Bhavya Bhandari, a financial services cyber risk management executive, shares his perspective on why global financial institutions must redefine cyber risk management to improve metrics, reporting, and oversight
Over the past several years, cyber incidents have disrupted critical operations, interrupted business activity, and drawn regulatory attention in increasingly short timeframes. In many cases, organizations affected by these events had recently completed formal cyber assessments. Controls were documented and validated, yet risk evolved faster than oversight mechanisms were able to respond.
Cyber risk has expanded well beyond the technology function. It now intersects with enterprise operations, regulatory exposure, and organizational resilience. Despite this shift, many organizations continue to manage cyber risk through models centered on periodic assessments, static controls, and retrospective reporting.
This approach is becoming harder to sustain. Threats evolve continuously, business environments change rapidly, and regulators are placing greater emphasis on evidence that controls operate effectively over time, not just at the point of an annual review. The growing use of artificial intelligence adds another layer of complexity. As AI becomes embedded in decision-making, customer interactions, and operational processes, risk conditions may shift more frequently than traditional oversight models were designed to accommodate.
As a result, some organizations are re-examining how cyber risk oversight works in practice. Rather than relying only on more frequent assessments or additional controls, attention is shifting toward operating models that provide more continuous visibility into risk.
Where point-in-time assessments fall short
Point-in-time testing has long been used by cyber and compliance teams to demonstrate alignment with internal policies and external requirements. While this approach provides structure, it often introduces inefficiencies. Assessments tend to be manual, time-consuming, and repetitive, with the same controls tested multiple times to satisfy overlapping regulatory and industry frameworks.
More importantly, point-in-time assessments may not reflect an organization’s ongoing risk posture. Controls can appear effective during testing but weaken as systems change, processes evolve, or third-party relationships expand. In environments where risk conditions shift rapidly, particularly those influenced by AI-enabled capabilities, static validation offers limited insight into whether controls continue to operate as intended.
Moving toward continuous oversight
To address these limitations, some organizations are embedding elements of continuous monitoring into cyber risk governance. Compliance is no longer treated as a standalone activity but is increasingly integrated into broader enterprise risk processes, including risk identification, control operation, issue management, and remediation.
Simplifying how controls are defined and assessed is often a foundational step. By aligning internal controls to regulatory and industry expectations once, organizations can evaluate them more consistently and reuse validation results across multiple reporting and regulatory requirements. This approach reduces duplication, improves data quality, and allows teams to focus on strengthening controls rather than repeatedly preparing for assessments.
From reporting to outcome-focused insights
Metrics play a central role in enabling more continuous oversight. Instead of relying primarily on static compliance reports, organizations are developing metrics tied to control performance and operational outcomes. These measures provide insight not only into whether a control exists, but whether it continues to function effectively as the environment changes.
When designed thoughtfully, these metrics offer earlier visibility into emerging weaknesses, including those introduced by rapidly evolving technologies such as artificial intelligence. Shared across operational teams, senior management, and boards, they support more informed discussions and enable earlier intervention when risk indicators begin to deteriorate.
A shift in cyber risk oversight
Taken together, these developments reflect a broader shift from periodic compliance toward continuous assurance. In this model, policy adherence becomes the by-product of integrated processes rather than isolated testing cycles. Risk visibility improves as conditions change, rather than being restructured after an incident or regulatory inquiry.
As cyber risk continues to evolve alongside artificial intelligence and increasingly interconnected financial ecosystems, institutions will need to move beyond incremental improvements to existing frameworks. The next phase of cyber risk management will require inserting continuous oversight into core operating models, supported by real-time metrics, integrated data, and closer alignment between technology, risk, and business functions.
Organizations that invest in adaptive, outcome-driven approaches are better positioned to anticipate emerging threats, respond more effectively to disruption, and meet rising regulatory expectations. Those that remain reliant on static, assessment-driven models may find themselves increasingly exposed in a risk environment that no longer operates on a fixed timeline.
