Manual, time-intensive reviews strained the security team

Every product and system change required a full security review. The process depended on individual interpretation and manual effort, making it hard for the security team to maintain depth and consistency across reviews while supporting the bank’s overall pace.

Checklist-driven reviews limited depth and context

Reviews focused on static policy requirements rather than how risks applied within each design. Without consistent context, it was difficult for security architects to assess how threats connected across components or where attention was most needed.

Developers spent time on low value, repetitive prep work

Before clinics, developers had to work through hundreds of generic requirements, many unrelated to their feature. The process felt like busy work and led to surface-level conversations instead of meaningful security dialogue.

Automated and consistent reviews increased capacity and depth

With Clover, automated design-stage reviews now apply the same logic across every design, removing variability and manual workload. The security team can manage more reviews at once and dedicate their time to deeper analysis and higher-value security work.

Threat- and requirement-led reviews provided richer context

By combining hundreds of policy controls with design-level threat modeling, Clover surfaces how risks manifest across systems and helps the security team focus on what matters most for each design.

Contextual, focused reviews enabled deeper developer analysis

Clover highlights only the requirements and threats relevant to each feature, giving developers a clear starting point for in-depth, in-context preparation and more valuable design-clinic sessions.

Scaling security reviews in a quarter of the time

Before Clover, every product and system change required a full manual review by the security team. Each review depended on how individual security architects interpreted hundreds of policy statements across multiple internal standards. With multiple new designs coming through each week, the workload multiplied quickly, and the team’s time was spent gathering, cross-checking, and documenting evidence rather than doing deep analysis. The process worked, but it was heavy. Each review demanded hours of effort, and ensuring that every architect applied the same level of scrutiny was nearly impossible. Maintaining consistent quality across such a high volume of work required significant coordination and created a constant strain on capacity.

With Clover, Virgin Money automated this stage of the process. Reviews now run automatically when new designs are created in Confluence, analyzing each design against all relevant policy frameworks. The platform filters hundreds of requirements down to the small percentage that actually applies to the design, surfacing both relevant controls and contextual threats. This has made reviews four times faster, allowing the team to scale security reviews in a quarter of the time while maintaining the same level of depth and scrutiny. Security architects can now focus on higher-value analysis, validating findings, exploring edge cases, and refining standards, rather than repetitive manual checks. The result is a consistent, automated review process that delivers the same high standard every time, regardless of workload.

Understanding the why behind every control with threat modeling

Virgin Money had already established a strong security framework, with hundreds of technical and policy requirements embedded across its design standards. But meeting requirements alone wasn’t enough. The bank wanted to understand why certain controls mattered in each context, and how individual design decisions shaped real risk. To reach that level of maturity, the security team was directed to become more threat-driven and to incorporate threat modeling into every review. That shift required a way to translate static requirements into dynamic, design-specific insights.

With Clover, reviews now combine policy coverage with contextual threat modeling. Instead of simply confirming whether a control exists, the team can see how risks emerge within the design itself - between components, data flows, and integration points. The platform translates requirements into specific threat statements, giving designers and security architects a shared understanding of intent. This common language also extends to operational teams such as the SOC and threat-hunting functions, who can now connect design-time threats to real-world monitoring scenarios and detection logic.

From checklists to buy-in: engaging developers in real security conversations

Before Clover, developers preparing designs for review faced hundreds of security requirements, many of which had little to do with their specific feature. Completing those checklists was repetitive and time-consuming, and the feedback loop that followed often centered on confirming or rejecting each control. The process gave limited space for exploration or learning. As a result, design-clinic sessions sometimes felt procedural rather than insightful, with teams focused on ticking boxes instead of discussing real security outcomes.

With Clover, developers now start their work with a focused view of what matters most. Each design review highlights only the subset of controls and threats relevant to that feature, giving developers a clear base for investigation and preparation. By the time they reach the clinic, conversations are deeper, more specific, and focused on real security decisions. Security architects and developers now spend their time addressing meaningful risks rather than navigating generic lists, creating a stronger partnership and a shared understanding of how security fits into every design decision.

Extending security from design to delivery

Virgin Money’s next goal is to take the same depth of insight now achieved during design reviews and extend it through the entire lifecycle of every product. By building on Clover’s ability to analyze threats and context at the design stage, the bank aims to apply the same intelligence throughout development, testing, and delivery. This includes validating before go-live, confirming that designs are implemented as intended in code, identifying drift between design and build, and ensuring previously identified risks are addressed, while providing continuous visibility into the design risk posture of its products.