemphasises another category of risks emanating from malicious or offensive use of AI itself. In 2025, AI-enabled cyberattacks rose by 89% compared with the previous year, according to CrowdStrike’s Global Threat Report.[7] The interaction between cyber offensive AI and legitimate, use-bound AI applications potentially alters how AI risks are understood and conceptualised, and the scale at which they may materialise. For instance,

AI as the attack vector: An increasingly important concern is the vulnerability of the banking infrastructure to external attackers using AI to manipulate, probe or exploit underlying systems. Mythos exemplifies the emerging paradigm of “AI as an attack vector”, where the AI model itself is capable of autonomously identifying exploit vulnerabilities, thereby reducing the time between vulnerability discovery and its exploitation.[8]

Vulnerability of responsible AI safeguards: Some of the safeguards commonly proposed to address the black box nature of these algorithms, like SHAP or LIME, may themselves become vulnerable to AI-driven cyberattacks.[9]This undermines the assumption that these safeguards alone make AI systems trustworthy.

Third-party risks increase the surface area of attack: In a banking system, where the regulated banks sit alongside non-bank payment providers, fintechs and third-party vendors, the entry point to AI-related risks is not restricted to regulated entities alone. The reliance on third parties and their APIs expands the attack surface for AI-related vulnerabilities. Thus, there is an increasing potential for correlated failures that can affect financial intermediation and payments at a systemic level.[10]

Consequently, it may no longer be sufficient for policymakers to focus exclusively on ensuring that the use of AI by regulated entities does not produce harmful outcomes. Any governance framework would also need to account for risks generated by external actors capable of exploiting banking infrastructure using AI. In this context, the governance measures and safeguards must become more responsive and adaptive to the threat landscape emerging around AI systems.

The Mythos Preview’s cyber capabilities[11] have once again renewed the focus of financial sector regulators on the unprecedented risk of AI technology being misused to exploit vulnerabilities in the banking infrastructure.[12] It is an opportune time to recalibrate priorities reflected in the FREE AI Report and make the recommendations actionable and enforceable for the ecosystem participants.

In this context, we propose certain recommendations to A) the regulated entities, Self- Regulatory Organizations (SROs) and the industry bodies and B) the RBI and the Government:

A. Recommendations to the regulated entities, the SROs and the industry bodies

Prioritise focus on the adoption of cybersecurity risk mitigation measures: The findings from the FREE AI Report indicate a low level of preparedness among regulated entities to tackle AI-driven cybersecurity risks. Of the 127 entities that reported use of AI, only 14 per cent conducted regular audits, only 18 per cent maintained audit logs and 14% conducted real-time performance monitoring. In light of the increasing potential of AI capabilities to be exploited;

a) Fast-track cybersecurity preparedness: The recommendation[13] on the adoption of cybersecurity measures by regulated entities should be fast-tracked and treated as an immediate regulatory priority, rather than remaining within a medium-term horizon. AI-enabled capabilities must also be a critical part of these cybersecurity defences, rather than an option. As the FREE AI Report itself recognises, AI-driven anomaly detection, threat intelligence, real-time intrusion monitoring and adaptive defence systems can strengthen financial system resilience. When threat actors can leverage enhanced AI capabilities to conduct attacks at speed and at scale, the regulated entities must also develop the capabilities to defend at scale.[14] Further, the recommendation relating to the conduct of internal audit by regulated entities should also be moved from the medium-term to the short-term horizon. Such audits are especially necessary to assess legacy systems to identify and surface vulnerabilities in the banking infrastructure.[15]

b) Adapt existing policies of the bank to emerging AI-driven cyberattacks: The recommendation requiring regulated entities to adopt board-approved AI policy is presently a medium-term recommendation. In the absence of a good AI policy benchmark and the suggested outline in the FREE AI Report being fairly minimal, it remains unclear whether these policies would effectively establish clear operation protocols against AI-driven cyberattacks. Regulated entities should identify convergence points between their proposed AI policies, existing cybersecurity and IT governance frameworks, particularly to avoid fragmented compliance structures and overlapping processes. The goal for the regulated entities should be to have consistent protocols to address AI-driven risks.

