16 Financial institutions generate and retain enormous volumes of data across transactions, customer interactions, fraud detection systems, market feeds, communications, backups, analytics and regulatory records. Much of that information must remain available for years while being protected against cyberattacks, unauthorized changes and infrastructure failures. That combination makes storage solutions for financial services unusually demanding. Banks, insurers, payment providers and other financial organizations need storage infrastructure that can support several requirements at the same time: strong data protection, predictable access, regulatory retention, rapid growth and increasingly data-intensive analytics and AI workloads. The challenge is deciding which storage architecture can meet those requirements without creating another collection of infrastructure silos. This guide examines the capabilities financial services organizations should evaluate when modernizing storage and explains where scalable object storage fits within a broader data strategy. Why financial services storage requirements are different Storage decisions in financial services affect far more than capacity planning. Financial data can include personally identifiable information (PII), transaction histories, account records, trading information, surveillance data, communications and internal business records. Depending on the institution and jurisdiction, different datasets may be subject to specific requirements governing retention, integrity, privacy and accessibility. Meanwhile, cyber resilience has become an infrastructure priority. A successful ransomware attack against backup or archive data can directly affect an institution’s ability to restore critical systems. Financial institutions also continue to create more unstructured data. Documents, email, call recordings, application logs, security telemetry, AI datasets and backup repositories can grow quickly, making traditional scale-up storage architectures increasingly difficult and expensive to expand. Storage infrastructure therefore has to address several priorities simultaneously: Protect sensitive financial and customer data Preserve required records for defined retention periods Support reliable backup and recovery Scale as data volumes increase Maintain data integrity and durability Provide controlled access to authorized applications and users Support analytics, AI and other emerging workloads Keep infrastructure manageable as capacity grows These requirements explain why financial services storage increasingly needs to be considered as data infrastructure rather than simply additional disk capacity. What are storage solutions for financial services? Storage solutions for financial services are data platforms designed to securely store, protect, retain and provide access to financial information while supporting regulatory, cyber-resilience and scalability requirements. Depending on the workload, an institution may use block, file, object, cloud or archival storage. There is rarely one appropriate storage type for every application. High-performance transactional databases, for example, have very different requirements from a seven-year archive or a backup repository. The goal is therefore to match storage architecture to the characteristics of the data. For large volumes of unstructured data, object storage has become particularly relevant because it provides a scalable architecture and rich metadata while supporting the S3 API widely used by modern applications. The core requirements for financial services data storage When evaluating storage solutions, financial institutions should look beyond raw capacity and performance specifications. Several characteristics determine whether a platform can support the operational and governance requirements of financial data. 1. Cyber resilience Financial institutions remain attractive targets for ransomware and other cyberattacks because disruption can have significant operational and financial consequences. Storage must therefore participate directly in the organization’s cyber-resilience strategy. One important capability is data immutability. With S3 Object Lock, objects can be protected using write-once-read-many (WORM) controls so that protected versions cannot be changed or deleted during a defined retention period. This is especially useful for backup repositories. If attackers obtain elevated credentials and attempt to delete backup data, properly configured immutable copies provide another layer of protection. However, immutability should be treated as one component of a broader architecture. Financial services organizations should also evaluate: Encryption for data at rest and in transit Strong identity and access controls Separation of administrative privileges Multi-factor authentication where supported Data integrity mechanisms Infrastructure and metadata protection Auditability Geographic or multi-site resilience Recovery testing The objective is to reduce the number of ways an attacker can compromise both production and recovery data. 2. Regulatory retention and data integrity Financial organizations may need to preserve particular records for specified periods and demonstrate that those records have not been improperly modified. This makes retention management a storage requirement rather than simply an application-level consideration. Object storage can apply retention policies directly to stored objects. Organizations can use these controls to support architectures where regulated records are protected from alteration or premature deletion. The precise requirements depend on the institution, data type and jurisdiction. In the United States, for example, SEC rules can impose recordkeeping requirements on certain regulated organizations. Storage teams should work with legal, compliance and security stakeholders to translate those obligations into technical policies. Important questions include: Which records must be retained? How long must each category be preserved? Can records be deleted before retention expires? Who is authorized to change retention policies? Are changes to retention settings auditable? How quickly must archived information be retrievable? What happens when a legal hold applies? Building these policies into the storage layer can help reduce dependence on manual processes. 