Bhavya Bhandari
Bhavya Bhandari

Artificial intelligence (AI) is changing how financial institutions identify, connect, and act on cybersecurity risk. For Bhavya Bhandari, a technology and cybersecurity risk management leader with more than 15 years advising financial institutions, the key shift is not simply faster detection. It is the ability to connect technical findings, control weaknesses, AI governance obligations, regulatory expectations, and business impact into an integrated model for cybersecurity risk oversight.

Bhandari is a trusted advisor to executive leadership and boards, specializing in cybersecurity strategy, governance, risk, and compliance (GRC), and exam preparedness across regulatory frameworks. His work has focused on helping financial institutions build scalable risk programs and translate cybersecurity, regulatory, and AI-related risk into measurable business and resiliency outcomes.

Bhandari's contribution is significant because AI is exposing the limits of traditional cybersecurity risk assessment. Before AI-enabled analysis became more common, financial institutions often prioritized findings through static severity ratings, isolated issue inventories, and periodic control assessments. This approach could identify individual weaknesses but often missed how lower-severity issues could combine into broader attack paths with greater business impact. The old model treated findings as individual issues to be ranked by severity; the new model treats findings as connected signals that reveal compounded exposure, business impact and remediation priority.

Bhandari's work has focused on risk modeling approaches that evaluate exposure across aggregated risk pathways, linking weaknesses across applications, infrastructure, identity and access management, data flows, third party dependencies, AI use cases, and control environments. These approaches help financial institutions understand not only whether a control gap exists, but how that gap connects to business services, AI adoption decisions, regulatory obligations, and remediation priorities.

This before-and-after shift is especially important in AI-enabled environments. Before, a financial institution might review vulnerability findings, access issues, application weaknesses, AI governance gaps, third party risks, and control deficiencies as separate workstreams. After applying a more integrated risk modeling approach, those data points can be assessed as connected exposure pathways, helping institutions identify which combinations of findings create the greatest risk and which remediation actions should be prioritized.

A defining feature of Bhandari's approach is the connection between AI-driven risk discovery and control mapping. AI can accelerate the identification of potential attack paths, but those insights only become useful when mapped to control obligations, governance processes, issue management workflows, and executive reporting structures. Bhandari has helped financial institutions align cybersecurity risk analysis with frameworks such as the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF), Cyber Risk Institute (CRI) Profile, International Organization for Standardization (ISO) 27001, FFIEC Information Security guidance, New York Department of Financial Services (NYDFS) requirements, and other global regulatory expectations.

His broader framework experience also informs his AI risk visibility work. Bhandari's contribution to the CRI Profile Version 2.0 uplift included strengthening framework mappings, diagnostic structures, and tagging approaches used to align security topics, regulatory expectations, and control requirements. That same emphasis on traceability is central to AI-era cybersecurity risk management. Financial institutions need to understand not only that AI has surfaced a risk, but how that risk connects to a diagnostic statement, control objective, policy requirement, business process, and reporting metric.

This approach now extends into AI governance through the CRI Financial Services AI Risk Management Framework (FS AI RMF). CRI describes the FS AI RMF as an industry-led, sector-specific AI risk management framework developed through public-private collaboration with more than 100 financial institutions and input from U.S. and international agencies, including NIST. Structurally aligned with the NIST AI RMF and expanded with 230 Control Objectives, the FS AI RMF is designed to help financial organizations manage and govern AI risks while enabling responsible innovation.

The FS AI RMF bridges high-level AI principles and practical implementation. CRI states that the framework translates AI principles into actionable, sector-relevant control objectives and implementation guidance; supports consistent evaluation, benchmarking, and maturity of AI governance; and helps organizations assess current AI adoption, define target adoption stages, and prioritize controls to close gaps responsibly. CRI also describes its broader work as connecting threats to mitigating controls and associated compliance, giving institutions a comprehensive view of risk from technical environments to board-level oversight. [myabt.com]

Bhandari has helped expand this thinking by supporting adoption of the CRI FS AI RMF as a practical model for assessing AI adoption and readiness. His work has included helping financial institutions evaluate AI governance and control maturity, map AI control objectives to adoption stages, and connect AI risks to cybersecurity-relevant topics already addressed through the CRI Profile. This alignment matters because AI systems can introduce or amplify risks related to data integrity, access control, third party dependencies, model monitoring, explainability, incident response, and resilience.

Bhandari has helped operationalize these concepts through cybersecurity and AI risk advisory work across global, national, and regional financial institutions. His work has supported clients in integrating cybersecurity and AI risk visibility into broader risk management frameworks by aligning technical findings, AI control objectives, diagnostic statements, internal processes, standards, controls, metrics, and reporting structures. This implementation experience helped translate assessment results into plain language, regulator and board-ready insights; benchmark maturity; prioritize risk; and convert findings into actionable roadmaps for governance, policy, control, and metric improvements.

The executive and board value of this work is central. AI may identify exposure faster, but leadership still needs to decide which risks require funding, which remediation actions should be accelerated, which control owners are accountable, and how risk reduction should be measured. Bhandari's approach helps translate technical exposure and AI governance gaps into decision-ready reporting by connecting risk pathways to business impact, control maturity, remediation timelines, adoption stage, and performance metrics.

The significance of Bhandari's contribution lies in practical application. His work helps financial institutions shift from static risk views to integrated models that connect AI-driven discovery, risk pathway analysis, control mapping, AI governance readiness, remediation prioritization, executive reporting, and board-level oversight. By linking technical exposure and AI adoption risk to governance and resiliency outcomes, Bhandari has contributed to a more actionable model for cybersecurity and AI risk oversight.