Address the growing capability gap to adopt cyber resilience standards and emphasise ecosystem support: The findings from the FREE AI Report indicate that cooperative banks and smaller NBFCs report lower levels of AI adoption, often citing infrastructural and resource constraints. These constraints are likely to limit their ability to build resilience against AI-driven cyber risks. Vulnerabilities of the smaller regulated entities and entities outside the RBI purview may still be exploited by the threat actors. Regulated entities may differ considerably in terms of technological maturity, operational capacity and financial resources, which may influence their ability to meet baseline cyber resilience standards against AI-driven threats. While larger regulated entities may have the capacity to invest independently in AI-driven cyber resilience infrastructure, smaller regulated entities may face greater operational and financial constraints in implementing comparable infrastructure.[16] Thus, the recommendation of individual capacity building efforts by the regulated entity alone[17] may be insufficient. The SROs, industry bodies and larger regulated entities would have a concrete role to play in supporting smaller regulated entities’ institutional capacity through shared testing frameworks[18] and access to AI expertise.

Catalyse the development of an AI Compliance Toolkit: The FREE AI Committee recommends[19] that the industry develop an AI compliance toolkit in the medium term and encourages third-party service providers to offer toolkit-based validation services. In light of the increasing integration of AI in the banking infrastructure and growing sophistication of AI-driven risks, the need for such a toolkit has become considerably more urgent. Regulated entities lack shared operational benchmarks to diagnose whether their practices are proportionate and responsive to emerging risks. The proposed toolkit can help translate the seven “sutras” into a common baseline for AI preparedness. Moreover, its utility may extend beyond regulated entities to fintechs and technology service providers. Especially in the absence of concrete supervisory benchmarks, the toolkit can help establish a common baseline for AI resilience and cybersecurity preparedness for the wider ecosystem. This will enable the entities to integrate responsible AI safeguards and defensive practices into their workflows. While the long-term relevance of this toolkit will depend on its ability to remain current and responsive to the emerging risks, the immediate priority should be to establish a first iteration of such shared standards.

Institutionalise continuous and systematic evidence collection on emerging AI risks: The emergence of advanced AI cyber capabilities such as Mythos, demonstrates how AI risks are evolving, thus a one-time evidence-gathering exercise may not suffice. Early insights from FREE AI surveys and consultations provide a useful starting point, however future regulatory responses will need to be supported by more contextual and recent understanding of the AI ecosystem. For instance, findings that around 15% of the surveyed entities use interpretation tools like SHAP or LIME are informative, but incomplete in isolation. Entities typically exist along a spectrum of responsible AI maturity that depends on the nature or scale of their AI integration and business operations. The FREE AI Report already underscores the value of evidence collection, emphasising the need for a sector-wide AI repository to capture adoption trends and risks across the financial ecosystem Thus, building institutional capacity for continuous and systematic feedback from the industry can help ensure that regulatory responses remain grounded in evidence and relevant over time.

B. Recommendations to the RBI and the Government

Operationalise AI incident reporting and real-time intelligence sharing: The recommendation on AI incident reporting and the sectoral risk intelligence framework[20] is likely to get renewed focus with the Ministry of Finance’s call to establish real-time threat intelligence among banks, CERT-IN, and other relevant agencies. In an interconnected financial system, cyber incidents that affect one entity can rapidly cascade across the ecosystem. This makes timely information sharing not only crucial from the perspective of institutional-level incident response but also alerting the wider ecosystem of entities to anticipate, prepare for and contain the threat. At present, it remains to be seen which regulator would be spearheading these efforts and how the responsibilities would be coordinated across regulators and agencies. Further, while greater information sharing is necessary, it would be necessary to have clarity on operational creases, including but not limited to the consequences of reporting, reporting timelines and the reporting infrastructure.