3. Scalability for continuous data growth Financial data rarely stops growing. Transaction histories accumulate. Backup sets become larger. Security systems generate logs continuously. Customer communication channels produce recordings and documents. Analytics platforms retain increasingly large datasets. AI introduces another source of growth through training data, document repositories, embeddings, checkpoints and generated content. Traditional storage systems often expand by adding larger arrays or replacing existing infrastructure. At sufficient scale, this can create repeated migration and refresh cycles. Scale-out object storage takes a different approach. Capacity can be expanded by adding infrastructure to a distributed system. Rather than repeatedly migrating datasets onto larger storage arrays, organizations can grow the underlying platform over time. This model is particularly useful for workloads that can reach petabyte scale, including: Backup repositories Long-term archives Security logs Application-generated data Document repositories Data lakes Analytics datasets AI training and retrieval datasets The architecture should also maintain predictable operations as object counts and capacity increase. 4. High data durability and availability Financial institutions cannot treat stored information as disposable. Data durability describes the likelihood that stored information will remain intact over time. Distributed storage platforms can protect data using techniques such as replication and erasure coding, allowing information to remain recoverable despite hardware failures. Availability addresses a related question: Can applications still access the data when parts of the infrastructure fail? The distinction matters. A system may preserve data without necessarily keeping it immediately accessible during a failure. Financial institutions should evaluate both durability and availability against the requirements of each workload. For critical environments, storage architecture may also span multiple sites or failure domains to reduce exposure to localized infrastructure problems. 5. Backup and recovery at financial-services scale Backups are only useful when they can be recovered. As financial datasets grow into hundreds of terabytes or petabytes, recovery performance becomes increasingly important. An architecture that can ingest backups quickly but cannot restore them within operational recovery objectives may leave the organization exposed during an incident. Modern backup architectures increasingly use S3-compatible object storage as a target. This allows organizations to combine large-scale backup capacity with capabilities such as S3 Object Lock for immutability. When evaluating a backup storage platform, financial institutions should test both sides of the workflow: Backup performance: Can the system continuously ingest data at the required rate? Restore performance: Can it deliver sufficient throughput when large amounts of information must be recovered? Concurrency: What happens when many backup or restore jobs run simultaneously? Immutability: Can protected backup copies resist unauthorized deletion? Scale: Will performance remain appropriate as the repository grows? Recovery testing should remain part of the operational process. Storage protection controls cannot replace validation that applications and data can actually be restored. Object storage for financial services Object storage is well suited to financial datasets that are large, predominantly unstructured and retained for extended periods. Unlike traditional file storage, object storage organizes information as objects containing the data itself, a unique identifier and associated metadata. The metadata model can be particularly useful for large data repositories because applications can associate additional information with stored objects. Meanwhile, the S3 API has become a common interface for applications that interact with object storage. For financial organizations, S3-compatible storage can provide a common foundation for multiple workloads rather than requiring a separate storage system for every new application. Potential use cases include backup, archive, analytics, application data and AI datasets. Financial services backup storage Backup is one of the clearest applications for object storage in financial services. Large institutions may protect data from thousands of systems across multiple locations. As those repositories expand, storage must provide capacity without making the backup environment increasingly difficult to operate. An S3-compatible backup target can provide scalable capacity while integrating with modern data protection platforms. When immutability is enabled, protected backup objects can also be retained for a defined period in a form designed to prevent modification or deletion. This combination helps financial institutions address two related problems: managing growing backup volumes and protecting recovery copies from ransomware. Long-term financial data archiving Archive storage has a different objective from backup. A backup exists primarily to recover systems or data after a loss or disruption. An archive preserves information for long-term reference, business, governance or regulatory purposes. Financial institutions can maintain enormous archives containing communications, records, documents and historical datasets. Keeping all of that information on high-cost primary storage is inefficient. At the same time, moving data to storage that is difficult to access can complicate investigations, analytics and compliance requests. Object storage provides another option: maintain large archives on scalable infrastructure while keeping data accessible through standard interfaces. Metadata can also help applications organize and identify objects across very large repositories. Storage for financial services AI and analytics AI is changing the storage profile of financial services organizations. Fraud detection, risk analysis, customer support, document processing, quantitative research and internal knowledge systems can all rely on large datasets. Many of these workloads involve