October 1, 2024
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The financial services industry operates in one of the most heavily regulated and risk-sensitive environments, where security is a top priority. As the frequency and complexity of cybersecurity threats continue to rise, traditional approaches to vulnerability management and compliance often struggle to keep pace. The financial industry has already been deploying Generative AI solutions to improve software security in the banking sector. And what’s fascinating is that GenAI is setting new benchmarks for operational efficiency and return on investment (ROI). In our latest article, we explore how a leading global bank leveraged GenAI to improve software security in banking.
The Problem: An Overwhelming Volume of Security Alerts
This case study is based on insights from Deloitte's State of Generative AI in the Enterprise 2024, which explores how generative AI is helping industries, including financial services. Modern banking systems are complex, with countless interconnected components, services, and applications. Each component needs to be secure against potential vulnerabilities, misconfigurations, and external threats, which creates a major challenge for security teams.
At the global bank in this case study, the security team was overwhelmed by:
- Millions of Daily Alerts: Every day, the bank receives millions of security alerts related to vulnerabilities, endpoint misconfigurations, and compliance risks.
- False Positives: A significant portion of alerts flagged issues that weren’t actual threats. These false positives consumed valuable time and resources.
- Inefficient Processes: The sheer volume of alerts made it nearly impossible to identify and prioritise critical risks effectively.
- Developer Frustration: Developers spent up to 80% of their time remediating security vulnerabilities, which detracted from their primary role of delivering new features and updates. This created tension between security teams and developers.
The Solution: Generative AI
To address these challenges, the bank adopted a GenAI-based solution that changed its approach to secure software development. This innovative system was built on a mature AI foundation and focused on three critical areas: automated controls, threat prioritisation and security protocol automation.
1. Automated Security Controls Across the SDLC
GenAI was embedded into the software development lifecycle (SDLC) to automate the implementation of security controls. These controls were derived from complex regulations, standards, and internal policies and codified into actionable tasks, such as:
- Preventative Controls: Proactively identifying vulnerabilities before code deployment.
- Detective Controls: Continuously monitoring for security breaches during production.
- Responsive Controls: Rapidly mitigating issues when a breach occurs.
- Corrective Controls: Ensuring similar vulnerabilities do not resurface in the future.
2. Prioritising Threats with Advanced Triaging
Given the large volume of security alerts, the bank needed an efficient way to identify the most critical vulnerabilities. GenAI provided advanced triaging capabilities that:
- Filtered Alerts: Millions of daily alerts were reduced to actionable items, and security teams were informed of only the most significant threats.
- Assessed Risk Levels: Each vulnerability was evaluated based on breachability (the size of the risk) and exploitability (the likelihood of it being exploited by a malicious actor).
- Distributed Tasks: Critical threats were routed to specialised teams, such as distributed denial-of-service (DDoS) or malware teams for faster resolution.
3. Automating Cybersecurity Protocols
In addition to improving threat prioritisation, GenAI identified opportunities for automation in routine security processes such as:
- User Account Monitoring: This feature automatically disables accounts that have not been used for more than 30 days, reducing the attack surface.
- Encryption Key Rotation: Ensuring encryption keys were updated at regular intervals, meeting compliance requirements.
- Real-Time Risk Assessment: Detecting potential vulnerabilities in real-time and applying pre-defined solutions to mitigate risks.
The Results: Improved Security, Efficiency and ROI
Generative AI delivered measurable outcomes across the bank’s operations including:
Reduction in Alert Volume and Developer Workload
By deploying advanced triaging and threat prioritisation, the number of actionable alerts was reduced dramatically. Developers went from managing millions of daily alerts to addressing fewer than 10 critical vulnerabilities a day. This freed up significant time and resources, so developers could focus on feature development rather than endless remediation tasks.
Enhanced Operational Efficiency
The automation of repetitive tasks, such as disabling inactive user accounts and rotating encryption keys, further streamlined operations. Security teams could allocate more time to strategic activities, while developers worked on delivering innovative features and updates.
ROI Calculation and Risk Mitigation
The solution’s impact was measured using a risk economic model. This model considered the cost of remediating risks against the potential cost of security breaches. The key domains positively impacted included:
- Data protection and encryption.
- Addressing vulnerabilities during data transit and usage.
- Network segmentation, logging and monitoring.
How to Implement Generative AI Solutions in Banking
AI Security in finance requires a robust and scalable infrastructure capable of supporting high-performance workloads. The AI Supercloud offers scalable AI solutions to meet the unique demands of banking operations. Here’s how you can deploy Generative AI solutions effectively with the AI Supercloud:
High-Performance GPUs for Accelerated AI Workloads
The AI Supercloud leverages cutting-edge GPUs like NVIDIA HGX H100, NVIDIA HGX H200, and NVIDIA Blackwell GB200 NVL72/36 to handle complex AI workloads. These GPUs deliver unmatched computational power, enabling banks to accelerate fraud detection, enhance risk modelling and run large-scale natural language processing tasks efficiently.
High-Speed Networking for Seamless Data Transfer
With NVIDIA Quantum-2 InfiniBand, the AI Supercloud provides ultra-fast interconnects between GPUs. This ensures reduced latency and faster data exchange, ideal for real-time threat detection, transaction monitoring and processing large datasets in financial analytics with minimal bottlenecks.
Optimised Storage Systems for Data Needs
The AI Supercloud’s NVIDIA-certified WEKA storage, integrated with GPUDirect technology, guarantees ultra-fast access to critical datasets. This ensures banking AI models can seamlessly ingest, process, and retrieve large amounts of financial data, essential for improving operational efficiency in AI-powered trading platforms and decision-making tools.
Start Your Generative AI Journey with Us
Ready to implement Generative AI solutions in your services? Schedule a call with our specialists to discover the best solution for your project’s budget, timeline, and technologies.
FAQs
How is Generative AI being used in banking?
Generative AI helps banks automate processes like threat detection, fraud prevention, and customer service while enhancing operational efficiency and ROI through advanced data analysis and real-time decision-making.
What are the key applications of AI for security in banking?
The key applications of Generative AI for security include cybersecurity, detecting fraud, personalised customer experiences, and accelerating software development through automated threat management.
What makes the AI Supercloud ideal for banking operations?
The AI Supercloud offers high-performance GPUs, advanced networking and optimised storage designed to handle complex AI workloads critical for high-stakes financial operations.
Can the AI Supercloud support real-time financial applications?
Yes, features like NVIDIA Quantum-2 InfiniBand and GPUDirect technology ensure low latency and high throughput for seamless operation of real-time AI applications in banking.
Is the AI Supercloud scalable for growing financial institutions?
Absolutely. With our Supercloud, you can scale up to thousands of GPU clusters of AI, with delivery in as little as 8 weeks.
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