Foster AI innovation in tandem with cybersecurity compliance expectations: The recommendation on leveraging AI to accelerate financial inclusion suggests lowering compliance expectations for inclusion-focused use cases without compromising certain basic safeguards in place.[21] The emergence of offensive AI capabilities strengthens the case for ensuring cybersecurity preparedness continues to form part of these basic safeguards. Reduced safety in such use cases may unintentionally expand the surface area of AI- enabled cyberattacks, identity abuse and fraud. This concern is significant given that these systems will interact with the vulnerable user segment and their personal data. Thus, fostering innovation should not translate into dilution of baseline cyber resilience safeguards. The minimum expectations relating to internal audits, incident reporting, continuous performance monitoring and cybersecurity preparedness should not be seen as a deterrent to innovation, but as an incentive to foster responsible innovation.

Clarify institutional mandates of cross-sectoral bodies for enhanced accountability and coordination in the ecosystem: Through the FREE AI Report and the AI Governance Group Guidelines, a distributed governance model has emerged, which brings together multiple actors operating through different pathways to pursue a common objective of a trustworthy AI ecosystem in finance.Within the banking sector, the RBI is expected to monitor AI-related risks, issue guidance and enforce existing regulations. Industry bodies and self-regulatory organisations are expected to facilitate responsible adoption by sharing best practices between the members, developing AI compliance toolkits and tracking AI integration.At the cross-sectoral level, the AI Governance Group and Economic Group will coordinate policies across ministries, departments and sectoral regulators and issue guidelines for compliance. It will be supported by a Technology and Policy Expert Committee.[22] The AI Safety Institute is expected to develop guidelines, codes, standards, respective evaluation metrics and testing frameworks for risk assessment. Standard-setting bodies such as the Bureau of Indian Standards have a role to play in developing AI risk taxonomies as well. Besides coordination on operationalising AI incident reporting and real-time intelligence sharing, there is a broader need for clarity on how these multiple actors would align in their overall mandates. For instance,

If the AI Governance and Economic Group were to issue guidelines, the weight and enforceability of such guidance remain uncertain, especially when the RBI may simultaneously address these issues within its own domain. Moreover, given that the RBI is not part of the AI Governance and Economic Group, ensuring convergence in their guidance would be key to avoiding inconsistent governance expectations.

While the FREE AI Report requires regulated entities to incorporate risk classification as part of their board-approved AI policies. Their interaction with risk taxonomies and classification frameworks developed by the AI Safety Institute and standard-setting bodies will remain to be seen.

Thus, a sharper articulation of institutional mandates with well-defined boundaries and clear points of interaction is needed, without which there is a risk of AI governance efforts creating inconsistent compliance expectations for regulated entities.

This blog is part of our on going series on AI related harms in finance

Footnotes

[1] https://www.sciencedirect.com/science/article/abs/pii/S0167404822003984

[2] https://www.imf.org/en/blogs/articles/2026/05/07/financial-stability-risks-mount-as-artificial-intelligence-fuels-cyberattacks

[3] https://www.mea.gov.in/bilateral-documents.htm?dtl/40809

[4] https://www.thehindu.com/sci-tech/technology/india-should-be-among-the-top-three-ai-superpowers-globally-pm-modi/article70643283.ece

[5] rbidocs.rbi.org.in/rdocs/PublicationReport/Pdfs/FREEAIR130820250A24FF2D4578453F824C72ED9F5D5851.PDF

[6] https://static.pib.gov.in/WriteReadData/specificdocs/documents/2025/nov/doc2025115685601.pdf

[7] https://www.crowdstrike.com/explore/2026-global-threat-report

[8] https://www.spglobal.com/market-intelligence/en/news-insights/articles/2026/5/anthropic-s-new-ai-model-pushes-banks-to-shore-up-cyber-defenses-100945008

[9] https://arxiv.org/pdf/2306.06123

[10] https://www.imf.org/en/blogs/articles/2026/05/07/financial-stability-risks-mount-as-artificial-intelligence-fuels-cyberattacks