Modernize the banking data core—without increasing risk
What banks need to govern data, strengthen cyber resilience, and innovate without risk
70%
of costs in certain banking activities could be eliminated through AI, with a net cost-base reduction of 15–20% after reinvestment.1
How financial institutions are judged:
Banks are judged by their ability to demonstrate:
The big question?
Not if modernization is required, but how to modernize while strengthening control, recovery assurance, and day-to-day operability.
When two worlds collide: control vs. speed
The constraint: regulated control
The imperative:
move at digital speed
Rules require that certain data and workloads remain under direct institutional control. Fully public cloud is not a good fit.
Zero tolerance for downtime and latency is now table stakes—for payments, trading, channels, partner ecosystems, and always-on customer experience.
vs.
Hybrid infrastructure reconciles competing demands
Maintain sovereign control for regulated and Tier-0 workloads
Use cloud selectively for elasticity, experimentation, and faster delivery cycles
Apply consistent security, observability, and lifecycle controls across environments
Hybrid is the operating model that balances governance, performance, and innovation—at banking scale.
New currency of trust: cyber resilience
In today’s threat environment—ransomware, destructive attacks, and credential misuse—supervisors increasingly expect evidence, not intent. Institutions must demonstrate recovery that is reliable, repeatable, tested, and auditable, with clear ownership across technology and risk.
Cyber resilience starts at the data layer—where recovery points are created, protected, and proven.
The Foundational Requirements For Financial Institutions:
AI makes storage modernization non-negotiable
What changes as AI moves into production
More workloads become latency-sensitive and data-intensive, relying on governed access to high-value datasets. Data gravity increases—and moving data to compute is not always viable or compliant.
What the data layer must guarantee for AI
Sustained low-latency performance and throughput, predictable scaling, governed data movement, and cyber recovery that restores clean data fast—so AI pipelines and critical services remain trustworthy.
60% of financial services leaders cite data privacy and sovereignty as the biggest barrier to AI adoption, underscoring that AI innovation can only succeed when institutions maintain full control over how and where their data is stored, governed, and processed.2
In an era where AI systems increasingly depend on sensitive, high‑value data, sovereign control is no longer optional—it is becoming a core determinant of operational trust, regulatory compliance, and competitive differentiation.
Rethinking infrastructure as a source of assurance
Resilience
Data governance
Operational continuity
70%
of costs in certain banking activities could be eliminated through AI, with a net cost-base reduction of 15–20% after reinvestment.1
Data governance
Operational continuity
Immutable recovery points to prevent tampering
Rapid identification of clean recovery points
Recovery at enterprise scale for critical services (not just single apps)
Isolated vaulting with separate access controls
In banking, the speed and certainty of recovery define operational credibility—and protect customer trust.
Availability, recoverability, performance headroom, and longevity are no longer technical metrics. For bank leadership,
they are the operational guarantees
that protect revenue, reputation, and regulatory confidence.
Modernize the data layer with a clear mandate: simplify operations, prove recoverability, and create performance headroom. That is how banks scale AI with confidence—without weakening governance.
As AI depends more on sensitive,
high-value data, sovereign control becomes a determinant of operational trust and compliance—not an architectural preference.
60%
Trusted by many of the world’s largest financial institutions to run business-critical data infrastructure
—Hitachi Vantara
1 McKinsey. Global Banking Annual Review 2025: Why precision, not heft, defines the future of banking.
Custom content for Hitachi from Studio by Informa TechTarget
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As AI moves from experimentation to production, banks are under pressure to modernize the data layer that makes outcomes possible—while improving resilience, auditability, and day-to-day operability.
Storage modernization is now an assurance agenda: predictable performance for Tier-0 workloads, stronger cyber recovery, and governed data access—so AI can scale without creating new control gaps.
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Routine recovery testing with evidence for audit and board reporting
Buying Center Checklist (Director-level)
Resilience proof: documented RTO/RPO targets and evidence from routine recovery exercises
Clean recovery: ability to identify and restore known-good data quickly after an event
Cyber posture at the data layer: immutable recovery points and separation of duties/access
Audit readiness: clear reporting for controls, retention, access, and recovery evidence
Predictable performance: low-latency and throughput under peak demand and mixed workloads
Non-disruptive change: upgrades and scaling without introducing instability or extended maintenance windows
Sovereignty & privacy: control over where sensitive datasets reside and how they move
Longevity & economics: consolidation and lifecycle design that avoids forced migrations across refresh cycles
Implication for the buying center: fund modernization that strengthens resilience and governance
Privacy, sovereignty, and model risk management are now core constraints. AI at scale requires control over where sensitive datasets live, who can access them, how they move, and how quickly they can be protected and recovered.
Infrastructure chosen only for today’s workloads becomes tomorrow’s constraint. Leading institutions design for multiple capital cycles—avoiding forced refreshes, risky migrations, and cost models that do not scale.
Implication: the winners will be those that modernize the data layer with controls that satisfy regulators and performance that satisfies customers.
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