by: Priyesh Patel
Since 2006 I have been fortunate to work with leading global and regional banks, financial services, and insurance firms (BFSIs) during IT consulting engagements. I remember working directly in the bank’s data center, mounting the keyboard and monitor to rack-mounted servers to install and configure IT solutions in the initial years. Due to security protocols, not even Secure Shell/Remote Desktop Protocol were allowed to access those mission-critical servers from internal staff networks. Gradually, I witnessed a shift to Virtual Desktop Infrastructure to implement and run IT solutions.
In recent years, banking digital transformation has become a mandate, and BFSIs embrace the identity-centric security paradigm. Depending on technology maturity, almost all BFSIs now have a multi-cloud strategy at play.
A multi-cloud strategy offers various benefits, including easing development processes and ensuring high-quality, agile, secure, and scalable software solutions. As a result, many BFSIs are adopting market-leading public clouds (AWS, Azure, and GCP) and fostering private cloud offerings (e.g., based on projects CNCF, serverless, open source, and more).
For each service or application, BFSIs choose to go for public, private, hybrid, or multi-cloud based upon their enterprise architecture principles. In my view, the security needs of the specific service still should play a significant role.
The technical fact is that possibilities with the cloud are endless: policies, blueprints for consistent deployments, elasticity, scale, security, availability, auto-failover groups with SQL, multi-region writes with NoSQL, the choice of APIs for the different needs, MongoDB API, Gremlin API, Cassandra API, Cosmos DB API, guaranteed throughput and I/O, storage redundancy, access tiers, performance tiers, network flexibility VPC, site-to-site, point-to-Site VPN, encryption at all levels in transit or at rest, container orchestrators, and many more from SaaS to PaaS to IaaS.
Taking an example of one of the leading BFSIs, that uses a multi-cloud for AI and machine learning (ML) for the following financial use cases:
The typical initiatives are:
It’s evident that BFSIs use AI and ML for financial data. General models don’t work, and therefore the BFSIs need a skilled ML workforce.
Additionally, AI / ML is an evolving space; up-skilling engineering practices is an ongoing job. BFSIs are supportive of approaches to automate the evaluation of these models and make them more robust.
While BFSIs are busy exploring and using these specialized areas with multi-clouds, they see the 3Vs challenge (Volume, Variety, and Velocity of data). Every cloud provider has monitoring, analytics, and orchestration services but achieving end-to-end observability is not easy with a DIY approach.
BFSI services that don’t have sensitive data should be scoped for a pattern that provides three-dimensional observability:
Each public cloud provider has a set of monitoring and analytics capabilities. Striking a balance between cloud-specific monitoring and developing domain-agnostic AIOps is not simple. It’s a journey.
Implementing AIOps for each step provides deeper observability:
An automation platform plays a significant role for BFSIs. Typically, BFSIs have many fragmented automations for database, file transfers, system tasks, batch processing, dynamic workloads, and more. To comply with business continuity, BFSIs have to prove their fitness via disaster recovery drills at a regular frequency, requiring another set of orchestration.
On top of these, multi-cloud has its own set of orchestration capabilities. An overarching automation platform orchestrates across all these islands of isolated automation and provides observability per line of business, forecasting, critical path, and more.
BFSIs are highly detail-oriented and careful with making changes into production environments, and many leverage topology (network, app, service topologies) for change impact analysis and approvals. This scenario is an example of observability of one dimension (machine data) helping another (process).
The new additions to production are evaluated via production transition design gates. Predictive analytics and capacity analytics both play a role here. Incident management analytics (canceled, closed without action, etc.) helps in governing monitoring rules. The massive chunk of repetitive requests serves as an input for improving automation maturity in new areas. These are more examples of observability dimensions complementing each other.
Traditionally, organizations have adopted various point monitoring tools to gain insights into the multiple data sources mentioned above. This approach no longer works when faced with the complexity of multi-cloud enterprise environments and can become extremely costly to maintain. To gain unified visibility and manage the chaos, adopting an AIOps solution, like Broadcom’s DX Operational Intelligence, is the first step to success.
Visit Broadcom Enterprise Software Academy’s AIOPs resource center to learn more about the value of AIOps.