unstructured information rather than traditional relational databases. For example, an enterprise retrieval-augmented generation system might process: Research reports Policies and procedures Customer correspondence Contracts Market research Historical documents Application logs Storage architecture must preserve the security classification of that information throughout the AI workflow. A customer record remains sensitive when it becomes part of a retrieval dataset. The same access, retention and protection principles that apply to the original information should therefore extend to AI copies and derived datasets. Scalable object storage can provide a foundation for these large data repositories while allowing applications to interact with information through S3-compatible interfaces. Data sovereignty and control Financial institutions operate under different legal and regulatory regimes, making data location an important architectural consideration. Some organizations need to maintain particular datasets within specific jurisdictions or infrastructure environments. Others may want greater control over where critical financial information resides. Private and hybrid cloud architectures can help institutions maintain this control while still providing cloud-style storage interfaces. S3 compatibility is useful in this context because applications can interact with object storage using a familiar API whether the underlying data resides in a private data center, hybrid environment or supported cloud architecture. This gives infrastructure teams greater flexibility when determining where particular datasets should live. Avoiding storage silos One of the less visible costs of financial data growth is infrastructure fragmentation. A new backup platform gets its own storage. The analytics team deploys another environment. Archives remain on legacy infrastructure. AI teams then create another large repository. Eventually, IT teams are operating several storage islands, each with separate capacity planning, lifecycle management and refresh schedules. A scalable object storage platform can consolidate appropriate unstructured workloads onto a shared architecture. Consolidation does not mean every application should use the same storage tier. Transactional databases and latency-sensitive applications may continue to require specialized infrastructure. Instead, the goal is to identify workloads with similar scale, durability, retention and access requirements and provide them with a common data foundation where appropriate. How to evaluate financial services storage solutions A useful evaluation should begin with the organization’s data rather than a product comparison. Start by identifying the major workloads and determining their capacity, performance, security, retention and recovery requirements. Then evaluate potential platforms against questions such as: Can the architecture scale without disruptive migrations? Does it support S3-compatible applications? How is data protected against hardware failures? Can data be made immutable? How are encryption and access controls implemented? Can retention policies be enforced at the storage layer? How does the system behave during component or site failures? What backup and restore throughput can it sustain? Can infrastructure span multiple locations where required? How easily can capacity and performance expand? Does it integrate with existing backup, archive and data-management applications? How is the platform monitored and operated at petabyte scale? Testing should reflect expected production conditions. Small proofs of concept can hide operational problems that appear only when object counts, concurrency or capacity increase substantially. Where Scality fits into financial services storage Scality provides S3-compatible object storage for large-scale enterprise environments, including financial institutions. Scality reports that its technology is used by seven of the world’s 15 largest banks. Scality’s architecture is designed for data-intensive workloads where scalability, cyber resilience and long-term durability are important. Its current platform includes capabilities for S3 Object Lock immutability, encryption, identity and access management, erasure coding, multi-site protection and large-scale S3 workloads. For financial services organizations, those capabilities can support several storage requirements within the same broader architecture, including cyber-resilient backup, long-term retention, large unstructured datasets and emerging AI workloads. The relevant question is not whether every financial application belongs on object storage. Many do not. The more useful question is where rapidly growing unstructured data can be consolidated onto infrastructure that provides the required protection, retention, accessibility and scale. For financial institutions managing petabytes of backup, archive, analytics and AI data, object storage can provide a practical foundation for that part of the data estate. Building a financial services storage strategy for the long term Financial services storage requirements will continue to evolve as data volumes increase, cyber threats change and new applications place additional demands on infrastructure. Regulatory data requirements are evolving as well. In 2026, U.S. financial regulators finalized joint data standards intended to improve interoperability and machine readability of regulatory data, with the joint rule taking effect October 1, 2026. Storage strategy therefore benefits from focusing on architectural characteristics that remain useful as individual workloads change: scalable capacity, strong data protection, policy-driven retention, standard interfaces and flexible deployment. Object storage addresses many of these requirements for large unstructured datasets. Combined with appropriate application, security and governance controls, it can give financial institutions a more consistent foundation for backup, archive, analytics and AI data while reducing dependence on isolated storage silos. For infrastructure teams evaluating storage solutions for financial services, that flexibility can make it easier to accommodate continued data growth without repeatedly redesigning the storage environment.
